From aeb51b3f509fd20b1824c3d4471a8b4af91db8b1 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Sat, 10 Oct 2026 17:06:07 +0200
Subject: [PATCH 01/28] Keep the beta channel config on beta after merging
 0.8.0

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0126B6HCdwnvuVrRkMeMEXfo
---
 visionstate/config.yaml | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/visionstate/config.yaml b/visionstate/config.yaml
index 3c754f1..ccf9e20 100644
--- a/visionstate/config.yaml
+++ b/visionstate/config.yaml
@@ -1,5 +1,5 @@
-name: VisionState
-version: "0.8.0"
+name: VisionState (beta)
+version: "0.7.1b6"
 slug: visionstate
 description: Teach a local AI to recognise states (open, closed, …), find objects (people, cars, pets) or read numbers in your camera images
 url: https://github.com/oleost/VisionState
@@ -13,7 +13,7 @@ arch:
 ingress: true
 ingress_port: 8099
 panel_icon: mdi:eye-check-outline
-panel_title: VisionState
+panel_title: VisionState β
 homeassistant_api: true
 hassio_api: true
 services:
@@ -34,3 +34,5 @@ schema:
   mqtt_port: port?
   mqtt_username: str?
   mqtt_password: password?
+environment:
+  VISIONSTATE_MEDIA: /media/visionstate_beta

From e18d905fb8ace791d476f83e69da82f9ae16c081 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Sat, 10 Oct 2026 17:28:41 +0200
Subject: [PATCH 02/28] Docs: one home per fact, a test that keeps them true to
 the code

- CLAUDE.md says where each kind of fact is written down; release steps move to docs/RELEASING.md,
  how to run it locally moves from README.md to CLAUDE.md, the code map in CLAUDE.md to ARCHITECTURE.md.
- SCOPE.md: no version stamp; kinds, wizard, schema and reading fields corrected; repository layout,
  tech stack, identity and channels moved out (ARCHITECTURE.md, RELEASING.md); roadmap in order.
- Wheel reader results live in tools/wheelreader/README.md; WHEEL_READER_PLAN.md gets a status
  column and B1 marked done.
- README.md: the entity list lives in DOCS.md; development points to CLAUDE.md.
- DOCS.md and the store text: object count, wheel reader and feedback wording updated.
- tests/test_docs.py checks every link, repository path, code name and `setting` (value) in the docs.
- SECURITY.md and issue forms (bug report; questions, ideas and shared readings go to Discussions).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0126B6HCdwnvuVrRkMeMEXfo
---
 .github/ISSUE_TEMPLATE/bug_report.yml  |  45 ++++
 .github/ISSUE_TEMPLATE/config.yml      |   8 +
 CLAUDE.md                              | 345 ++++++++++---------------
 README.md                              |  75 +-----
 SECURITY.md                            |  11 +
 docs/ARCHITECTURE.md                   |  33 ++-
 docs/RELEASING.md                      | 113 ++++++++
 docs/SCOPE.md                          | 146 +++--------
 docs/WHEEL_READER_PLAN.md              |  85 +++---
 tools/wheelreader/README.md            |  13 +-
 visionstate/DOCS.md                    |  16 +-
 visionstate/README.md                  |   6 +-
 visionstate/backend/tests/test_docs.py | 155 +++++++++++
 13 files changed, 610 insertions(+), 441 deletions(-)
 create mode 100644 .github/ISSUE_TEMPLATE/bug_report.yml
 create mode 100644 .github/ISSUE_TEMPLATE/config.yml
 create mode 100644 SECURITY.md
 create mode 100644 docs/RELEASING.md
 create mode 100644 visionstate/backend/tests/test_docs.py

diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
new file mode 100644
index 0000000..1690e17
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -0,0 +1,45 @@
+name: Bug report
+description: Something does not work as described in the documentation
+labels: [bug]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for reporting! Remove camera addresses and passwords from anything you paste.
+  - type: input
+    id: version
+    attributes:
+      label: VisionState version
+      description: Settings → Apps → VisionState (or VisionState (beta))
+      placeholder: "0.8.0"
+    validations:
+      required: true
+  - type: dropdown
+    id: kind
+    attributes:
+      label: Sensor kind
+      options:
+        - State sensor
+        - Object sensor
+        - Reading sensor
+        - Not about one sensor
+    validations:
+      required: true
+  - type: input
+    id: system
+    attributes:
+      label: Home Assistant version and hardware
+      placeholder: "2026.10.0, Raspberry Pi 4 / Intel NUC / Proxmox VM"
+  - type: textarea
+    id: what
+    attributes:
+      label: What happened, and what did you expect?
+      description: Steps to see it again help a lot. A screenshot is welcome.
+    validations:
+      required: true
+  - type: textarea
+    id: log
+    attributes:
+      label: Log
+      description: The app's Log tab, around the time it happened.
+      render: text
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 0000000..3f91377
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,8 @@
+blank_issues_enabled: true
+contact_links:
+  - name: Questions, ideas and shared meter readings
+    url: https://github.com/oleost/VisionState/discussions
+    about: Ask, suggest a feature, or share an export of readings (Quality tab → Export readings) to help improve reading.
+  - name: Security vulnerability
+    url: https://github.com/oleost/VisionState/security/advisories/new
+    about: Report it privately, not as an issue.
diff --git a/CLAUDE.md b/CLAUDE.md
index 66ca9fe..47a6613 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -3,13 +3,39 @@
 VisionState — a Home Assistant app that turns camera images into sensors, all locally:
 **states** the user teaches it (garage door open/closed; embedding model + small per-sensor
 classifier), **objects** found by a pretrained detector (people, cars, animals) and **readings**
-(a number on a display, OCR). This file is the contributor guide for people and AI assistants.
+(a number on a display or the wheels of a meter). This file is the contributor guide for people
+and AI assistants.
 
-- **Source of truth for scope and design decisions:** [`docs/SCOPE.md`](docs/SCOPE.md).
-  Update it when a decision changes.
-- **Language:** everything in the repository (code, comments, UI text, docs, commit
-  messages, this file) is written in English.
-- Public repository `github.com/oleost/VisionState`, Apache-2.0. Pull requests go to `beta`.
+- **Language:** everything in the repository (code, comments, UI text, docs, commit messages) is
+  written in English.
+- Public repository `github.com/oleost/VisionState`, Apache-2.0. Pull requests go to `beta`;
+  releases and branches: [`docs/RELEASING.md`](docs/RELEASING.md).
+
+## Where things are written down
+
+Every fact has **one** home; other files link to it instead of repeating it.
+
+| What | Home |
+|---|---|
+| What the app does for a user, how to use it, the defaults a user sees | [`visionstate/DOCS.md`](visionstate/DOCS.md) (the app's *Documentation* tab) |
+| The pitch, install, screenshots | [`README.md`](README.md) (and the short store text `visionstate/README.md`) |
+| Design decisions and why, non-goals, roadmap, open ideas | [`docs/SCOPE.md`](docs/SCOPE.md) |
+| Where the code is, how a check flows, data on disk, recipes for common changes | [`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md) |
+| Values of defaults, limits and tunables | `settings.py` and the model registries — docs name the setting, not its value (DOCS.md may state what the user sees) |
+| Branches, channels, CI, release steps | [`docs/RELEASING.md`](docs/RELEASING.md) |
+| How to work: principles, checks, testing, pitfalls | this file |
+| The wheel reader model: data, licences, results, how to train it | [`tools/wheelreader/README.md`](tools/wheelreader/README.md) |
+| The plan for better counter readings, and its status | [`docs/WHEEL_READER_PLAN.md`](docs/WHEEL_READER_PLAN.md) |
+| What changed in a version | `visionstate/CHANGELOG.md` (written at release, never edited afterwards) |
+| Test image sources | `visionstate/backend/tests/assets/README.md` |
+
+- Update the home of a fact **in the same change** as the code; a change that makes a doc wrong is
+  not finished.
+- No "as of version X" or "currently vN" status lines outside the CHANGELOG and the roadmap: they go
+  stale. Say what is, and let git say when.
+- `tests/test_docs.py` (backend, runs in CI) checks every Markdown link, every repository path and
+  every `module.name` / `settings.X["key"]` in backticks against the code — a rename that leaves a
+  doc behind fails the tests.
 
 ## Principles (do not break these)
 
@@ -19,205 +45,114 @@ classifier), **objects** found by a pretrained detector (people, cars, animals)
 - **Camera credentials never leak:** `redact.py` masks them in logs, errors and exports.
 - **No hard-coded defaults** outside `settings.py` / the registries; the UI reads them from
   `GET /api/v1/config`.
-- Home Assistant state values stay lower-case keys (`open`, `car_parked`).
+- Home Assistant state values stay lower-case keys (`open`, `car_parked`); unique IDs and topics
+  never change once released.
 - Marketing text (README, release notes) promotes VisionState on its own merits — no comparisons
   with other products.
 
-## Layout and conventions
-
-- `visionstate/` is the Home Assistant app (build context of the Dockerfile).
-  - `backend/visionstate/settings.py` holds every backend default/tunable; import from there.
-  - **Map of the code: [`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md)** — what runs where, how a
-    check flows, which files a common change touches. Keep it current when the structure changes.
-  - `backend/visionstate/engine/` is the runtime: `Runtime` (`runtime.py`) is put together from one
-    mixin per part (`models`, `checks`, `objects`, `reading`, `teaching`, `training`, `publishing`,
-    `history`, `reminders`) on `RuntimeBase` (`base.py`, the shared state); a part inherits the parts it uses.
-    `state.py` holds `SensorConfig`/`LiveState`, `logic.py` the pure decisions. New engine code goes
-    into the part it belongs to; a long-running loop must survive any exception (log it once, try
-    again) — only `CancelledError` may end it.
-  - `backend/visionstate/backbones.json` (state sensors), `detectors.json` (object sensors) and
-    `readers.json` (reading sensors; also the wheel readers for mechanical counters, trained with
-    `tools/wheelreader` — see its README) are the model registries. Entries are never changed or
-    removed once released (a new model gets a new id); `python -m visionstate.backbones <dir>`
-    downloads every bundled model of all three.
-  - Sensor kinds (`settings.SENSOR_KINDS`): `single_state`, `objects` and `reading`; the UI's tabs per kind
-    are in `ui.ts` (`TABS_BY_KIND`). New features must say which kind(s) they apply to.
-  - The History page and every sensor's History tab are one view (`lib/components/history/`,
-    a row component per kind); its filter is in the page URL (`lib/history.ts`). On the server
-    `api/history.py` turns every filter into SQL in one place (`history_query`); a new filter
-    goes there, a new event into `settings.HISTORY_EVENTS` + `EVENT_ROWS` + `ui.ts HISTORY_EVENTS`.
-  - Object sensors can be taught (`teach.py`, `api/teach.py`, `settings.TEACH`): taught boxes are
-    `sample` rows with `object_label`; own labels live in `objects["custom"]`. The detector is never
-    retrained; DINOv2 (always loaded) compares boxes with the taught ones.
-  - `frontend/src/lib/tokens.css` holds all colours/type/spacing; `ui.ts` holds UI constants;
-    `api.ts` is the only place that builds API URLs; `router.svelte.ts` defines app paths.
-  - The UI reads sensor defaults, limits and palette from `GET /api/v1/config`.
-- Backend checks: `cd visionstate/backend && .venv/Scripts/python -m pytest -q && .venv/Scripts/ruff check visionstate tests && .venv/Scripts/ruff format visionstate tests`
-  (tests need the model: `python -m visionstate.backbones models`), and the types:
-  `.venv/Scripts/pyright --pythonpath .venv/Scripts/python.exe` (0 errors; CI runs it). CI measures
-  coverage and fails below 88 %; add `--cov=visionstate --cov-report=term-missing:skip-covered` to see
-  what is not tested.
-- Frontend checks: `cd visionstate/frontend && npm run check && npm run build`.
-- **UI testing routine** (every UI change, before a beta release) — many users run Home Assistant
-  on phones/tablets:
-  - Automated: `cd visionstate/frontend && npm run build && VS_PYTHON=../backend/.venv/Scripts/python npm run e2e`
-    (Playwright, `e2e/`; also runs in CI; once: `npx playwright install chromium webkit`). It starts the backend + `scripts/fake_camera.py`,
-    seeds one sensor of each kind (a trained state sensor, an object sensor on a real photo and
-    reading sensors on a drawn display and a drawn mechanical counter), and runs every page on **desktop** (1440×900, mouse),
-    **mobile** (Pixel 7, real touch via CDP) and **iphone** (iPhone 14, WebKit like the Home Assistant
-    app on iOS; gestures with the mouse): no console errors, no sideways scrolling, no text
-    running out of buttons or cards, plus the wizard (all three kinds), region editor gestures,
-    labelling, review, boxes, teaching an object sensor and readings. Add a test
-    for every new page or gesture.
-  - Look at the full-page screenshots in `test-results/pages/{desktop,mobile,iphone}/` after UI changes,
-    for every page in all three projects.
-  - Then try the change by hand in a real browser (Claude: the Chrome tools) — passing tests are not
-    enough: start `scripts/fake_camera.py 8198` and the backend (`VISIONSTATE_PORT`, `_DATA`, `_MEDIA`,
-    `_FRONTEND=frontend/dist`, `_BUNDLED_MODELS` as in `playwright.config.ts`; a copy of the last e2e
-    run's `data` and `media` folders gives seeded sensors), use the changed screens at desktop width
-    and at ~390 px, and check the console. Reload after a rebuild (a hash-only navigation keeps the old page).
-    A Chrome window does not get narrower than 500 px (and a maximised one not at all): for a real
-    phone width, open any same-origin URL (e.g. `/api/v1/status`) and replace the page with
-    `<iframe src="/#/…" style="width:390px;height:844px">` — media queries then see 390 px.
-  - Handy while working (add `VS_PYTHON=…` as above): only the phone `npx playwright test
-    --project=mobile`; one test `npx playwright test -g "reading sensor"`; watch it in a browser
-    `--headed` or step through it with `--ui`; after a failure `npx playwright show-trace
-    test-results/<test>/trace.zip` shows every step with DOM snapshots.
-  - Never rely on Ctrl/Shift/hover-only interactions; hide keyboard hints with `.kbd-only`.
-
-## Testing and verifying locally
-
-- **Run the app against a fake camera** (no Home Assistant needed):
-  `python scripts/fake_camera.py 8765`, then start the backend (see README → Run it locally) and
-  use `http://127.0.0.1:8765/…` as an *HTTP snapshot URL*. The camera serves `/snapshot.jpg`
-  (synthetic garage door; `/set?state=open|closed|partial`, `/set?night=1`), `/photo/<name>.jpg`
-  (real CC0 photos from `backend/tests/assets`, for object sensors) and
-  `/display.jpg?text=12:05&style=lcd|led` (a drawn seven-segment display, for reading sensors)
-  and `/counter.jpg?value=89939.5&digits=7&decimals=3` (a drawn mechanical counter with rolling
-  digit wheels; `.5` = the last wheel half way to the next digit).
-- **Check the Home Assistant side** with any local MQTT broker (e.g. Mosquitto on 1883) and
-  `VISIONSTATE_MQTT_HOST=127.0.0.1`: the discovery configs appear under `homeassistant/…` and
-  values under `visionstate/<slug>/…`. The CI smoke test does the same against the built image.
-- **Test pitfalls found the hard way** (keep tests stable):
-  - The UI polls frames and status, so the network never goes idle — wait for elements or for
-    `img.complete`, never for `networkidle`.
-  - History rows and retrains happen *after* a value or state is published.
-    Never read them right after; poll with `wait_for` / `expect.poll`. A sensor reports `ok` with
-    its first model — wait for `model.n_samples` when a test needs the model trained on all labels.
-  - Touch in Playwright goes through CDP (`e2e/helpers.ts`): a fast release starts a fling that
-    swallows the next tap, so drags rest briefly before lifting. Full-page screenshots drop touch
-    emulation — assert touch-only CSS in the test, not from a screenshot.
-  - A change that makes text longer while something reloads can shift the page on phones and make
-    a tap land elsewhere; keep the last result on screen while re-testing.
-  - A Svelte `$effect` tracks every state read until the first `await` of any function it calls.
-    If that function later sets what it read, the effect restarts it on every answer — the live
-    frames polled the camera dozens of times a second from 0.1.0 until 0.6.5. Call such functions
-    with `untrack`, name the real dependencies with `void x`, and give a polling loop a counter so
-    a restart ends the old loop (a request still on its way would otherwise start a second one).
-    The e2e test "no page asks the server more often than it polls" counts requests per page.
-  - The Chrome tab Claude drives counts as hidden (`document.visibilityState`); views that only
-    poll while visible look fine there. Use `setVisibility` (e2e) or override it by hand.
-- **Models:** registries pin a Hugging Face revision and SHA-256. Every model file is checked
-  against real inputs in tests (`test_objects.py`, `test_reading.py`) — one public ONNX conversion
-  turned out to be broken and only a test with real photos showed it. Test images must be CC0 /
-  public domain (list sources in `tests/assets/README.md`) or drawn by our own code
-  (`tests/displays.py`).
-- **Local experiment data** lives in `/VisionStateLocal/` (ignored by git, outside the Docker build
-  context): images with other licences may be used there for trying things out, never in the
-  repository, the tests or a shipped model. Its `README.md` lists each source and licence.
-- **Windows development:** create the venv inside the project (`visionstate/backend/.venv`; long
-  paths break pip elsewhere), and set `PYTHONUTF8=1` for scripts that read or write source files.
-  The app itself handles the Windows event loop (`__main__.py`).
-- **Docs screenshots** (`docs/promo/`): Playwright at 1440 px wide with device scale 1.2 (1728 px
-  images), against the fake camera and CC0 photos, with a local MQTT broker so the header shows
-  *MQTT connected*. Keep images in one row the same height, and show the user before publishing.
-
-## Branches, channels and releases
-
-- **Branches and channels — never commit to `main` directly.**
-  - `beta` is the working branch: every change lands here first (directly or via PR to `beta`).
-    Its `visionstate/config.yaml` is the beta channel ("VisionState (beta)", versions `X.Y.ZbN`,
-    own media folder). Home Assistant users get it via `https://github.com/oleost/VisionState#beta`.
-  - `main` is the stable channel and only changes by promoting a tested beta (PR from a
-    `promote/X.Y.Z` branch). `main` is branch-protected.
-  - `scripts/channel.py` is the only way to change name/version/channel fields in config.yaml.
-  - Home Assistant pulls prebuilt images (`image:` in config.yaml), so a version must never reach a
-    branch before its images exist. CI refuses tags that do not match config.yaml and branches that
-    carry the wrong channel.
-- **A beta tag is the CI run.** Run the local checks first (backend, frontend, e2e, hands-on in
-  a browser), then push the tag: its CI tests, builds both images on native runners, and — for
-  beta tags — creates the pre-release and fast-forwards `beta`. If it fails, nothing was
-  published: delete the tag (`git push origin :refs/tags/vX.Y.ZbN`, `git tag -d …`), fix, and tag
-  the same version again. For a stable release, or when unsure, run CI first on a temporary
-  branch with a draft PR against `beta` and close it afterwards.
-- Documentation-only changes (README, DOCS.md, CLAUDE.md, images) may go to `main` through a PR
-  without a beta; merge `main` back into `beta` afterwards.
-- **Beta release** (on `beta`, about 10 minutes, unattended after the tag):
-  0. **The maintainer says "go" first.** Before committing the change and releasing, start a local
-     instance for the maintainer to try (fake camera + backend with seeded sensors, see the UI
-     testing routine), give the URL of the changed screens, sum up what was done and show the
-     release notes (the new `CHANGELOG.md` entry). Commit, version and tag only after an explicit "go".
-  1. `python scripts/channel.py beta X.Y.ZbN`, add a `## X.Y.ZbN` entry to `visionstate/CHANGELOG.md`
-     (it becomes the release notes), commit. Do **not** push `beta`.
-  2. `git tag vX.Y.ZbN && git push origin vX.Y.ZbN` (**tag only**). CI then publishes
-     `ghcr.io/oleost/visionstate-{amd64,aarch64}:X.Y.ZbN`, creates the GitHub pre-release and moves
-     `beta` to the tagged commit; `gh run watch` follows it.
-  3. `git fetch origin && git status` — local `beta` should equal `origin/beta`. If the release
-     changed a file under `.github/workflows/`, CI may not push the branch: run `git push origin beta`
-     yourself once the images exist (the registry answers 200 for
-     `https://ghcr.io/v2/oleost/visionstate-<arch>/manifests/X.Y.ZbN` with an anonymous pull token).
-     A tagline for the release title is optional: `gh release edit vX.Y.ZbN --title "X.Y.ZbN — …"`.
-- **Before promoting a beta to stable** (there is no separate release candidate: the beta being
-  promoted is checked as it is) — go through all of it, and tell the user what
-  was checked and what was found:
-  1. **CI on the beta being promoted is green**, including the checks that only block stable
-     releases: the upgrade test from the last stable release and back (`scripts/upgrade_test.sh`,
-     both architectures), the old CPU check (amd64 `kvm64`, aarch64 `cortex-a53`), the memory peak
-     with all three models (see the notice in the smoke test; limit in `MEMORY_LIMIT`), the app
-     options check (`scripts/check_options.py`), `pip-audit` / `npm audit` (a warning on betas,
-     an error on stable), and e2e on desktop, Android and iPhone (WebKit).
-  2. **On the test Home Assistant** (a Home Assistant OS VM with the beta app installed):
-     - update the beta app to the beta being promoted; the standing test sensors (one of each kind) keep
-       working, their entities in Home Assistant keep their IDs, the log has no warnings or errors;
-     - restart Home Assistant, reboot the host and restart the MQTT broker: the app and its
-       entities come back by themselves;
-     - make a backup of the app and restore it: the sensors are back;
-     - **soak for 1 hour** with the sensors checking: memory of the app (Supervisor app stats)
-       and its data on disk level off instead of growing, the log stays quiet;
-     - open the app in Home Assistant at desktop width and on a phone (Ingress);
-     - prepare the **update from the last stable release in a real Home Assistant**: the stable
-       app (repository without `#beta`) is installed there too. It shares MQTT topics and unique
-       IDs with the beta app, so never run both: stop the beta app, start the stable one and give
-       it one sensor of each kind (import exports of the standing test sensors).
-  3. **What changed since the last stable release**: new or upgraded dependencies have a licence
-     that fits Apache-2.0 (`git diff vX.Y.Z -- visionstate/backend/requirements.txt
-     visionstate/frontend/package.json`); `homeassistant:` in config.yaml still names the oldest
-     Home Assistant that works; DOCS.md, README.md and SCOPE.md describe what is being released.
-- **Promote to stable** (only when the user says the beta is tested, after the checks above):
-  1. `git switch -c promote/X.Y.Z beta`; `python scripts/channel.py stable X.Y.Z`; in the changelog,
-     merge the `X.Y.ZbN` entries into one `## X.Y.Z` entry; commit.
-  2. `git merge origin/main`; if `visionstate/config.yaml` conflicts, re-run
-     `python scripts/channel.py stable X.Y.Z` to resolve it; commit.
-  3. `git tag vX.Y.Z && git push origin vX.Y.Z` (tag only); wait for the images.
-  4. Push the branch, open a PR to `main`, wait for CI, merge it. Right away, update the stable app
-     on the test Home Assistant (reload the store): its sensors keep working, their entities keep
-     their IDs, the log has no warnings or errors. Only then `gh release create vX.Y.Z --latest`;
-     stop the stable app and start the beta app again.
-  5. Merge `main` back into `beta`, keeping beta's config: `git switch beta && git merge main`,
-     then `python scripts/channel.py beta <last beta version>` and commit (a beta version whose
-     images exist); the next beta release sets the next version.
-- Python version is **3.14** (Dockerfile image, CI `setup-python`, ruff `target-version`, local
-  `.venv` created with `py -3.14`). Upgrade all of them together; Dependabot ignores Python image
-  upgrades for that reason.
-- **Old CPUs and virtual machines must keep working** (x86-64-v1, e.g. Proxmox `kvm64`; ARMv8.0,
-  e.g. Raspberry Pi 3/4 and Home Assistant Yellow with CM4 — Green is ARMv8.2). NumPy is pinned
-  below 2.4 because newer wheels need x86-64-v2 (Dependabot ignores them). CI runs
-  `scripts/cpu_probe.py` in each image under an emulated old CPU (amd64 `kvm64`, aarch64
-  `cortex-a53`); when it fails after a dependency update, that update needs a newer CPU — keep the
-  old version.
-- **Docs checklist** — when behaviour, defaults, versions or the workflow change, update in the same
-  change: `visionstate/DOCS.md` (user guide in HA), `README.md` (front page), `docs/SCOPE.md`
-  (design as built, roadmap), `visionstate/CHANGELOG.md`, and this file.
-- Dependency updates: Dependabot opens one grouped PR per ecosystem monthly. CI runs the tests, a
-  smoke test that starts the built image (both architectures) against a real MQTT broker, and the
-  upgrade test from the last stable release.
+## Conventions
+
+- `visionstate/` is the Home Assistant app (build context of the Dockerfile); the map of the code
+  is [`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md) — keep it current when the structure changes.
+- Sensor kinds (`settings.SENSOR_KINDS`): `single_state`, `objects`, `reading`. A new feature says
+  which kind(s) it applies to.
+- New engine code goes into the part (mixin) it belongs to. A long-running loop must survive any
+  exception (log it once, try again) — only `CancelledError` may end it.
+- Model registries (`backbones.json`, `detectors.json`, `readers.json`): entries are never changed
+  or removed once released; a new model gets a new id, pinned by Hugging Face revision and SHA-256,
+  and is checked against real inputs in the tests (one public ONNX conversion turned out to be
+  broken and only a test with real photos showed it).
+- Frontend: colours/type/spacing only in `tokens.css`, UI constants in `ui.ts`, API URLs only in
+  `api.ts`, app paths in `router.svelte.ts`. There is **no formatter** for the frontend (single
+  quotes, ~120-character lines, kept by hand) — never run Prettier on it. Backend: ruff format.
+- Never rely on Ctrl/Shift/hover-only interactions; hide keyboard hints with `.kbd-only`.
+- Test images must be CC0 / public domain (sources in `tests/assets/README.md`) or drawn by our own
+  code (`tests/displays.py`). Local experiment data lives in `/VisionStateLocal/` (ignored by git,
+  outside the Docker build context): images with other licences may be used there for trying things
+  out, never in the repository, the tests or a shipped model; its `README.md` lists each source.
+
+## Checks
+
+- Backend: `cd visionstate/backend && .venv/Scripts/python -m pytest -q && .venv/Scripts/ruff check visionstate tests && .venv/Scripts/ruff format visionstate tests`
+  and the types `.venv/Scripts/pyright --pythonpath .venv/Scripts/python.exe` (0 errors; on Linux and
+  macOS the venv's programs are in `.venv/bin/`). Tests need
+  the bundled models once: `python -m visionstate.backbones models`. CI fails below 88 % coverage;
+  `--cov=visionstate --cov-report=term-missing:skip-covered` shows what is not tested.
+- Frontend: `cd visionstate/frontend && npm run check && npm run build`.
+- UI: see the routine below — every UI change, and before every beta release.
+
+## Run it locally
+
+```bash
+cd visionstate/backend
+py -3.14 -m venv .venv                     # inside the project: long paths break pip elsewhere on Windows
+.venv/Scripts/pip install -r requirements-dev.txt
+.venv/Scripts/python -m visionstate.backbones models
+VISIONSTATE_DATA=./dev/data VISIONSTATE_MEDIA=./dev/media VISIONSTATE_BUNDLED_MODELS=./models \
+  HA_URL=http://homeassistant.local:8123 HA_TOKEN=<long-lived token> \
+  VISIONSTATE_MQTT_HOST=<broker> .venv/Scripts/python -m visionstate
+```
+
+The UI is then on port 8099 (no login: only on your own machine). `npm run dev` in
+`visionstate/frontend` serves the UI with hot reload and proxies `/api` to it. On Windows set
+`PYTHONUTF8=1` for scripts that read or write source files; the app handles the Windows event loop
+itself (`__main__.py`).
+
+- **Fake camera** (no Home Assistant needed): `python scripts/fake_camera.py 8765`, and use
+  `http://127.0.0.1:8765/…` as an *HTTP snapshot URL*: `/snapshot.jpg` (a drawn garage door;
+  `/set?state=open|closed|partial`, `/set?night=1`), `/photo/<name>.jpg` (the CC0 photos from
+  `backend/tests/assets`, for object sensors), `/display.jpg?text=12:05&style=lcd|led` (a drawn
+  seven-segment display) and `/counter.jpg?value=89939.5&digits=7&decimals=3` (a drawn mechanical
+  counter; `.5` = the last wheel half way to the next digit).
+- **The Home Assistant side:** any local MQTT broker (e.g. Mosquitto on 1883) and
+  `VISIONSTATE_MQTT_HOST=127.0.0.1`: discovery configs appear under `homeassistant/…`, values under
+  `visionstate/<slug>/…`.
+
+## UI testing routine
+
+Many users run Home Assistant on phones and tablets.
+
+- **Automated:** `cd visionstate/frontend && npm run build && VS_PYTHON=../backend/.venv/Scripts/python npm run e2e`
+  (Playwright, `e2e/`; once: `npx playwright install chromium webkit`). It starts the backend and
+  `scripts/fake_camera.py`, seeds one sensor of each kind (a trained state sensor, an object sensor
+  on a real photo, reading sensors on a drawn display and a drawn mechanical counter), and runs every
+  page on **desktop** (1440×900, mouse), **mobile** (Pixel 7, real touch via CDP) and **iphone**
+  (iPhone 14, WebKit like the Home Assistant app on iOS): no console errors, no sideways scrolling,
+  no text running out of buttons or cards, plus the wizard, region editor gestures, labelling,
+  review, boxes, teaching and readings. Add a test for every new page or gesture.
+- Look at the full-page screenshots in `test-results/pages/{desktop,mobile,iphone}/` for every page.
+- **Then by hand in a real browser** (Claude: the Chrome tools) — passing tests are not enough:
+  start `scripts/fake_camera.py 8198` and the backend (`VISIONSTATE_PORT`, `_DATA`, `_MEDIA`,
+  `_FRONTEND=frontend/dist`, `_BUNDLED_MODELS` as in `playwright.config.ts`; a copy of the last e2e
+  run's `data` and `media` folders gives seeded sensors), use the changed screens at desktop width
+  and at ~390 px, and check the console. Reload after a rebuild (a hash-only navigation keeps the
+  old page). A Chrome window does not get narrower than 500 px: for a real phone width, open any
+  same-origin URL (e.g. `/api/v1/status`) and replace the page with
+  `<iframe src="/#/…" style="width:390px;height:844px">`.
+- Handy: only the phone `npx playwright test --project=mobile`; one test `-g "reading sensor"`;
+  watch it `--headed` or step through it `--ui`; after a failure `npx playwright show-trace
+  test-results/<test>/trace.zip`.
+- **Docs screenshots** (`docs/promo/`): Playwright at 1440 px wide with device scale 1.2, against the
+  fake camera and CC0 photos, with a local MQTT broker so the header shows *MQTT connected*. Keep
+  images in one row the same height, and show the maintainer before publishing.
+
+## Test pitfalls found the hard way
+
+- The UI polls frames and status, so the network never goes idle — wait for elements or for
+  `img.complete`, never for `networkidle`.
+- History rows and retrains happen *after* a value or state is published. Never read them right
+  after; poll with `wait_for` / `expect.poll`. A sensor reports `ok` with its first model — wait for
+  `model.n_samples` when a test needs the model trained on all labels.
+- Touch in Playwright goes through CDP (`e2e/helpers.ts`): a fast release starts a fling that
+  swallows the next tap, so drags rest briefly before lifting. Full-page screenshots drop touch
+  emulation — assert touch-only CSS in the test, not from a screenshot.
+- A change that makes text longer while something reloads can shift the page on phones and make a
+  tap land elsewhere; keep the last result on screen while re-testing.
+- A Svelte `$effect` tracks every state read until the first `await` of any function it calls. If
+  that function later sets what it read, the effect restarts it on every answer — the live frames
+  polled the camera dozens of times a second from 0.1.0 until 0.6.5. Call such functions with
+  `untrack`, name the real dependencies with `void x`, and give a polling loop a counter so a
+  restart ends the old loop. The e2e test "no page asks the server more often than it polls"
+  counts requests per page.
+- The Chrome tab Claude drives counts as hidden (`document.visibilityState`); views that only poll
+  while visible look fine there. Use `setVisibility` (e2e) or override it by hand.
diff --git a/README.md b/README.md
index 717745c..421e1b6 100644
--- a/README.md
+++ b/README.md
@@ -133,16 +133,8 @@ help test them, add `https://github.com/oleost/VisionState#beta` as a repository
    **20 per state**, including some at night.
 5. Under **Settings → When to check**, add your motion sensor or garage opener as a trigger.
 
-That's it — `sensor.garage_door` is now in Home Assistant:
-
-| Entity | What it is |
-|---|---|
-| `sensor.<name>` | The state (`open`, `closed`, …) — or `unknown` when the AI isn't sure |
-| `sensor.<name>_confidence` | How sure the AI is, in % |
-| `image.<name>_last_frame` | The region that was classified |
-| `button.<name>_classify_now` | Check right now (handy in automations) |
-| `switch.<name>_enabled` | Pause / resume |
-| `sensor.visionstate_review_queue` | Frames waiting for review (all sensors) |
+That's it — `sensor.garage_door` is now in Home Assistant, with its confidence, the last frame,
+a button to check now and a switch to pause it:
 
 ```yaml
 # Example: notify when the garage door has been open for 10 minutes
@@ -158,8 +150,8 @@ actions:
       message: The garage door has been open for 10 minutes.
 ```
 
-The full user guide is in [`visionstate/DOCS.md`](visionstate/DOCS.md) (also on the app's
-*Documentation* tab).
+The full user guide — every entity, object and reading sensors, triggers, review — is in
+[`visionstate/DOCS.md`](visionstate/DOCS.md) (also on the app's *Documentation* tab).
 
 ## How it works
 
@@ -198,58 +190,17 @@ the app's data folder (included in Home Assistant backups).
 ## Development
 
 ```
-visionstate/            Home Assistant app (config.yaml, Dockerfile, docs)
-  backend/              Python 3.14 · FastAPI · ONNX Runtime · scikit-learn
-  frontend/             Svelte 5 · Vite · TypeScript
-scripts/channel.py      Switches the app config between the stable and beta channel
-scripts/fake_camera.py  A fake camera (garage door, real photos, drawn displays and counters) for local testing
-docs/SCOPE.md           Design as built, decisions and roadmap
-CLAUDE.md               Contributor guide: conventions, how to test and verify, release steps
-```
-
-New features land on the `beta` branch first and reach `main` (stable) only after testing;
-please open pull requests against `beta`. [`CLAUDE.md`](CLAUDE.md) explains the conventions, how to
-test changes locally (fake camera, MQTT, UI tests on desktop and phone) and the pitfalls we ran
-into — it is written for people and AI coding assistants alike.
-
-<details>
-<summary>Run it locally</summary>
-
-Backend:
-
-```bash
-cd visionstate/backend
-python3.14 -m venv .venv && . .venv/bin/activate  # Windows: py -3.14 -m venv .venv; .venv\Scripts\activate
-pip install -r requirements-dev.txt
-python -m visionstate.backbones models            # download the bundled model once
-pytest
-VISIONSTATE_DATA=./dev/data VISIONSTATE_MEDIA=./dev/media VISIONSTATE_BUNDLED_MODELS=./models \
-  HA_URL=http://homeassistant.local:8123 HA_TOKEN=<long-lived token> \
-  VISIONSTATE_MQTT_HOST=<broker> python -m visionstate
-```
-
-> ⚠️ Outside Home Assistant the app has **no login**: anyone who can reach port 8099 can use it.
-> Only run it like this on your own machine or behind a reverse proxy with authentication.
-
-Frontend (proxies `/api` to the backend on port 8099):
-
-```bash
-cd visionstate/frontend
-npm install
-npm run dev
-```
-
-UI tests (Playwright) start the backend and a fake camera by themselves and run every page on a
-desktop browser and on an emulated phone with touch:
-
-```bash
-cd visionstate/frontend
-npx playwright install chromium   # once
-npm run build
-VS_PYTHON=../backend/.venv/bin/python npm run e2e
+visionstate/          the Home Assistant app — backend/ (Python 3.14, FastAPI, ONNX Runtime) and frontend/ (Svelte 5)
+tools/wheelreader/    training code of the wheel reader model
+scripts/              fake camera for local testing, channel switch, CI helpers
+docs/                 design (SCOPE), map of the code (ARCHITECTURE), releases (RELEASING)
 ```
 
-</details>
+New features land on the `beta` branch first and reach `main` (stable) only after testing; please
+open pull requests against `beta`. [`CLAUDE.md`](CLAUDE.md) is the contributor guide — how to run it
+locally against a fake camera, the checks, the UI tests on desktop and phones, and the pitfalls we
+ran into — written for people and AI coding assistants alike. Security issues: see
+[`SECURITY.md`](SECURITY.md).
 
 Issues and ideas are welcome in [GitHub Issues](https://github.com/oleost/VisionState/issues).
 
diff --git a/SECURITY.md b/SECURITY.md
new file mode 100644
index 0000000..53ccfab
--- /dev/null
+++ b/SECURITY.md
@@ -0,0 +1,11 @@
+# Security
+
+Please report a vulnerability privately: **Security → Report a vulnerability** on this repository
+(GitHub's private vulnerability reporting), not as a public issue. You get an answer within a few
+days, and a fix goes out as a beta first, then in the next stable release.
+
+Supported: the latest stable release and the current beta.
+
+What VisionState relies on, so you know what counts as a vulnerability: it is only reachable
+through Home Assistant Ingress (other requests are refused); camera credentials are never written to
+logs, errors or exports; nothing is sent anywhere but to your own Home Assistant and MQTT broker.
diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index bced7d7..de14a7b 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -4,6 +4,26 @@ A map of the code: what runs where, how one check flows through it, and which fi
 for common changes. What VisionState does and why is in [`SCOPE.md`](SCOPE.md); how to work on
 it is in [`CLAUDE.md`](../CLAUDE.md).
 
+## Repository
+
+```
+repository.yaml, README.md, CLAUDE.md, SECURITY.md
+visionstate/            the Home Assistant app: config.yaml, Dockerfile, DOCS.md, CHANGELOG.md, icon/logo
+  backend/              Python package `visionstate` + tests (the Dockerfile builds from visionstate/)
+  frontend/             Svelte app, Playwright tests in e2e/
+scripts/                channel.py (stable/beta config), fake_camera.py (test camera), CI helpers:
+                        check_options.py, cpu_probe.py, upgrade_test.sh, discovery_ids.py
+tools/wheelreader/      training code of the wheel reader model (not part of the app image)
+docs/                   SCOPE.md, ARCHITECTURE.md, RELEASING.md, WHEEL_READER_PLAN.md, promo/ (README images)
+.github/                CI workflow, Dependabot, issue forms
+```
+
+**Stack:** Python 3.14, FastAPI, uvicorn, SQLAlchemy (SQLite), asyncio; ONNX Runtime (CPU only),
+NumPy, Pillow, PyAV (bundled FFmpeg), scikit-learn; aiomqtt, websockets, httpx. Frontend: Svelte 5 +
+Vite + TypeScript, plain CSS with design tokens, a hash router (Ingress-safe), bundled fonts. Docker
+image `python:3.14-slim`, built by GitHub Actions and published on GHCR. Quality: pytest (with a fake
+camera, Home Assistant and MQTT), ruff, pyright, svelte-check, Playwright, an image smoke test.
+
 ## The process
 
 One Python process (`python -m visionstate`) serves the API and the built UI through Home
@@ -97,8 +117,12 @@ error is kept in `LiveState.error` and shown in the UI.
 - **SQLite** (`<data>/visionstate.db`): `sensor` (+ `state`), `sample` (+ `sample_label`,
   `embedding` cache), `prediction` (the history and the review queue), `reading_stat`,
   `model_info` (per sensor), `setting` (global settings as JSON).
-- **Files** (`<media>/`): `samples/<sensor>/`, `history/<sensor>/`, thumbnails; `<data>/heads/`
-  (trained heads), `<data>/models/` (downloaded models).
+- **Files** (`<media>/`): `samples/<sensor>/` (training images; for object sensors the crops of
+  taught boxes), `history/<sensor>/`, `thumbs/`; `<data>/heads/` (trained heads), `<data>/models/`
+  (downloaded models, left out of backups).
+- **Object sensors' taught boxes** are `sample` rows with `object_label`, `detected`, `box` and
+  `score`; own labels live in `sensor.objects["custom"]`. Readings and object events are
+  `prediction` rows (see `db.py`).
 - **Settings of a sensor** that have many fields (`triggers`, `objects`, `reading`, `review`) are
   stored as JSON and always read through `settings.merge_*`, so a field added later gets its
   default for existing sensors without a migration.
@@ -113,6 +137,7 @@ error is kept in `LiveState.error` and shown in the UI.
 | `lib/router.svelte.ts` | Hash routes (`paths`), a page's query (`route.query`, `setQuery`) |
 | `lib/history.ts` | The history filter: from and to the page URL, and as the API takes it |
 | `lib/ui.ts` | UI constants (tabs per sensor kind, wizard texts); `tokens.css` colours, type, spacing |
+| `lib/objects.ts`, `lib/reading.ts`, `lib/roi.ts`, `lib/format.ts` | Helpers: names and colours of objects and own labels, reading values and units, region geometry, formatting |
 | `pages/` | Dashboard, the new sensor wizard (`NewSensor.svelte`; its Detect step per kind in `pages/wizard/`), Review, History, Settings, and `SensorPage` with one file per tab in `pages/sensor/` (its History tab is `HistoryView`) |
 | `lib/components/history/` | `HistoryView` (filters, list, paging, new rows) shared by the History page and the History tabs, and one row component per sensor kind |
 | `lib/components/` | Building blocks (region editor, frames with boxes, editors for triggers, reading, states…) |
@@ -139,6 +164,10 @@ a test that an old database is upgraded (see `tests/test_entity_ids.py`).
 **A new API endpoint**: in the `api/` file of its topic, input as a pydantic model, its function
 in `lib/api.ts`, its type in `lib/types.ts`.
 
+**A new history event**: its name in `settings.HISTORY_EVENTS`, which rows it matches in
+`EVENT_ROWS` (`api/history.py`; every filter becomes SQL in `history_query`, nowhere else), its
+label in `HISTORY_EVENTS` in `lib/ui.ts`.
+
 **A new AI model**: a new entry in the registry JSON (never change a released one), checked
 against real inputs in the tests.
 
diff --git a/docs/RELEASING.md b/docs/RELEASING.md
new file mode 100644
index 0000000..0ad5b57
--- /dev/null
+++ b/docs/RELEASING.md
@@ -0,0 +1,113 @@
+# Releasing
+
+Branches, channels, versions and the release steps. How to work on the code is in
+[`CLAUDE.md`](../CLAUDE.md).
+
+## Channels and branches
+
+| | Stable | Beta |
+|---|---|---|
+| Branch | `main` (branch-protected; changes only by promoting a tested beta) | `beta` (every change lands here first, directly or by a PR to `beta`) |
+| Home Assistant repository | `https://github.com/oleost/VisionState` | `https://github.com/oleost/VisionState#beta` |
+| App name | VisionState | VisionState (beta) |
+| Version | `X.Y.Z` | `X.Y.ZbN` |
+| Images | `ghcr.io/oleost/visionstate-{amd64,aarch64}:X.Y.Z` (+`latest`) | `…:X.Y.ZbN` (+`beta`) |
+| Media folder | `/media/visionstate` | `/media/visionstate_beta` |
+| GitHub release | latest | pre-release |
+
+- **Never commit to `main` directly.** Documentation-only changes (Markdown, images, issue
+  templates) may go to `main` through a PR without a beta; merge `main` back into `beta` afterwards.
+- `scripts/channel.py` is the only way to change the name, version and channel fields in
+  `visionstate/config.yaml`.
+- Home Assistant pulls prebuilt images (`image:` in config.yaml), so a version must never reach a
+  branch before its images exist. CI refuses tags that do not match config.yaml and branches that
+  carry the wrong channel.
+- The slug (`visionstate`), the MQTT discovery prefix and node id (`homeassistant` / `visionstate`)
+  and every `unique_id` never change: entity IDs in Home Assistant hang on them.
+
+## What CI does
+
+On every push to `main`/`beta`, every PR and every tag (`.github/workflows/ci.yml`): backend
+(ruff, pyright, pytest with coverage ≥ 88 % — the docs check included —, the app options check
+`scripts/check_options.py`, `pip-audit`), frontend (`svelte-check`, build, `npm audit`), e2e on
+desktop, Android and iPhone (WebKit), then both images built on native runners and tested: a smoke
+test against a real MQTT broker (start, discovery, the memory peak with all three models under
+`MEMORY_LIMIT`, a clean `docker stop`), `scripts/cpu_probe.py` under an emulated old CPU (amd64
+`kvm64`, aarch64 `cortex-a53`) and `scripts/upgrade_test.sh` (from the last stable release and
+back). The audits only warn on betas and block stable releases. Only a version tag publishes the
+images; a beta tag then also creates the pre-release from the changelog entry and fast-forwards
+`beta`.
+
+If a tag's CI fails, nothing was published: delete the tag (`git push origin :refs/tags/vX.Y.ZbN`,
+`git tag -d …`), fix, and tag the same version again. When unsure, run CI first on a temporary
+branch with a draft PR against `beta` and close it afterwards.
+
+## Beta release
+
+On `beta`, about 10 minutes, unattended after the tag.
+
+0. **The maintainer says "go" first.** Run the local checks (backend, frontend, e2e, hands-on in a
+   browser; see `CLAUDE.md`), start a local instance for the maintainer to try (fake camera +
+   backend with seeded sensors), give the URL of the changed screens, sum up what was done and show
+   the release notes (the new `CHANGELOG.md` entry). Commit, version and tag only after an explicit
+   "go".
+1. `python scripts/channel.py beta X.Y.ZbN`, add a `## X.Y.ZbN` entry to `visionstate/CHANGELOG.md`
+   (it becomes the release notes), commit. Do **not** push `beta`.
+2. `git tag vX.Y.ZbN && git push origin vX.Y.ZbN` (**tag only**); `gh run watch` follows it.
+3. `git fetch origin && git status` — local `beta` should equal `origin/beta`. If the release
+   changed a file under `.github/workflows/`, CI may not push the branch: run `git push origin beta`
+   yourself once the images exist (the registry answers 200 for
+   `https://ghcr.io/v2/oleost/visionstate-<arch>/manifests/X.Y.ZbN` with an anonymous pull token).
+   A tagline for the release title is optional: `gh release edit vX.Y.ZbN --title "X.Y.ZbN — …"`.
+
+## Before promoting a beta to stable
+
+There is no separate release candidate: the beta being promoted is checked as it is. Go through
+all of it and tell the maintainer what was checked and what was found.
+
+1. **CI on that beta is green**, including what only blocks stable releases (the audits).
+2. **On the test Home Assistant** (a Home Assistant OS VM with the beta app installed):
+   - update the beta app to the beta being promoted; the standing test sensors (one of each kind)
+     keep working, their entities keep their IDs, the log has no warnings or errors;
+   - restart Home Assistant, reboot the host and restart the MQTT broker: the app and its entities
+     come back by themselves;
+   - make a backup of the app and restore it: the sensors are back;
+   - **soak for 1 hour** with the sensors checking: the app's memory (Supervisor app stats) and its
+     data on disk level off instead of growing, the log stays quiet;
+   - open the app in Home Assistant at desktop width and on a phone (Ingress);
+   - prepare the **update from the last stable release**: the stable app is installed there too. It
+     shares MQTT topics and unique IDs with the beta app, so never run both: stop the beta app,
+     start the stable one, and give it one sensor of each kind (exports of the standing test sensors).
+3. **What changed since the last stable release**: new or upgraded dependencies have a licence that
+   fits Apache-2.0 (`git diff vX.Y.Z -- visionstate/backend/requirements.txt
+   visionstate/frontend/package.json`); `homeassistant:` in config.yaml still names the oldest Home
+   Assistant that works; the docs describe what is being released (the roadmap in `SCOPE.md` and
+   the status in `WHEEL_READER_PLAN.md` included).
+
+## Promote to stable
+
+Only when the maintainer says the beta is tested, after the checks above.
+
+1. `git switch -c promote/X.Y.Z beta`; `python scripts/channel.py stable X.Y.Z`; in the changelog,
+   merge the `X.Y.ZbN` entries into one `## X.Y.Z` entry; commit.
+2. `git merge origin/main`; if `visionstate/config.yaml` conflicts, re-run
+   `python scripts/channel.py stable X.Y.Z`; commit.
+3. `git tag vX.Y.Z && git push origin vX.Y.Z` (tag only); wait for the images.
+4. Push the branch, open a PR to `main`, wait for CI, merge it. Right away, update the stable app on
+   the test Home Assistant (reload the store): its sensors keep working, their entities keep their
+   IDs, the log has no warnings or errors. Only then `gh release create vX.Y.Z --latest`; stop the
+   stable app and start the beta app again.
+5. Merge `main` back into `beta`, keeping beta's config: `git switch beta && git merge main`, then
+   `python scripts/channel.py beta <last beta version>` and commit (a beta version whose images
+   exist); the next beta release sets the next version.
+
+## Dependencies and platforms
+
+- Dependabot opens one grouped PR per ecosystem monthly, against `beta`.
+- Python is **3.14** everywhere (Dockerfile image, CI `setup-python`, ruff `target-version`, the
+  local `.venv`). Upgrade all of them together; Dependabot ignores Python image upgrades for that
+  reason.
+- **Old CPUs and virtual machines must keep working** (x86-64-v1, e.g. Proxmox `kvm64`; ARMv8.0,
+  e.g. Raspberry Pi 3/4 and Home Assistant Yellow with CM4). NumPy is pinned below 2.4 because newer
+  wheels need x86-64-v2 (found through issue #29; Dependabot ignores them). When `cpu_probe.py`
+  fails after a dependency update, that update needs a newer CPU — keep the old version.
diff --git a/docs/SCOPE.md b/docs/SCOPE.md
index bec8c93..4c8d9b3 100644
--- a/docs/SCOPE.md
+++ b/docs/SCOPE.md
@@ -1,8 +1,9 @@
 # Scope & Design Decisions
 
-> Describes VisionState **as built** (stable 0.6.3, 2026-10-05) and the open
-> ideas. Update it whenever a decision changes.
-> Project: **VisionState** · Licence: Apache-2.0 · Repository: `github.com/oleost/VisionState`
+> The design as built, the decisions behind it, and the roadmap. Update it in the same change as
+> the decision. How to use the app: [`visionstate/DOCS.md`](../visionstate/DOCS.md); where the code
+> is: [`ARCHITECTURE.md`](ARCHITECTURE.md); channels and releases: [`RELEASING.md`](RELEASING.md).
+> Setting names below are keys in `settings.py`, which holds their values.
 
 ## 1. Vision
 
@@ -13,8 +14,8 @@ the matching state on a live image, or by bulk-uploading images/video. Everythin
 locally, on any CPU, with a polished UI inside Home Assistant.
 
 A second sensor kind, **object sensors**, finds common objects (people, cars, animals …) with a
-pretrained detector, and a third, **reading sensors**, reads a number from a display with a
-text recognizer. Neither needs training.
+pretrained detector, and a third, **reading sensors**, reads a number from a display or the wheels
+of a meter. Neither needs training.
 
 ## 2. Goals / Non-goals
 
@@ -31,7 +32,7 @@ text recognizer. Neither needs training.
 - Tracking objects across frames (identities, paths, line crossing), zones within one sensor,
   custom-trained detectors.
 - Multi-label sensors (the data model allows this later, see §10).
-- Reading pointer dials (needle gauges, the small red hands on some water meters) — see §16.
+- Reading pointer dials (needle gauges, the small red hands on some water meters) — see §14.
 - Cloud training or cloud inference; telemetry of any kind.
 - armv7 / i386 (deprecated by Home Assistant).
 
@@ -39,7 +40,7 @@ text recognizer. Neither needs training.
 
 | Term | Meaning |
 |---|---|
-| **Sensor** | One thing to recognise, e.g. "Garage door". Becomes one HA device with entities. Its **kind** is `single_state` (learned states) or `objects` (detector), fixed at creation. |
+| **Sensor** | One thing to recognise, e.g. "Garage door". Becomes one HA device with entities. Its **kind** is `single_state` (learned states), `objects` (detector) or `reading` (a number), fixed at creation. |
 | **State** | One possible value of a sensor. Has a display name ("Open") and a key (`open`, derived from the name, used in HA). |
 | **Source** | Where images come from: HA camera entity, HTTP snapshot URL, RTSP stream, or upload. |
 | **ROI** | Region of interest — a rectangle, or a polygon (up to `ROI_MAX_POINTS` corners), cropped from the frame before classification; outside a polygon is filled with a neutral colour. |
@@ -57,7 +58,7 @@ text recognizer. Neither needs training.
 - Home Assistant app with **Ingress** (sidebar panel, HA handles authentication); requests
   from anything other than the Ingress proxy are refused.
 - Architectures: `amd64`, `aarch64`. Prebuilt images on GHCR, pulled by Home Assistant.
-- Two release channels (see §18): stable (`main`) and beta (`beta` branch, `#beta` repo URL).
+- Two release channels, stable and beta ([`RELEASING.md`](RELEASING.md)).
 - Also runnable as a plain Docker container / Python process for development, configured via
   environment variables and a long-lived HA token (no login outside HA).
 - MQTT broker discovered automatically through the Supervisor services API
@@ -198,20 +199,13 @@ the digits) at a cost of `wheel_shift_penalty` per bin. The last wheel is rounde
 digit; the confidence is the mean probability per wheel. It always returns `digits` digits, so
 the digit count check does not apply. Counters made before it (no stored `counter_reader`) keep
 the text reader: schema v12 stores `"ocr"` for them, and so does importing an older export
-(`db.keep_text_reader`). How it is to get better (benchmark, the last value as an expectation,
-calibrated confidence, self-calibration of the region, wheels-v2, learning per sensor):
-[`WHEEL_READER_PLAN.md`](WHEEL_READER_PLAN.md). Training code and data sources: `tools/wheelreader`
-(synthetic wheels drawn with OFL fonts, the CC0 Word-Wheel Water Meter Dataset (Sci Data 2026),
-the CC0 exports shared in issues #32/#40).
-
-- Evaluated for the wheel reader (2026-10-09; whole reading exact, last digit rounded): a
-  user's ESP32 water meter with red wheels, held out from training: 17/17 (PP-OCRv6 small 15,
-  tiny 8 with 5 too high); two water meters never seen in training (bolausson, test only): 30–33
-  of 35 and 15–18 of 18 with no value too high (small 30 with 2 too high, 15 with 1); the
-  Dryad test set (2,400 photos): 97.7 % when the photo is the right way up. Trained without the
-  user's images it read them just as well. The remaining misses are ±1 on a half-turned last
-  wheel. The decode score separates right from wrong reads only moderately, so it is not used
-  for more than the confidence.
+(`db.keep_text_reader`). The model, its training data and its
+results: [`tools/wheelreader/README.md`](../tools/wheelreader/README.md); how it is to get better:
+[`WHEEL_READER_PLAN.md`](WHEEL_READER_PLAN.md).
+
+- Why a wheel reader (2026-10-09): on every meter tested it read more readings right than
+  PP-OCRv6 small and gave no value too high, which the text reader did (numbers in its README).
+  The remaining misses are ±1 on a half-turned last wheel.
 - Evaluated (spike on Commons photos): PP-OCR read LCD, LED, dot-matrix and flip-segment
   displays correctly (7/7 with a tight region); it fails on small blurry LCDs, and on rolling
   counter wheels when the whole counter is read as one line. DINOv2 per digit (4/23) and a CNN
@@ -319,15 +313,10 @@ the pixels are decoded.
 
 ## 8. Home Assistant integration (MQTT Discovery)
 
-One HA **device** per sensor:
-
-| Entity | Type | Purpose |
-|---|---|---|
-| `sensor.<name>` | `sensor` (`device_class: enum`, options = state keys + `unknown`) | The result |
-| `…_confidence` | `sensor` (%) | Top probability |
-| `image.…_last_frame` | `image` | The ROI that was classified |
-| `button.…_classify_now` | `button` | Check now (automations) |
-| `switch.…_enabled` | `switch` | Pause / resume |
+One HA **device** per sensor (the entities a user sees are listed in DOCS.md). A state sensor:
+the result as `sensor` with `device_class: enum` (options = state keys + `unknown`), its
+confidence, an `image` of the region that was classified, a `button` to check now and a `switch`
+to pause it.
 
 **Object sensors** replace the first two with two entities per selected class:
 `binary_sensor.<name>_<class>` (`device_class: occupancy`, attributes: confidence,
@@ -381,8 +370,9 @@ outside Home Assistant it shows the expected IDs.
 Principle: **easy by default, details on demand.** Dark theme, responsive.
 
 1. **Dashboard** — cards per sensor: live thumbnail, state, confidence, 24 h timeline, health.
-2. **New sensor wizard** — camera → region → *detect*: states (names) or objects (popular
-   first, all 80 behind "Show all", with a test on a fresh frame) → optional triggers.
+2. **New sensor wizard** — camera → region → *detect*: states (names), objects (popular
+   first, all 80 behind "Show all") or a reading (display or counter), each tested on a fresh
+   frame → optional triggers.
 3. **Label** — live view, one button per state (keys 1–9), current prediction, day/night
    coverage, last trigger, undo.
 4. **Upload** — drag & drop images/ZIP/video; label in a grid or accept all suggestions.
@@ -431,12 +421,12 @@ loads.
 
 ## 10. Data model & extensibility
 
-- SQLite; schema version in `PRAGMA user_version` with additive migrations (`db.MIGRATIONS`,
-  currently v10). An older version started on a newer database ignores the columns it does not
+- SQLite; schema version in `PRAGMA user_version` (`db.SCHEMA_VERSION`) with additive migrations
+  (`db.MIGRATIONS`). An older version started on a newer database ignores the columns it does not
   know (rollback works; checked by the upgrade test).
-- `sensor.kind`: `single_state`, `objects` (`sensor.objects` holds classes, `min_size`,
-  `clear_after_s`, `use_taught` and the own labels `custom`) or `reading` (`sensor.reading` holds mode, decimals, unit, device class,
-  display, `digits`, `max_step`, `spot_rate`); reserved for `multi_label`. Every reading is
+- `sensor.kind`: `single_state`, `objects` (`sensor.objects` holds the fields of
+  `settings.OBJECT_DEFAULTS`, the classes and the own labels `custom`) or `reading`
+  (`sensor.reading` holds the fields of `settings.READING_DEFAULTS`); reserved for `multi_label`. Every reading is
   counted per sensor and local day in `reading_stat` (reads, accepted, rejected per reason);
   every rejected reading is a `prediction` row with its frame and `review_reason` "rejected"
   (spot checks: "spot_check"), and `read_ok` / `correct_value` hold the user's verdict. Verified
@@ -501,41 +491,13 @@ automatically.
 - Not implemented: full export of all sensors + global settings, merge/replace import modes,
   exporting trained heads (retraining is faster than shipping them).
 
-## 13. Configuration (app options)
+## 13. Configuration
 
-`log_level`, `discovery_prefix`, and optional `mqtt_host`,
-`mqtt_port`, `mqtt_username`, `mqtt_password`. Everything else (AI model, review rules,
-sensor settings) lives in the UI.
+App options are only for logging and a broker Home Assistant does not provide (list in DOCS.md).
+Everything else (AI models, review rules, sensor settings) lives in the UI and the database, so it
+is part of backups and needs no restart.
 
-## 14. Tech stack
-
-| Layer | Choice |
-|---|---|
-| Runtime | Python 3.14 (image and CI), FastAPI, uvicorn, SQLAlchemy, asyncio |
-| Inference | ONNX Runtime, NumPy, Pillow, PyAV (bundled FFmpeg) |
-| Training | scikit-learn |
-| MQTT / HA | aiomqtt, websockets, httpx |
-| Frontend | Svelte 5 + Vite + TypeScript, plain CSS with design tokens (`tokens.css`), hash router (Ingress-safe), bundled fonts |
-| Packaging | HA app repository, Docker (python:3.14-slim), GitHub Actions → GHCR |
-| Quality | pytest (unit + integration with a fake camera/HA/MQTT; coverage measured in CI, floor 88 %), ruff, svelte-check, Playwright UI tests (desktop + phone with touch, against the real backend and `scripts/fake_camera.py`), image smoke test in CI, Dependabot (monthly, to `beta`) |
-| Docs | `README.md`, `visionstate/DOCS.md` (shown in HA), `CHANGELOG.md`, this file |
-
-## 15. Repo layout
-
-```
-/                       repository.yaml, README.md, CLAUDE.md
-/visionstate            HA app: config.yaml, Dockerfile, DOCS.md, CHANGELOG.md, icon/logo
-/visionstate/backend    Python package + tests (inside the app dir: the Dockerfile builds from it);
-                        visionstate/engine/ is the runtime: Runtime (runtime.py) from one mixin
-                        per part (models, checks, objects, reading, teaching, training,
-                        publishing, history), state.py and logic.py (pure decisions)
-/visionstate/frontend   Svelte app
-/scripts                channel.py (stable/beta config switch), fake_camera.py (test camera)
-/docs                   SCOPE.md, promo/ (README screenshots and logos)
-/.github                CI workflow, Dependabot
-```
-
-## 16. Roadmap
+## 14. Roadmap
 
 | Milestone | Content | Version |
 |---|---|---|
@@ -553,48 +515,20 @@ sensor settings) lives in the UI.
 | **Mechanical counters** ✅ | Rolling digit wheels (water, gas): one cell per wheel, digit count check | 0.6.1 (beta 0.6.1b6) |
 | **Teaching object sensors** ✅ | Correct a box (not it / something else), own labels ("Our car"), missed boxes, Quality tab | 0.6.3 (beta 0.6.3b10) |
 | **Readings & light** ✅ | Reading Quality tab and review of rejected readings, extra reading entities (rate, problem, reader image), a light for each check, regular check off / trigger states, entity IDs without prefix | 0.6.3 (betas 0.6.3b1–b16) |
+| **Hardening** ✅ | Loops that survive unexpected errors (MQTT bridge, sensor loops), redacted log lines and tracebacks, source address checks, frame and image size limits, sturdier import, engine split into a package, coverage in CI | 0.6.4 |
 | **History** ✅ | History page with filters shared by every sensor's History tab, the images behind the Quality tab's mix-ups | 0.7.0 |
-| **Wheel reader** ✅ | Mechanical counters read wheel by wheel with a model made for VisionState; wheels mid-turn read right | 0.8.0 (betas 0.7.1b3–b4) |
-| **Better counter readings** | Benchmark, the last value as an expectation, calibrated confidence, extra decimal, export v2, self-calibrating regions, wheels-v2, learning per sensor — see [`WHEEL_READER_PLAN.md`](WHEEL_READER_PLAN.md) | export v2 in 0.8.0 (betas 0.7.1b5–b6); the rest planned |
+| **Wheel reader** ✅ | Mechanical counters read wheel by wheel with a model made for VisionState; wheels mid-turn read right; export v2 of readings | 0.8.0 (betas 0.7.1b3–b6) |
 | **Review reminder** ✅ | A notification in Home Assistant (optionally a push) when frames have waited for review a long time | 0.8.0 (beta 0.7.1b1) |
-| **Hardening** ✅ | Loops that survive unexpected errors (MQTT bridge, sensor loops), redacted log lines and tracebacks, source address checks, frame and image size limits, sturdier import, engine split into a package, coverage in CI | 0.6.4 |
+| **Better counter readings** | Benchmark, the last value as an expectation, calibrated confidence, extra decimal, self-calibrating regions, wheels-v2, learning per sensor — status per phase in [`WHEEL_READER_PLAN.md`](WHEEL_READER_PLAN.md) | planned |
 
-**Open ideas** (not scheduled): full export/import of everything; merge/replace import;
+**Open ideas** (not scheduled): more on the History page (export of a filtered selection, a
+summary per hour or day, "seen in the frame" as a filter, saved filters, bulk actions); full
+export/import of everything; merge/replace import;
 less MQTT/camera traffic (throttle frame publishing, reuse the engine's latest frame in the UI);
 video de-duplication on the ROI instead of the full frame; light theme following Home
 Assistant; mechanical counters beyond [`WHEEL_READER_PLAN.md`](WHEEL_READER_PLAN.md): pointer
 dials and gauges; several readings per sensor (a sign with four prices, a
-counter plus its dials); issue templates; per-sensor model
+counter plus its dials); per-sensor model
 choice with unloading of idle models (DINOv2 stays loaded: object sensors that were taught use
 it); zones and line crossing for object sensors; classes outside COCO (an open-vocabulary
 detector) — until then, a state sensor covers many of them ("parcel on the doorstep").
-
-## 17. Identity
-
-| Item | Value |
-|---|---|
-| Name | VisionState (beta: "VisionState (beta)") |
-| App slug | `visionstate` |
-| Repository | `github.com/oleost/VisionState` (`#beta` for the beta channel) |
-| Images | `ghcr.io/oleost/visionstate-{amd64,aarch64}:X.Y.Z` (+`latest`) / `:X.Y.ZbN` (+`beta`) |
-| MQTT discovery prefix / node id | `homeassistant` / `visionstate` |
-| Licence | Apache-2.0 |
-
-## 18. Release channels
-
-- **beta** branch: all changes land here first; versions `X.Y.ZbN`, GitHub pre-releases, own
-  media folder. The maintainer runs it permanently.
-- **main** branch: stable; changes only by promoting a tested beta through a PR
-  (branch-protected, CI required).
-- CI builds, tests and smoke-tests the image on both architectures, each on its own hardware
-  (start, MQTT, discovery, clean `docker stop` with exit code 0) before publishing, refuses
-  tags that do not match `config.yaml`, and refuses the wrong channel on a branch.
-- The amd64 image is also run under an emulated x86-64-v1 CPU (`kvm64`, qemu-user) with
-  `scripts/cpu_probe.py`, which exercises every native library and the app. NumPy is pinned
-  below 2.4 for that reason (2.4+ needs x86-64-v2; found through issue #29). Checked 2026-10:
-  with NumPy 2.3.5 all other pins (SciPy 1.18, scikit-learn 1.9, ONNX Runtime 1.30, Pillow 12,
-  PyAV 18) work on `kvm64` and `qemu64`.
-- A beta tag is the whole release: after both images are published CI creates the pre-release
-  from the changelog entry and fast-forwards `beta`, so the branch never carries a version
-  without images. Stable releases are promoted by hand.
-- Step-by-step procedures are in `CLAUDE.md`.
diff --git a/docs/WHEEL_READER_PLAN.md b/docs/WHEEL_READER_PLAN.md
index 5edbb91..f7af4fb 100644
--- a/docs/WHEEL_READER_PLAN.md
+++ b/docs/WHEEL_READER_PLAN.md
@@ -1,8 +1,7 @@
 # Wheel reader: plan for better counter readings
 
-Status: **plan** (written 2026-10-09, after 0.7.1b4); B1 (the export) is built in 0.7.1b5. This document is
-the design for improving how VisionState reads **mechanical counters** (rolling digit wheels on
-water and gas meters). It applies to reading sensors with the *counter* display and the wheel reader
+The design for improving how VisionState reads **mechanical counters** (rolling digit wheels on
+water and gas meters); what is done and what is next is in the table in §9. It applies to reading sensors with the *counter* display and the wheel reader
 (`counter_reader: "wheels"`). The general design is in [`SCOPE.md`](SCOPE.md), and the model and its
 training are in [`tools/wheelreader`](../tools/wheelreader/README.md).
 
@@ -13,13 +12,7 @@ training are in [`tools/wheelreader`](../tools/wheelreader/README.md).
   over 100 bins (0.0–9.9). `readers.wheel_value` then finds the most likely value whose wheels fit
   together, from right to left, with one common shift of up to ±2 bins. The last wheel is rounded,
   and the confidence is the mean probability per wheel.
-- **Trained on** 600 k drawn cells, the CC0 Dryad Word-Wheel set (nearest-digit labels; about a
-  third of the crops are upside down, picked by a synthetic-only model) and the CC0 exports from
-  issues #32 and #40.
-- **Results:**
-  - #40 meter, held out: 17/17 (PP-OCRv6 small 15, tiny 8).
-  - Two meters never seen: 30–33/35 and 15–18/18, no value too high.
-  - Dryad test: 97.7 % with the right orientation.
+- **Training data and results:** [`tools/wheelreader/README.md`](../tools/wheelreader/README.md).
 - **Known weaknesses, with evidence:**
   - *±1 on the last digit* when the last wheel is about half way. The rounding moves with the
     per-frame shift (pedro32: 81 exact + 12 at +1).
@@ -46,7 +39,7 @@ training are in [`tools/wheelreader`](../tools/wheelreader/README.md).
 5. **No telemetry.** Data only arrives when a user exports it and shares it on purpose.
 6. **The registry rules stay.** A changed model, or changed calibration of a model, gets a new
    registry id. Existing sensors keep their behaviour until the user picks the new one, as in
-   0.7.1b4.
+   0.8.0 (counters made before the wheel reader kept the text reader).
 
 ## 3. Phase 0 — benchmark and metrics (the base for everything else)
 
@@ -147,9 +140,10 @@ In the decoder the expectation can be tested, explained, set per sensor and used
 
 ## 5. Phase B — better data in
 
-### B1. The export ("Export checked readings")
+### B1. The export ("Export readings") — done
 
-**Built in 0.7.1b5–b6**, with these changes to the table below (decided 2026-10-10):
+Built in 0.8.0; what it contains is in [`SCOPE.md`](SCOPE.md) §12. Decisions that differ from the
+first plan (2026-10-10):
 - the light is recorded per reading automatically (greyscale picture, and whether VisionState had
   the sensor's light on) instead of a flag in the dialog;
 - frames are **not** deduplicated in the app — a reading of the same frame is still a reading; the
@@ -157,28 +151,13 @@ In the decoder the expectation can be tested, explained, set per sensor and used
 - the unchecked accepted readings are **always** included (no option, no count limit): the images
   are what is scarce. Their value is the reader's guess, so the tools use them as labels only after
   a check (§B4);
-- no spot-check button (0.7.1b5 had one; removed in b6);
-- no count limits: one ZIP of at most 24 MB (GitHub's limit is 25 MB per file; about 1,000
-  readings), checked readings first, then the newest unchecked ones; what does not fit is counted
-  in `left_out`. Splitting into parts was built and dropped as more than we need now. Images are
-  not scaled down: the data stays as the app stored it.
-
-It already gives the region with a margin, the right value, the answer, the region each reading
-was made in and the CC0 licence. To add (format version `"format": 2` in `readings.json`, and
-README.txt to match):
-
-| Add | Why |
-|---|---|
-| reader id and model version **per reading** | today `reader` is the current choice for the whole export; after 0.7.1b4 OCR and wheel readings are mixed |
-| wheel reader details per reading: position and probability per wheel, the shift, V with its fraction | shows where the model hesitated without running it again; lets us check calibration on shared data |
-| an optional free-text **"What meter is this?"** in the export dialog | today it has to be written in the post, and it gets forgotten |
-| an optional **light** flag (day / night or IR / flash), not the time of day | IR and flash are their own problems; the time of day is more than we need |
-| each frame **once** | the old export had one image read 15 times |
-| optionally the latest **unchecked accepted** readings, marked `"answer": "unchecked"` | today almost only rejected readings get checked, so exports are mostly failures. A counter only goes up, so ordered unchecked readings still give useful weak labels |
-| a button to **spot-check** a few accepted readings before exporting | easier than changing `spot_rate` |
-
-Privacy stays as it is: only the region with a small margin, no camera, name or position, and the
-day only. The user looks at the images before sharing (README.txt).
+- no spot-check button; `spot_rate` is the way to get accepted readings checked;
+- one ZIP of at most 24 MB (GitHub's limit is 25 MB per file), checked readings first, then the
+  newest unchecked ones; what does not fit is counted in `left_out`. Splitting into parts was built
+  and dropped as more than we need now. Images are not scaled down.
+
+Privacy: only the region with a small margin, no camera, name or position, and the day only. The
+user looks at the images before sharing (README.txt in the ZIP).
 
 ### B2. Collecting
 
@@ -251,7 +230,9 @@ the correlation peak and skip frames where it is weak (light changes, IR switchi
 
 The manual 4-point correction stays planned for strongly angled cameras (see the earlier
 decision: measure the read rate at real angles first, for example with the user's ESP32 camera, or
-with `fake_camera` given a perspective parameter). C2 can later suggest the four points.
+with `fake_camera` given a perspective parameter). C2 can later suggest the four points. Open
+questions: reuse the 4-corner polygon region or a new corner tool (existing sensors must keep
+working), the corner order and orientation, and dragging corners on a phone.
 
 ## 7. Phase D — wheels-v2
 
@@ -294,21 +275,23 @@ with `fake_camera` given a perspective parameter). C2 can later suggest the four
 
 ## 9. Order, size and dependencies
 
-| Phase | Builds on | Size | Gives |
-|---|---|---|---|
-| 0 Benchmark | — | small | a fair measure for everything after |
-| A1 Expectation | 0 | medium | far fewer too-high and unclear readings |
-| A2 Calibration | 0 | small | a threshold that means what it says |
-| A3 Extra decimal | — | small | a smoother rate, earlier leak detection |
-| B1 Export v2 | — | small–medium | the data we need, labelled properly |
-| B2 Collecting | B1 | ongoing | more meter types |
-| C2 Calibration per sensor | 0 | medium | a stable last digit, forgiving regions |
-| C3 Camera drift | C2 | small–medium | sensors survive small camera moves |
-| C4 4-point perspective | real angle tests | medium | angled cameras |
-| D wheels-v2 | 0, B (data), C1 | large | a better base model |
-| E Learning per sensor | D (embedding), A2 | medium | the last step for an unusual meter |
-
-Suggested order: **0 → A1 + A2 + B1 (in parallel; B2 starts right after B1) → A3 → C2 → D → E**,
+| Phase | Builds on | Size | Gives | Status |
+|---|---|---|---|---|
+| 0 Benchmark | — | small | a fair measure for everything after | planned |
+| A1 Expectation | 0 | medium | far fewer too-high and unclear readings | planned |
+| A2 Calibration | 0 | small | a threshold that means what it says | planned |
+| A3 Extra decimal | — | small | a smoother rate, earlier leak detection | planned |
+| B1 Export v2 | — | small–medium | the data we need, labelled properly | **done, 0.8.0** |
+| B2 Collecting | B1 | ongoing | more meter types | next (issue forms point to Discussions) |
+| C2 Calibration per sensor | 0 | medium | a stable last digit, forgiving regions | planned |
+| C3 Camera drift | C2 | small–medium | sensors survive small camera moves | when a real case asks for it |
+| C4 4-point perspective | real angle tests | medium | angled cameras | waits for a test with a real angled meter |
+| D wheels-v2 | 0, B (data), C1 | large | a better base model | planned |
+| E Learning per sensor | D (embedding), A2 | medium | the last step for an unusual meter | planned |
+
+Update the status column in the same change that builds a phase.
+
+Suggested order: **0 → A1 + A2 (B2 alongside) → A3 → C2 → D → E**,
 with C3 and C4 when real cases ask for them. A and C2 need no new training and help every existing
 counter sensor right away.
 
diff --git a/tools/wheelreader/README.md b/tools/wheelreader/README.md
index 2c6414a..ed72861 100644
--- a/tools/wheelreader/README.md
+++ b/tools/wheelreader/README.md
@@ -26,9 +26,16 @@ Licence: Apache-2.0. Trained on data that may be used for anything:
 | [Word-Wheel Water Meter Dataset](https://doi.org/10.5061/dryad.7d7wm3860) (Sci Data 2026), recognition crops | CC0 | training (labels: the nearest digit per wheel) |
 | Checked readings exported from VisionState and shared in issues [#32](https://github.com/oleost/VisionState/issues/32) and [#40](https://github.com/oleost/VisionState/issues/40) | CC0 (the export's LICENSE.txt) | training; `wheels-v1` was evaluated with #40 0001–0017 held out, then trained on all of it |
 
-Results (whole reading exact, last digit rounded; see `docs/SCOPE.md`): the #40 water meter
-held out 17/17 (PP-OCRv6 small 15/17), two meters never seen 30–33/35 and 15–18/18 with no value
-too high, the Dryad test set 97.7 % when the photo is the right way up.
+Results of `wheels-v1` (2026-10-09; whole reading exact, last digit rounded):
+
+| Set | Wheel reader | PP-OCRv6 small | PP-OCRv6 tiny |
+|---|---|---|---|
+| #40 water meter with red wheels (ESP32 camera), 0001–0017 held out | 17/17 | 15/17 | 8/17 (5 too high) |
+| Two water meters never seen in training (test only, GPL images kept locally) | 30–33/35 and 15–18/18, none too high | 30/35 (2 too high), 15/18 (1 too high) | — |
+| Dryad test set (2,400 photos), right way up | 97.7 % | — | — |
+
+Trained without the #40 images it read them just as well. The remaining misses are ±1 on a
+half-turned last wheel. Known weaknesses and what comes next are in the plan below.
 
 ## Making it again
 
diff --git a/visionstate/DOCS.md b/visionstate/DOCS.md
index c8a9a82..415adb3 100644
--- a/visionstate/DOCS.md
+++ b/visionstate/DOCS.md
@@ -74,7 +74,7 @@ away. Sensors not sent are marked on the dashboard.
 
 ## Object sensors
 
-An object sensor finds common objects — people, cars, bicycles, cats, dogs, birds and 70 more —
+An object sensor finds common objects — people, cars, bicycles, cats, dogs, birds and 74 more —
 with a pretrained detector. There is nothing to label: pick the objects in the wizard (popular
 ones first, all others under **Show all**) and the wizard tests it on a fresh frame right away.
 Where it gets your camera wrong, you can correct it later (see *Teaching an object sensor*).
@@ -198,10 +198,9 @@ First choose **what it looks like**:
   last wheel turning: the value stays, but it is not counted as rejected and does not go to the
   review queue (the Live tab says *Last wheel turning*). If the last wheel never stands still,
   you can also leave it out of the region and count one digit and one decimal less.
-  On a real water meter with red wheels (an ESP32 camera with its flash) the wheel reader read
-  all 17 checked images right; the text reader read 15 with PP-OCRv6 small and 8 with the
-  included tiny reader. New counters use the wheel reader; counters set up before it came keep the
-  text reader until you choose *Wheel reader* on their Settings tab (their Live tab suggests it).
+  The wheel reader reads mechanical counters better than the text reader on every meter it was
+  tried on. New counters use it; counters set up before it came keep the text reader until you
+  choose *Wheel reader* on their Settings tab (their Live tab suggests it).
 - **Safety net**: a reading is rejected — and the last value kept — when the reader is less sure
   than the minimum (default 70 %), finds no number, a counter reads lower than before, a
   mechanical counter is read with the wrong number of digits, or the value changes more than the
@@ -248,10 +247,9 @@ First choose **what it looks like**:
   has been tried on three kinds of real water meter and a public set of 2,400 meter photos;
   other meters may need a tighter region. Small pointer dials (the red hands on some water
   meters) are not read.
-- Reading sensors are **new** and have hardly been tried on real cameras yet. Feedback helps a
-  lot: what the display or meter is, whether it read correctly, and a screenshot of *What the reader
-  sees* — in [GitHub Discussions](https://github.com/oleost/VisionState/discussions) or as an
-  issue.
+- Feedback on reading helps a lot, above all from meters and displays it has not seen yet: what
+  the display or meter is, whether it read correctly, and a screenshot of *What the reader sees* —
+  or an export (above) — in [GitHub Discussions](https://github.com/oleost/VisionState/discussions).
 - The default check interval is 30 s; a trigger (for example a motion sensor or image change
   detection) makes it read right away.
 
diff --git a/visionstate/README.md b/visionstate/README.md
index f3647b4..7f0a9bd 100644
--- a/visionstate/README.md
+++ b/visionstate/README.md
@@ -1,8 +1,8 @@
 # VisionState
 
-Teach a local AI what your camera sees — garage door open, closed or halfway, gate shut,
-car in the driveway — and use it as a Home Assistant sensor. Label a few examples by clicking,
-and the sensor learns in seconds. Runs 100 % locally on any CPU.
+Teach a local AI what your camera sees — garage door open, closed or halfway, gate shut — and
+use it as a Home Assistant sensor. Find people, cars and animals without any training, or read the
+number on a power, water or gas meter. Runs 100 % locally on any CPU.
 
 ![VisionState dashboard](https://raw.githubusercontent.com/oleost/VisionState/main/docs/promo/dashboard.png)
 
diff --git a/visionstate/backend/tests/test_docs.py b/visionstate/backend/tests/test_docs.py
new file mode 100644
index 0000000..6af736f
--- /dev/null
+++ b/visionstate/backend/tests/test_docs.py
@@ -0,0 +1,155 @@
+"""The docs stay true to the code: every link, repository path and code name they mention exists.
+
+Runs on the repository checkout (CI); skipped where the docs are not around (the app image).
+A failure here means a rename or a removal left a doc behind: fix the doc in the same change.
+"""
+
+import re
+from pathlib import Path
+
+import pytest
+
+from visionstate import settings
+
+REPO = Path(__file__).resolve().parents[3]
+BACKEND = REPO / "visionstate" / "backend" / "visionstate"
+FRONTEND = REPO / "visionstate" / "frontend"
+
+pytestmark = pytest.mark.skipif(not (REPO / "CLAUDE.md").exists(), reason="no repository checkout")
+
+# The CHANGELOG is history: it describes each version as it was.
+DOCS = [
+    *REPO.glob("*.md"),
+    *(REPO / "docs").glob("*.md"),
+    REPO / "visionstate" / "DOCS.md",
+    REPO / "visionstate" / "README.md",
+    REPO / "tools" / "wheelreader" / "README.md",
+    REPO / "visionstate" / "backend" / "tests" / "assets" / "README.md",
+]
+# Where a path in a doc may start from.
+BASES = [
+    REPO,
+    REPO / "visionstate",
+    REPO / "visionstate" / "backend",
+    BACKEND,
+    FRONTEND,
+    FRONTEND / "src",
+    FRONTEND / "src" / "lib",
+]
+SOURCE_SUFFIXES = {".py", ".ts", ".svelte", ".md", ".yml", ".yaml", ".sh", ".css", ".toml"}
+TOP_DIRS = {"visionstate", "docs", "scripts", "tools", ".github", "backend", "frontend", "e2e", "tests", "lib",
+            "pages", "engine", "api"}  # fmt: skip
+# Paths that are made at run time, not kept in the repository.
+NOT_IN_REPO = (".venv", "dist", "node_modules", "test-results", "dev/", "VisionStateLocal", "models")
+FILE_WORDS = {"json", "py", "md", "ts", "yml", "yaml", "txt", "zip", "jpg", "png", "onnx", "db", "svelte", "css",
+              "sh", "pt", "spec"}  # fmt: skip
+SKIP_TREES = ("node_modules", ".venv", "dist", "test-results", "__pycache__", ".git")
+
+
+def _files() -> list[Path]:
+    return [p for p in REPO.rglob("*") if p.is_file() and not any(part in SKIP_TREES for part in p.parts)]
+
+
+FILES = _files()
+NAMES = {p.name for p in FILES}
+MODULES = {p.stem: p for p in BACKEND.rglob("*.py") if "__pycache__" not in p.parts and p.stem != "__init__"}
+BACKEND_TEXT = "\n".join(p.read_text(encoding="utf-8") for p in MODULES.values())
+# Tables whose columns a doc may name (`prediction.read_ok`); "sensor" is also a Home Assistant domain.
+TABLES = set(re.findall(r'__tablename__ = "(\w+)"', (BACKEND / "db.py").read_text(encoding="utf-8"))) - {"sensor"}
+# A plan names code that is still to be written; only its links and paths are checked.
+PLANS = {REPO / "docs" / "WHEEL_READER_PLAN.md"}
+
+
+def _prose(doc: Path) -> str:
+    """The doc without fenced code blocks (commands and examples, not references)."""
+    return re.sub(r"^```.*?^```", "", doc.read_text(encoding="utf-8"), flags=re.M | re.S)
+
+
+def _defines(text: str, name: str) -> bool:
+    n = re.escape(name)
+    return re.search(rf"(def|class)\s+{n}\b|^\s*{n}\s*[:=]|self\.{n}\s*[:=]|^\s*{n}\s*$", text, re.M) is not None
+
+
+def _settings_dicts() -> dict[str, dict]:
+    return {k: v for k, v in vars(settings).items() if k.isupper() and isinstance(v, dict)}
+
+
+def _problem(token: str, doc: Path) -> str | None:
+    """Why `token` (inline code in `doc`) does not match the repository, or None."""
+    if any(c in token for c in " <>{}*…?#:,|$=") or token.startswith(("/", "http", "-")):
+        return None
+    # settings.X["key"] / X["key"]
+    m = re.fullmatch(r'(?:settings\.)?([A-Z][A-Z0-9_]+)\["(\w+)"\]', token)
+    if m:
+        d = getattr(settings, m[1], None)
+        if not isinstance(d, dict):
+            return f"settings.{m[1]} is not a dict of settings" if token.startswith("settings.") else None
+        return None if m[2] in d else f'settings.{m[1]} has no "{m[2]}"'
+    # module.name, Class.name, table.column
+    m = re.fullmatch(r"([A-Za-z_]\w*)\.([A-Za-z_]\w*)(\(\))?", token)
+    if m and m[2] not in FILE_WORDS:
+        if doc in PLANS:
+            return None
+        first, name = m[1], m[2]
+        if first in MODULES:
+            text = MODULES[first].read_text(encoding="utf-8")
+            if _defines(text, name) or name.endswith("_"):
+                return None
+            return f"{first}.py does not define {name}"
+        if first[0].isupper() and re.search(rf"class {first}\b", BACKEND_TEXT):
+            return None if _defines(BACKEND_TEXT, name) else f"nothing defines {first}.{name}"
+        if first in TABLES:
+            return None if _defines((BACKEND / "db.py").read_text(encoding="utf-8"), name) else f"no column {token}"
+        return None
+    # paths
+    if not re.fullmatch(r"[\w.@-]+(/[\w.@-]+)*/?", token) or any(n in token for n in NOT_IN_REPO):
+        return None
+    path = token.rstrip("/")
+    has_dir = "/" in path
+    if has_dir and path.split("/")[0] not in TOP_DIRS and not path.startswith(".."):
+        return None
+    if not has_dir and Path(path).suffix not in SOURCE_SUFFIXES:
+        return None
+    if not has_dir:
+        return None if path in NAMES else f"no file named {path}"
+    for base in [doc.parent, *BASES]:
+        if (base / path).exists():
+            return None
+    return f"no such path {token}"
+
+
+def test_links_in_the_docs_lead_somewhere():
+    missing = []
+    for doc in DOCS:
+        for target in re.findall(r"\]\(([^)\s]+)\)", _prose(doc)):
+            if target.startswith(("http://", "https://", "mailto:", "#")):
+                continue
+            if not (doc.parent / target.split("#")[0]).exists():
+                missing.append(f"{doc.relative_to(REPO)}: {target}")
+    assert not missing, "links to nothing:\n" + "\n".join(missing)
+
+
+def test_code_named_in_the_docs_exists():
+    problems = []
+    for doc in DOCS:
+        for token in re.findall(r"`([^`\n]+)`", _prose(doc)):
+            why = _problem(token.strip(), doc)
+            if why:
+                problems.append(f"{doc.relative_to(REPO)}: `{token}` — {why}")
+    assert not problems, "the docs name things the code does not have:\n" + "\n".join(problems)
+
+
+def test_values_named_in_the_docs_match_settings():
+    """`some_setting` (0.88) in a doc must be the value in settings.py."""
+    values: dict[str, set] = {}
+    for d in _settings_dicts().values():
+        for k, v in d.items():
+            if isinstance(v, (int, float)) and not isinstance(v, bool):
+                values.setdefault(k, set()).add(float(v))
+    wrong = []
+    for doc in DOCS:
+        pattern = r'`(?:[A-Z_]+\[")?(\w+)(?:"\])?` \((\d+(?:\.\d+)?)[ ),;]'
+        for key, number in re.findall(pattern, _prose(doc)):
+            if len(values.get(key, ())) == 1 and float(number) not in values[key]:
+                wrong.append(f"{doc.relative_to(REPO)}: `{key}` ({number}) — settings.py says {values[key].pop():g}")
+    assert not wrong, "values in the docs differ from settings.py:\n" + "\n".join(wrong)

From 31a9600cc120e65db808130326fd6ca5e3ddcfae Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Sat, 10 Oct 2026 18:01:26 +0200
Subject: [PATCH 03/28] Docs: the maintainer commits straight to beta,
 contributors open a PR

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0126B6HCdwnvuVrRkMeMEXfo
---
 CLAUDE.md         | 5 +++--
 docs/RELEASING.md | 5 +++++
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/CLAUDE.md b/CLAUDE.md
index 47a6613..7a88613 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -8,8 +8,9 @@ and AI assistants.
 
 - **Language:** everything in the repository (code, comments, UI text, docs, commit messages) is
   written in English.
-- Public repository `github.com/oleost/VisionState`, Apache-2.0. Pull requests go to `beta`;
-  releases and branches: [`docs/RELEASING.md`](docs/RELEASING.md).
+- Public repository `github.com/oleost/VisionState`, Apache-2.0, owned by its maintainer (oleost).
+  The maintainer (and Claude working for them) commits straight to `beta`; everyone else opens a
+  pull request against `beta`. Branches and releases: [`docs/RELEASING.md`](docs/RELEASING.md).
 
 ## Where things are written down
 
diff --git a/docs/RELEASING.md b/docs/RELEASING.md
index 0ad5b57..ffb2081 100644
--- a/docs/RELEASING.md
+++ b/docs/RELEASING.md
@@ -15,6 +15,11 @@ Branches, channels, versions and the release steps. How to work on the code is i
 | Media folder | `/media/visionstate` | `/media/visionstate_beta` |
 | GitHub release | latest | pre-release |
 
+- **Changes go to `beta`.** The maintainer commits and pushes straight to `beta` (after the local
+  checks): config.yaml keeps its version, so Home Assistant users get nothing new until the next
+  tag, and a PR would only run CI twice. Contributors and Dependabot open a PR against `beta`. Edit
+  `visionstate/DOCS.md` together with the feature it describes: the app's Documentation tab may
+  show it from the repository before the feature is released.
 - **Never commit to `main` directly.** Documentation-only changes (Markdown, images, issue
   templates) may go to `main` through a PR without a beta; merge `main` back into `beta` afterwards.
 - `scripts/channel.py` is the only way to change the name, version and channel fields in

From 202e8de1b78cae7b684050f3ca3bb2f7a3e4306d Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Sat, 10 Oct 2026 18:45:22 +0200
Subject: [PATCH 04/28] Docs: research and plan for face recognition on object
 sensors

Models and their licences, privacy and the AI Act question, how a check
would flow, on-demand download, the user interface and pitfalls. Not built;
phase 2 (identities) waits for a legal assessment.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0126B6HCdwnvuVrRkMeMEXfo
---
 CLAUDE.md                     |   1 +
 docs/ARCHITECTURE.md          |   3 +-
 docs/FACE_RECOGNITION_PLAN.md | 430 ++++++++++++++++++++++++++++++++++
 docs/SCOPE.md                 |   3 +-
 4 files changed, 435 insertions(+), 2 deletions(-)
 create mode 100644 docs/FACE_RECOGNITION_PLAN.md

diff --git a/CLAUDE.md b/CLAUDE.md
index 7a88613..c7e3151 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -27,6 +27,7 @@ Every fact has **one** home; other files link to it instead of repeating it.
 | How to work: principles, checks, testing, pitfalls | this file |
 | The wheel reader model: data, licences, results, how to train it | [`tools/wheelreader/README.md`](tools/wheelreader/README.md) |
 | The plan for better counter readings, and its status | [`docs/WHEEL_READER_PLAN.md`](docs/WHEEL_READER_PLAN.md) |
+| Face recognition: models, licences, privacy and the plan (not built) | [`docs/FACE_RECOGNITION_PLAN.md`](docs/FACE_RECOGNITION_PLAN.md) |
 | What changed in a version | `visionstate/CHANGELOG.md` (written at release, never edited afterwards) |
 | Test image sources | `visionstate/backend/tests/assets/README.md` |
 
diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index de14a7b..66d4e2a 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -14,7 +14,8 @@ visionstate/            the Home Assistant app: config.yaml, Dockerfile, DOCS.md
 scripts/                channel.py (stable/beta config), fake_camera.py (test camera), CI helpers:
                         check_options.py, cpu_probe.py, upgrade_test.sh, discovery_ids.py
 tools/wheelreader/      training code of the wheel reader model (not part of the app image)
-docs/                   SCOPE.md, ARCHITECTURE.md, RELEASING.md, WHEEL_READER_PLAN.md, promo/ (README images)
+docs/                   SCOPE.md, ARCHITECTURE.md, RELEASING.md, WHEEL_READER_PLAN.md,
+                        FACE_RECOGNITION_PLAN.md, promo/ (README images)
 .github/                CI workflow, Dependabot, issue forms
 ```
 
diff --git a/docs/FACE_RECOGNITION_PLAN.md b/docs/FACE_RECOGNITION_PLAN.md
new file mode 100644
index 0000000..bc9d76e
--- /dev/null
+++ b/docs/FACE_RECOGNITION_PLAN.md
@@ -0,0 +1,430 @@
+# Face recognition: research and plan
+
+A plan, not yet built: recognising **who** a person is (`Ole`, `Kari`, *someone unknown*) when an
+object sensor sees a person. It applies to the `objects` sensor kind only. The general design is in
+[`SCOPE.md`](SCOPE.md), the code map in [`ARCHITECTURE.md`](ARCHITECTURE.md). Research done
+2026-10-10; licence statements were read from the model pages that day and must be checked again
+before a model is chosen (§4).
+
+## 1. The idea in one paragraph
+
+An object sensor that counts `person` can switch on **Recognise faces**. For every counted person
+box, VisionState looks for a face in the upper part of the box, aligns it, turns it into a face
+embedding and compares it with the faces the user taught for each person. A match makes the box
+that person. People are **own labels** under `person` (as "Our car" is under `car` today), so each
+one gets the binary sensor and count that own labels already get, and the teaching, review
+("Is this Kari?"), Quality tab and export work the way they already do for own labels. Only the
+embedding differs: a face model instead of DINOv2 on the whole box.
+
+## 2. Why a part of object sensors, not a new sensor kind
+
+- **The person detector already runs.** Face recognition is a second step on boxes the object
+  sensor already found, so it costs nothing while nobody is there. A separate kind would detect
+  people a second time.
+- **The model for it already exists.** Own labels (`sensor.objects["custom"]`, `{key, name,
+  parent}`), taught boxes (`sample` rows with `object_label`), matching with a threshold, a margin
+  and an *ask* band, keeping an answer while a box stays (`keep_iou`), the review queue and export
+  are all built (SCOPE §5, *Teaching object sensors*). Face recognition is another way to compute
+  the similarity for one parent class.
+- **Why DINOv2 is not enough for people.** DINOv2 on a whole person box mostly measures clothing,
+  posture and background: the same person in another jacket looks like someone else, and two
+  people in similar clothes look the same. A face model is trained to ignore exactly that. This is
+  measured in phase 0 (§6), not assumed: DINOv2 on the box is the baseline every face model must beat.
+
+## 3. How one check would flow
+
+1. The object check finds and counts person boxes as today.
+2. **Face search** (when the sensor has Recognise faces on, also before anyone is taught, so the
+   Live tab can show whether faces are usable, §9.2):
+   for at most N counted person boxes (most certain first, like `TEACH["max_checked"]`), crop the
+   upper part of the box with a margin from the full frame (not the scaled 640×640 detector input),
+   scale it up to the face detector's input and run the face detector. Keep the best face per
+   person box, with its 5 landmarks (eyes, nose, mouth corners). A face outside its person box is
+   ignored. A box that keeps a confident name (step 7) is not searched every check, only every few
+   checks, to save CPU while someone stands in view.
+3. **Quality gate:** skip faces that are too small (eye distance in pixels, a setting), too blurry
+   (variance of the Laplacian), turned too far (landmark symmetry) or with a low detector score.
+   Starting point for the minimum: the literature puts reliable identification at roughly 70–100
+   pixels between the eyes and calls it unreliable below about 40; phase 0 sets the default.
+   A skipped face is *no face*, never *unknown*: a back of a head says nothing about who it is.
+4. **Align:** a similarity transform from the 5 landmarks onto the recogniser's template
+   (112×112 for the ArcFace family) — a small NumPy least-squares fit (Umeyama) and Pillow's
+   affine transform, so no OpenCV dependency is added.
+5. **Embed** with the face recogniser (L2-normalised), cached per sample like other embeddings.
+6. **Compare** with each taught person: cosine similarity to the mean of the person's best 3
+   taught faces (not the single best: one mislabelled face would then match anything like it;
+   Frigate uses a trimmed mean for the same reason). The same rule as taught boxes: a match needs
+   the threshold *and* a margin over the next person; between the ask threshold and the match
+   threshold the box is *ask* and goes to the review queue; below it the face is *unknown*.
+   **The thresholds belong to the face model**, stored with its registry entry, not in `TEACH`:
+   DINOv2's 0.88 means nothing for a face model (OpenCV's SFace demo uses a cosine of 0.363 for
+   "same person").
+7. **Over time:** a person box that stays (`keep_iou`) keeps its name while its face, when seen,
+   stays close enough; a name is published after M consistent checks or one very certain one.
+   When a person appears, the existing burst triggers give extra checks, so a passing person is
+   seen several times and the clearest face counts.
+8. **Publish:** each person's own label turns on/off with the existing debounce and
+   `clear_after_s`. *Unknown person* turns on only when several usable faces of the same person box
+   matched nobody and that box never got a name — one bad frame of a family member must not raise
+   it (§11).
+
+What the face models cost is measured in phase 0; a person crop is small, so the face detector
+runs on about 160–320 px, not on the whole frame.
+
+## 4. Models found (licences as stated on 2026-10-10)
+
+The repository rule applies: weights in a registry are pinned by Hugging Face revision and SHA-256
+and tested on real inputs. The hard question for faces is the **licence of the weights and of the
+data they were trained on**: almost all public face models are MIT/Apache *code* with weights
+trained on research-only data sets. VisionState is Apache-2.0, so anyone may use it commercially;
+weights it ships must allow that too.
+
+### Face detectors
+
+| Model | Licence | Training data | Notes |
+|---|---|---|---|
+| **YuNet** (OpenCV Zoo) | MIT (the model directory) | WIDER FACE, whose Hugging Face card says CC BY-NC-ND 4.0 | Tiny (under 1 MB), 5 landmarks, ONNX; the 2026may variant has a dynamic input (multiples of 32). Best technical fit; the data question must be settled. |
+| **BlazeFace full-range** (MediaPipe) | Apache-2.0 (per a re-host; check Google's model card) | Google's own, per its model card (to read) | TFLite only; needs an ONNX conversion that we check against real photos. Made for faces up to about 5 m. |
+| SCRFD, RetinaFace (InsightFace) | Code MIT, **models non-commercial research only** | WIDER FACE | Not usable. |
+| YOLO face models (Ultralytics based) | AGPL-3.0 | WIDER FACE | Not usable in an Apache-2.0 app. |
+
+### Face recognisers
+
+| Model | Licence | Training data | Size / accuracy | Notes |
+|---|---|---|---|---|
+| **SFace** (OpenCV Zoo, MobileFaceNet with SFace loss) | Apache-2.0 (the model directory) | Not stated in the zoo; the paper trained on CASIA-WebFace, VGGFace2 and MS-Celeb-1M | 112×112, fp32 and int8 ONNX; the zoo reports 0.994 on its own evaluation | Made to pair with YuNet's 5 landmarks. Smallest step to a working pipeline; data provenance unclear. |
+| **EdgeFace** XXS / XS / S / Base (Idiap) | BSD-3-Clause code; weights listed as BSD-3 by a third party | WebFace260M subsets (4M, 12M), terms not found | 1.2–18 M parameters; LFW 99.57–99.83 %, CPLFW 90.3–93.8 % | Won the compact track of the IJCB 2023 efficient face recognition competition; ONNX ports exist. Best accuracy per FLOP. |
+| **AuraFace v1** (fal) | Apache-2.0 | "A commercial dataset" (not named) | The recogniser `glintr100.onnx` is 261 MB; LFW 0.9965, CPLFW 0.909 | The only one that says its data allows commercial use. Too big as a default on a Raspberry Pi; an optional download. Its repository also carries InsightFace's SCRFD detector, which is *not* covered by that. |
+| ArcFace (ONNX Model Zoo), FaceNet ports | Apache/MIT on the files | MS1M / VGGFace2 / CASIA (research terms; MS-Celeb-1M was withdrawn) | — | Same data problem as SFace, and larger. |
+| InsightFace packs (buffalo_l, antelopev2, …) | **Non-commercial research only** | — | — | Not usable. |
+
+**Prior art, Frigate** (its source, `dev` branch, 2026-10-10): faces are only looked for inside a
+`person` box. Detector: YuNet through OpenCV (`cv2.FaceDetectorYN`, a 100 kB file), plus OpenCV's
+LBF landmark model (56 MB) to align onto the ArcFace 5-point template. Recognisers: *small* =
+FaceNet (TFLite, 94 MB, CPU), *large* = an ArcFace ONNX of 261 MB (the size of the glintr100
+network that AuraFace uses; which weights it is was not checked) for GPU/NPU. All four files are
+downloaded on first use from one GitHub release, tagged Apache-2.0, that names no source or
+training data. Matching: one mean embedding per person (outliers below cosine 0.3 dropped, then a
+trimmed mean), cosine to it, mapped to a confidence by a sigmoid; a name needs several consistent
+recognitions, larger faces weigh more, blurry ones less. Its docs advise 20–30 varied photos per
+person and warn that bulk-adding similar ones overfits.
+
+**Leaning:** prototype with **YuNet + SFace** (both from OpenCV Zoo, made to fit together, tiny)
+and **EdgeFace-XS** as the second recogniser, with AuraFace as the accuracy reference. Before
+anything ships, a decision on the training-data question (§10, question 1). Sizes in the OpenCV
+Zoo: SFace 38.7 MB fp32 and 9.9 MB int8; YuNet well under 1 MB.
+
+## 5. Privacy and the law
+
+Not legal advice; the points to design for, and to say plainly in DOCS.md.
+
+- **Biometric data.** A face template used to identify someone is special-category data under
+  GDPR Art. 9. A private person's camera is only outside GDPR under the household exemption, and
+  the CJEU (*Ryneš*, C-212/13, 2014) held that a home camera that also covers public space (a
+  street, a neighbour's door) is not purely household use. Norway follows GDPR through the EEA.
+- **EU AI Act — the biggest open question of this plan.**
+  - A *user* is fine: the Act does not apply to the obligations of deployers who are natural
+    persons in a purely personal, non-professional activity (Art. 2(10)).
+  - The *software* may not be: a camera that names people without their active involvement, at a
+    distance, by comparing with stored faces fits the definition of a **remote biometric
+    identification system** (Art. 3(41), recital 17), and those are **high-risk** (Annex III,
+    point 1(a); only one-to-one *verification* — "is this the person they claim to be" — is
+    excluded). The exemption for free and open-source software (Art. 2(12)) does **not** cover
+    systems placed on the market or put into service as high-risk. Whether a free, non-monetised
+    GitHub project is "placed on the market" (a "commercial activity, whether for payment or free
+    of charge") is not settled.
+  - Timing: the Annex III obligations apply from 2 December 2027 after the "Digital Omnibus"
+    (Regulation (EU) 2026/1744, per secondary sources — check the Official Journal).
+  - The Art. 5 prohibition on *untargeted* scraping of facial images from the internet or CCTV to
+    build or expand recognition databases applies to everyone: VisionState must never grow a gallery
+    of strangers by itself.
+  - **Consequence for the plan:** phases 0 and 1 (finding faces, no identities) identify nobody
+    and are not affected. **Phase 2 waits for a written legal assessment** of whether VisionState
+    would be a high-risk system and what that would mean (or a design that is clearly outside it).
+- **Norway:** Datatilsynet's guidance for private cameras: your own house and garden only, not
+  public areas where people pass, and not a neighbour's property. The AI Act comes to Norway through
+  the EEA.
+- Elsewhere other rules apply (e.g. Illinois BIPA); the docs tell the user it is their
+  responsibility, as for any camera.
+
+Design rules that follow:
+
+1. **Off by default**, switched on per sensor with a short explanation of what is stored. The
+   people taught are the user's household and guests; the docs say they should know, children
+   included.
+2. **Only taught faces are a gallery, and only a human adds to it.** A recognised face is never
+   added to the person's faces by itself (that drifts: one wrong match teaches the next one).
+   Unknown faces are not stored as templates; they only live in history frames, under the normal
+   history limits (`history_days`, `history_max_gb`).
+3. **Faces are kept where only the app can read them.** Training images live in `/media`, which
+   Home Assistant's media browser shows to every user of the house; taught face crops go to the
+   app's own `/data` instead. That puts them in Home Assistant backups — say so in DOCS.md, and that
+   backups should be encrypted.
+4. **Embeddings are biometric data too.** Published attacks rebuild a recognisable face from an
+   ArcFace-style template, so the cached embeddings are deleted with the person and never exported
+   (an import computes them again).
+5. **Forgetting is complete:** deleting a person deletes their face samples, crops and cached
+   embeddings; switching the feature off offers to delete everything.
+6. **Export:** sensor bundles include taught faces only when the user ticks it; the readings export
+   is unaffected. No face ever leaves the machine otherwise (no telemetry, as always).
+7. **Not a security function.** No liveness check: a photo of a face is that face. DOCS.md says
+   not to unlock doors or disarm alarms on it alone.
+8. Infrared night images, masks, hats and sunglasses make recognition worse; the docs say so and
+   the Quality tab shows it (§9.5).
+
+## 6. Plan
+
+| Phase | What | Done when |
+|---|---|---|
+| **0. Spike** (local, `/VisionStateLocal/`) | Frames from real doorbell/driveway cameras of the household (with their consent), at the camera resolution Home Assistant actually delivers. Measure: how often a usable face is found in a person box per pass; detectors (YuNet, BlazeFace) on person crops; recognisers (SFace, EdgeFace-XS/S, AuraFace) against **DINOv2 on the box** as the baseline: true matches at a fixed false-match rate, the gap between same-person and other-person similarities, day vs IR. Time per face on a desktop CPU and a Raspberry Pi 4. Read the weights' and data's terms; ask the authors where unclear. | A detector and recogniser chosen with numbers, the minimum face size and thresholds proposed, and a written verdict on licences. Or a "not good enough at typical camera distances" verdict, which is a valid outcome. |
+| **1. Faces only** | A face registry (detectors and recognisers, pinned like the others), face search in person boxes, face boxes drawn in the Live tab, no identities. | Users can see whether their camera gives usable faces before teaching anything. |
+| **2. People** (after the legal assessment, §5) | Recognise faces on an object sensor; teach a face from the Live tab or history ("Who is this?" → a person, new or existing); matching, ask band, entities per person and *unknown person*; Forget person. | A person is recognised on a test with CC0 / public-domain photos (below) and on the spike data. |
+| **3. Quality and review** | Review questions ("Is this Kari?"), Quality tab per person (faces taught, similarity between people — look-alikes —, possibly mislabelled faces), history filter by person, import/export with the opt-in. | e2e tests on desktop, mobile and iPhone. |
+| **4. Later** | Teaching from uploaded photos of a person (face found in the photo); a Home Assistant *event* entity "person recognised" for automations; the face from several checks combined per visit. | — |
+
+**Test images.** Tests need several photos of the same real person that are CC0 or in the public
+domain. Works of the US federal government are public domain (e.g. NASA astronaut portraits: several
+photos per person); the sources go in `tests/assets/README.md` as usual. Personal photos of the
+household stay in `/VisionStateLocal/` and never enter the repository, a test or a model.
+
+## 7. Where it would go in the code
+
+Following the existing structure (names are proposals):
+
+- A face registry next to `detectors.json` and `readers.json` (a detector and a recogniser per
+  entry), and a face module next to `teach.py`: crop, detect, quality gate, align, embed.
+- Loading in `engine/models.py` (loaded on first use, released when no sensor uses it, as the
+  detector and reader are); the per-check step in `engine/objects.py` after counting, the taught
+  faces index next to the taught boxes in `engine/teaching.py`.
+- Settings: a face block in `settings.py` (minimum eye distance, blur limit, thresholds, max faces
+  per check), and `recognise_faces` in `settings.OBJECT_DEFAULTS`; all exposed through
+  `GET /api/v1/config`.
+- Data: taught faces are `sample` rows with `object_label` = the person's key, the face box and
+  landmarks, and a crop of the face **with a margin, not aligned** (in `/data`, §5): another
+  recogniser may need another template or size (FaceNet takes 160 px, the ArcFace family 112), so
+  changing the model must be able to align again from the stored crop. Their embeddings are cached
+  under their own `roi_key`. A schema migration only if the box and landmarks do not fit the
+  existing columns.
+- Entities: each person is an own label, so its binary sensor and count already exist; *unknown
+  person* and the later event entity are new, with new unique IDs that never change.
+
+## 8. The model is downloaded only when someone turns it on
+
+Nothing about faces is in the app image, and nothing is downloaded or loaded until a user turns on
+**Recognise faces** for a sensor and confirms it. This is the mechanism the optional models already
+use, extended by one step:
+
+- **Today:** a registry entry with `"bundled": false` is fetched by `backbones.download` (to
+  `/data/models`, kept out of Home Assistant backups, checked against its SHA-256, refused when it
+  does not match) when it is chosen under **Settings → AI model**, where the choice reads
+  "… · download 21 MB". The detector and the reader are loaded on first use (`ensure_detector`,
+  `ensure_reader`), and the status rows say "loaded when …" until then.
+- **For faces:** the face registry's entries are never bundled. Turning faces on for the first
+  sensor downloads the chosen face model (detector and recogniser) and loads it; turning off the
+  last sensor that uses it releases it from memory. The file stays in `/data/models`, so turning it
+  on again is instant and needs no network; **Settings → AI model** can delete it while no sensor
+  uses it (a new button; the other models have no such need, they are small or bundled).
+- **While it downloads** the sensor keeps working as an object sensor without faces. Today a
+  download shows only "Switching (may download)…"; the face model is larger, so the status reports
+  progress (bytes so far / size), and the sensor shows it (§9).
+- **When it fails** (no internet, a checksum that does not match): the sensor's face card shows the
+  error and **Try again**; the status row *Face model* shows it in red, as the other models do.
+  Without internet the user can put the file in `/data/models` by hand; the error names the file
+  and its source.
+- **Import:** a sensor imported with taught faces gets the faces but not the feature: face
+  recognition stays off until the user turns it on there (with the same confirmation and download).
+
+## 9. User interface
+
+Built from the pieces object sensors already have, so nothing is new to learn: the teach sheet
+(tap a box), *Is this …?* in the review queue, the Quality tab that appears once something was
+taught, the *What you taught* card on the Settings tab, own labels with their entities, and
+**Settings → AI model**. Everything works by tap (phones and tablets first, no hover or keyboard
+needed), and all texts below are proposals. Model names and sizes in them are examples until phase 0
+has chosen the model.
+
+### 9.1 Where it is switched on
+
+- **Not in the new-sensor wizard.** The wizard stays short; when `person` is among the chosen
+  objects, one line under it says *"Later you can teach it to recognise people by their face
+  (Settings tab)."* Turning on a biometric feature deserves its own decision, not a step in a flow.
+- **Sensor → Settings tab, card *Faces*** — only on object sensors that look for `person`
+  (otherwise the card says *"Add Person to the objects to recognise faces."*). While off:
+
+  > **Recognise people by their face**
+  > Name the people you know; each gets an on/off sensor in Home Assistant.
+  > [ Turn on… ]
+
+- **Turn on…** opens a sheet (the teach sheet's layout: a bottom sheet on phones, a dialog on
+  desktop) that says what happens before anything is downloaded:
+
+  > **Recognise faces on *Front door*?**
+  > - Faces are looked for only in people this sensor finds, and compared with faces **you** name.
+  > - Stored on this Home Assistant only, in the app's own storage (not the media folder): a small
+  >   picture of each face you teach. Faces of people you have not named are not kept, apart from
+  >   the normal history frames. The people you teach should know about it.
+  > - Needs the face model *YuNet + SFace* (Apache-2.0, 38 MB), downloaded once from Hugging Face.
+  > - Cameras that see a street or a neighbour's door may need their consent where you live.
+  >   *Read more* (DOCS.md).
+  > - It is not a lock: a photo of a face looks like that face.
+  >
+  > [ Download and turn on ]  [ Cancel ]
+
+  The model name, licence, size and source come from the registry through `GET /api/v1/config`,
+  never from the UI. When the model is already downloaded the button is just **Turn on**.
+- **After confirming** the card shows the download (*"Downloading the face model… 12 of 38 MB"*,
+  a thin progress bar) and then the card's normal state (§9.6). Leaving the page does not stop it.
+
+### 9.2 Seeing faces before teaching anyone
+
+The first question a user has is *does my camera see faces well enough?* — answer it before
+asking them to teach.
+
+- **Live tab:** inside each person box a small face box is drawn (rounded, in the person's colour)
+  when a face was found. A person box without a usable face gets a quiet tag in the *Right now*
+  list: *"Person · face too small"*, *"… · face turned away"*, *"… · face blurry"*, *"… · no face
+  seen"* — the quality gate's reasons, in words.
+- **Faces card** (Settings tab) and the top of the Quality tab: *"A usable face in 9 of the last
+  40 people seen."* When that share is low, a hint names the likely fix: a camera closer to where
+  faces are, a higher resolution (an RTSP source instead of the snapshot), more light.
+
+### 9.3 Teaching a person
+
+- **Tap a person box** (Live tab or a history frame) → the teach sheet. With faces on, its
+  close-up shows **the face**, not the whole body (on a phone the face is a few pixels in the
+  frame), and a section comes first:
+
+  > **Who is this?**
+  > [ Kari ] [ Ole ] [ + New person… ]
+  > Not someone you know? Close this.
+
+  The existing choices (*Not a person*, *Something else…*) stay below it.
+- **No usable face in the box:** the section says *"No clear face here — pick a moment where the
+  face is visible"* and offers no names: a person is only ever taught from a face. That keeps a back
+  or a jacket out of someone's faces.
+- **New person…** asks for a name (as **New label…** does) and explains: *"Gets an on/off sensor
+  and a count in Home Assistant."* People are own labels under `person`, marked as taught by face,
+  so the existing own labels made from whole boxes keep working beside them.
+- **After teaching** a toast says what it learned and what helps: *"Got it: this is Kari (1 face).
+  A few more, in other light and angles, make it reliable."* Up to the number the docs recommend,
+  each teach says how many faces the person has.
+- **More than one person in a frame:** each box is taught on its own, as today.
+
+### 9.4 When it is not sure: the review queue
+
+- **"Is this Kari?"** — the same review card as own labels, but with the **face close-up beside the
+  frame** (stacked on phones) and, beside it, one of Kari's taught faces to compare with. The card
+  of own labels says *"It looks 82 % like …"*; a face model's cosine (OpenCV's SFace demo already
+  calls 0.363 the same person) would read as nonsense there, so faces say *"probably"* / *"not
+  sure"* instead of a number. Answers: *Yes, Kari* · *No, it is …* (chips of the other people and
+  **New person…**) · *Skip*. A *yes* or *no* teaches that face.
+- **"Who is this?"** for faces that match nobody is **off by default** (the Faces card: *Ask me
+  about faces I do not know*): it keeps frames of strangers longer (review frames get twice the
+  history time), so it is the user's choice. Answers: the people chips, **New person…**,
+  **Don't know** (removes the item, teaches nothing).
+- Limits as today: one waiting question per person, a cooldown, none with review off.
+
+### 9.5 The Quality tab: People
+
+A section **People** above *What you taught* (the tab appears once a person is taught, as now):
+
+- One card per person: name, the number of faces and a strip of the taught face crops (tap one to
+  see the frame it came from; **×** forgets that face), *last seen 5 min ago*, and a simple meter —
+  *1–4 faces: "teach a few more"*, enough: none.
+- **Look-alikes:** when two people's faces come close (the closest pair of taught faces above a
+  setting), a notice on both cards: *"Kari and Eva look alike to the AI — teach both in more
+  light and angles."*
+- **Possibly mislabelled:** a taught face that is closer to another person than to its own gets a
+  badge (as mislabelled samples do on state sensors), with *Move to Eva* and *Forget*.
+- **Remove person** (tap again to confirm): their faces, their entities in Home Assistant, gone.
+
+### 9.6 The Faces card when on (sensor Settings tab)
+
+- **Recognise faces** (on/off). Off keeps what was taught for later, as **Use what you taught**
+  does; the entities of people stay but report off.
+- **Ask me about faces I do not know** (off by default, §9.4).
+- **Smallest face** — a slider like *Smallest object*, in pixels between the eyes, defaulting from
+  the face settings in `settings.py` (the value is never in the UI).
+- **Forget all faces** (tap again to confirm): every taught face and person, their entities, and
+  the cached face embeddings. **Forget all** of *What you taught* forgets faces too.
+- The model line: *"YuNet + SFace · 38 MB · change under Settings → AI model."*
+
+### 9.7 Settings → AI model and Status
+
+- A field **Faces (object sensors)** with the face models (*"… · download 38 MB"* while not
+  downloaded), the licence and source as for the detector, and *"Downloaded when a sensor turns on
+  face recognition."* Changing it re-embeds every taught face (as a new state backbone retrains),
+  so the confirmation says so; people are kept.
+- A status row **Face model**: *loaded when a sensor recognises faces* / its name / the download
+  progress / the error in red.
+- **Delete downloaded file** next to it while no sensor uses faces.
+
+### 9.8 Home Assistant, history and export
+
+- **Entities:** each person is an on/off sensor and a count (an own label); **Unknown person** is
+  one more on/off sensor per sensor, made when faces are turned on. The *In Home Assistant* card on
+  the Live tab lists them, as it does for classes.
+- **History:** people are in the *What* filter like own labels; a row of a person shows the face
+  close-up.
+- **Export:** for a sensor with taught faces, **Export** asks first: *Include the taught faces (N)*
+  — unticked by default. Without them the bundle carries the people (names) but no faces.
+
+### 9.9 Testing the UI
+
+As every UI change (CLAUDE.md, *UI testing routine*): e2e tests on desktop, mobile and iPhone for
+the turn-on sheet (with a face model served by the test, never a real download in CI), teaching a
+face from the Live tab, the review card, the People section and forgetting; and by hand at desktop
+width and ~390 px. The fake camera would need a frame with faces: public-domain photos (§6).
+
+## 10. Open questions for the maintainer
+
+0. **The AI Act (§5).** Get a written legal assessment before phase 2: is VisionState with face
+   recognition a high-risk remote biometric identification system, is a free GitHub project
+   "placed on the market", and what would follow? Phases 0–1 do not depend on it.
+
+1. **Licence bar.** Ship only weights whose training data allows any use (today: AuraFace, and
+   perhaps BlazeFace), or accept models like YuNet/SFace whose code licence is permissive but
+   whose training data is research-only? A middle way: never bundle such a model, download it on
+   demand after the user accepts its terms (as the optional models are downloaded today).
+2. **Default target:** must face recognition run on a Raspberry Pi 4, or is it fine as
+   "desktop/NUC recommended"?
+3. **Camera resolution:** Home Assistant's camera proxy often gives a reduced snapshot; does a
+   doorbell at 2–4 m give enough pixels per face? Phase 0 answers it; maybe an RTSP source per
+   sensor becomes the recommendation for faces.
+4. **Unknown person:** only an entity, or also a review item ("Who is this?") — which stores the
+   frame longer under review retention?
+5. **Where to switch it on:** only on the sensor's Settings tab after a confirmation (§9.1), or
+   also in the new-sensor wizard? The plan keeps it out of the wizard.
+
+## 11. Pitfalls to avoid
+
+Collected from the research, from how others built it, and from VisionState's own history.
+
+- **Every check is another chance of a false match.** A false-match rate per comparison is
+  multiplied by the checks per visit (bursts!) and by the number of people taught. So a name needs
+  agreement across checks (§3 step 7), the margin over the next person matters as much as the
+  threshold, and thresholds are measured at a false-match rate per *visit*, not per image.
+- **"Unknown" from a bad face.** Without the quality gate every family member seen from the side
+  becomes *unknown person* — a false alarm in an automation. A face that fails the gate is no face.
+- **Learning from its own answers.** Adding recognised faces to the gallery automatically drifts
+  towards wrong people (the wheel reader plan's rule: only human answers are labels). Frigate's
+  docs also warn that bulk-adding similar faces overfits.
+- **One threshold for all models.** Similarities of different models are not comparable; each
+  model's thresholds live with its registry entry and are measured on our data.
+- **Faces that are not people at the door:** a TV, a poster or a framed photo in view. The region
+  (ROI) and the person detector's size limits keep most out; the docs mention it.
+- **Children, and time.** Recognition is weaker for children, and their faces change quickly. The
+  Quality tab shows how old each person's taught faces are and suggests adding recent ones.
+- **Night and infrared.** Greyscale IR frames match colour faces worse. Measure it in phase 0; if
+  needed, keep separate day and night faces per person (the history already knows a greyscale
+  frame).
+- **Unequal accuracy across groups of people.** Public models are less accurate for some skin
+  tones and ages; the docs say so instead of promising one accuracy.
+- **A low-resolution snapshot.** The camera proxy may scale the frame down; then no face is ever
+  usable. The Live tab's face reasons (§9.2) make that visible instead of failing silently.
+- **Licence by repository label.** A repository tagged Apache-2.0 says nothing about the data
+  behind the weights (Frigate's model release names none). Read the model card and the data's
+  terms before a model enters the registry.
+- **Polling while a face model loads.** A large model loaded in the event loop would freeze the
+  UI; load it in a worker thread like the others, and never let a failed download stop the
+  sensor's loop (CLAUDE.md: long-running loops survive any exception).
diff --git a/docs/SCOPE.md b/docs/SCOPE.md
index 4c8d9b3..f4d891a 100644
--- a/docs/SCOPE.md
+++ b/docs/SCOPE.md
@@ -530,5 +530,6 @@ Assistant; mechanical counters beyond [`WHEEL_READER_PLAN.md`](WHEEL_READER_PLAN
 dials and gauges; several readings per sensor (a sign with four prices, a
 counter plus its dials); per-sensor model
 choice with unloading of idle models (DINOv2 stays loaded: object sensors that were taught use
-it); zones and line crossing for object sensors; classes outside COCO (an open-vocabulary
+it); zones and line crossing for object sensors; recognising people by their face on object sensors
+(research, models, privacy and a phased plan: [`FACE_RECOGNITION_PLAN.md`](FACE_RECOGNITION_PLAN.md)); classes outside COCO (an open-vocabulary
 detector) — until then, a state sensor covers many of them ("parcel on the doorstep").

From 5ae933493b7bd4298905f29c9a6d1f38ca3a942a Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Sat, 10 Oct 2026 18:51:58 +0200
Subject: [PATCH 05/28] Docs: face recognition plan reads the AI Act with the
 Commission's guidelines

Home use is outside the Act (the guidelines' own example); a free,
non-monetised project is not placed on the market, so phase 2 no longer
waits for a legal assessment.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0126B6HCdwnvuVrRkMeMEXfo
---
 docs/FACE_RECOGNITION_PLAN.md | 59 ++++++++++++++++++++++-------------
 1 file changed, 37 insertions(+), 22 deletions(-)

diff --git a/docs/FACE_RECOGNITION_PLAN.md b/docs/FACE_RECOGNITION_PLAN.md
index bc9d76e..50180bc 100644
--- a/docs/FACE_RECOGNITION_PLAN.md
+++ b/docs/FACE_RECOGNITION_PLAN.md
@@ -122,25 +122,44 @@ Not legal advice; the points to design for, and to say plainly in DOCS.md.
   GDPR Art. 9. A private person's camera is only outside GDPR under the household exemption, and
   the CJEU (*Ryneš*, C-212/13, 2014) held that a home camera that also covers public space (a
   street, a neighbour's door) is not purely household use. Norway follows GDPR through the EEA.
-- **EU AI Act — the biggest open question of this plan.**
-  - A *user* is fine: the Act does not apply to the obligations of deployers who are natural
-    persons in a purely personal, non-professional activity (Art. 2(10)).
-  - The *software* may not be: a camera that names people without their active involvement, at a
-    distance, by comparing with stored faces fits the definition of a **remote biometric
-    identification system** (Art. 3(41), recital 17), and those are **high-risk** (Annex III,
-    point 1(a); only one-to-one *verification* — "is this the person they claim to be" — is
-    excluded). The exemption for free and open-source software (Art. 2(12)) does **not** cover
-    systems placed on the market or put into service as high-risk. Whether a free, non-monetised
-    GitHub project is "placed on the market" (a "commercial activity, whether for payment or free
-    of charge") is not settled.
+- **EU AI Act.** Read in the Act and in the Commission's *Guidelines on prohibited AI practices*
+  (C(2025) 884, 2025; non-binding but the Commission's reading):
+  - **The user is outside the Act.** It does not apply to the obligations of deployers who are
+    natural persons in a purely personal, non-professional activity (Art. 2(10)). The guidelines
+    give almost exactly our case as the example: *"an individual using a facial recognition system
+    at home (e.g. to control access and to monitor for safety the entrance to the home) would fall
+    under the exclusion"* (para. 34).
+  - **The software itself stays in scope** for whoever provides it (para. 35: a system used for
+    personal purposes "remains a high-risk AI system as classified in Article 6"). Recognising
+    household members on a general camera, who do not step "actively and consciously in front of
+    a camera installed in a way fostering active participation" (para. 306), fits *remote
+    biometric identification* (Art. 3(41)), which is high-risk (Annex III, point 1(a)). One-to-one
+    *verification* and access control to premises are not "remote" (para. 305) — a doorbell that
+    recognises the person ringing is closer to that, but VisionState does not limit itself to it.
+  - **But the Act binds providers who place a system on the market or put it into service**, and
+    "making available on the market" means supply "in the course of a commercial activity, whether
+    in return for payment or free of charge" (Art. 3(10)). Recital 103 says making components
+    available through open repositories is not in itself monetisation; the Commission's guidance
+    for the Cyber Resilience Act, built on the same product-law terms, says sharing free and open
+    source code on a public repository is not placing it on the market. VisionState is free, has no
+    paid tier, no paid support and no telemetry. On that reading it is not placed on the market at
+    all, so the high-risk duties do not reach it; the open-source exemption of Art. 2(12) is then
+    not even needed (it excludes high-risk systems).
+  - **What would change it:** monetising VisionState (a paid version, paid support, a company
+    selling it or bundling it in a product). Then face recognition would need the high-risk route
+    or must be left out.
   - Timing: the Annex III obligations apply from 2 December 2027 after the "Digital Omnibus"
-    (Regulation (EU) 2026/1744, per secondary sources — check the Official Journal).
+    (Regulation (EU) 2026/1744, per secondary sources). The Commission's draft guidelines on
+    high-risk classification (May 2026) are to be checked once final.
   - The Art. 5 prohibition on *untargeted* scraping of facial images from the internet or CCTV to
-    build or expand recognition databases applies to everyone: VisionState must never grow a gallery
-    of strangers by itself.
-  - **Consequence for the plan:** phases 0 and 1 (finding faces, no identities) identify nobody
-    and are not affected. **Phase 2 waits for a written legal assessment** of whether VisionState
-    would be a high-risk system and what that would mean (or a design that is clearly outside it).
+    build or expand recognition databases applies to everyone: VisionState must never grow a
+    gallery of strangers by itself.
+  - Others ship the same feature today (Frigate, commercial home cameras); that shows practice, not
+    what applies from December 2027 — Frigate's maker also sells a subscription, VisionState sells
+    nothing.
+  - **Verdict for the plan (not legal advice):** low risk for a free, non-monetised project;
+    building phases 0–2 is fine (the maintainer's decision, 2026-10-10). Before face recognition
+    reaches the stable channel, re-read the final high-risk guidelines.
 - **Norway:** Datatilsynet's guidance for private cameras: your own house and garden only, not
   public areas where people pass, and not a neighbour's property. The AI Act comes to Norway through
   the EEA.
@@ -178,7 +197,7 @@ Design rules that follow:
 |---|---|---|
 | **0. Spike** (local, `/VisionStateLocal/`) | Frames from real doorbell/driveway cameras of the household (with their consent), at the camera resolution Home Assistant actually delivers. Measure: how often a usable face is found in a person box per pass; detectors (YuNet, BlazeFace) on person crops; recognisers (SFace, EdgeFace-XS/S, AuraFace) against **DINOv2 on the box** as the baseline: true matches at a fixed false-match rate, the gap between same-person and other-person similarities, day vs IR. Time per face on a desktop CPU and a Raspberry Pi 4. Read the weights' and data's terms; ask the authors where unclear. | A detector and recogniser chosen with numbers, the minimum face size and thresholds proposed, and a written verdict on licences. Or a "not good enough at typical camera distances" verdict, which is a valid outcome. |
 | **1. Faces only** | A face registry (detectors and recognisers, pinned like the others), face search in person boxes, face boxes drawn in the Live tab, no identities. | Users can see whether their camera gives usable faces before teaching anything. |
-| **2. People** (after the legal assessment, §5) | Recognise faces on an object sensor; teach a face from the Live tab or history ("Who is this?" → a person, new or existing); matching, ask band, entities per person and *unknown person*; Forget person. | A person is recognised on a test with CC0 / public-domain photos (below) and on the spike data. |
+| **2. People** | Recognise faces on an object sensor; teach a face from the Live tab or history ("Who is this?" → a person, new or existing); matching, ask band, entities per person and *unknown person*; Forget person. | A person is recognised on a test with CC0 / public-domain photos (below) and on the spike data. |
 | **3. Quality and review** | Review questions ("Is this Kari?"), Quality tab per person (faces taught, similarity between people — look-alikes —, possibly mislabelled faces), history filter by person, import/export with the opt-in. | e2e tests on desktop, mobile and iPhone. |
 | **4. Later** | Teaching from uploaded photos of a person (face found in the photo); a Home Assistant *event* entity "person recognised" for automations; the face from several checks combined per visit. | — |
 
@@ -378,10 +397,6 @@ width and ~390 px. The fake camera would need a frame with faces: public-domain
 
 ## 10. Open questions for the maintainer
 
-0. **The AI Act (§5).** Get a written legal assessment before phase 2: is VisionState with face
-   recognition a high-risk remote biometric identification system, is a free GitHub project
-   "placed on the market", and what would follow? Phases 0–1 do not depend on it.
-
 1. **Licence bar.** Ship only weights whose training data allows any use (today: AuraFace, and
    perhaps BlazeFace), or accept models like YuNet/SFace whose code licence is permissive but
    whose training data is research-only? A middle way: never bundle such a model, download it on

From b7b2dfa28d068e06012929b8a325d034accf663e Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Sun, 11 Oct 2026 02:51:16 +0200
Subject: [PATCH 06/28] 0.8.1b1: larger AI models, a model comparison and
 tunable teaching limits

- D-FINE M and L (object sensors) and DINOv2 base fp32 (state sensors),
  downloaded on first use, pinned by revision and SHA-256. Measured on the
  CC0 test photos: same objects found at the 60 % threshold; DINOv2 base
  separates taught objects best (its 8-bit and fp16 variants were worse or
  heavier and are not offered). PP-OCRv6 medium read worse than tiny and
  small on drawn displays and was left out.
- Every registry entry has a measured footprint (memory, time per check,
  accuracy rank), shown under Settings -> AI model -> Compare the models.
- Teaching limits (match, keep, ask, rescue similarity and rescue floor) can
  be set globally and per object sensor; empty fields inherit. Defaults per
  backbone (settings.TEACH_BY_BACKBONE); export/import carry a sensor's own.
- tests/test_models.py checks every model on real inputs; CI downloads all
  models (backbones models --all).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .github/workflows/ci.yml                      |   6 +-
 CLAUDE.md                                     |   5 +-
 docs/ARCHITECTURE.md                          |   8 +-
 docs/SCOPE.md                                 |  38 +++++-
 visionstate/CHANGELOG.md                      |  20 ++++
 visionstate/DOCS.md                           |  25 +++-
 visionstate/backend/tests/test_models.py      | 108 +++++++++++++++++
 visionstate/backend/tests/test_teach.py       |  80 +++++++++++++
 visionstate/backend/visionstate/api/common.py |  32 +++++
 .../backend/visionstate/api/sensors.py        |   8 +-
 visionstate/backend/visionstate/api/system.py | 108 +++++++++--------
 .../backend/visionstate/backbones.json        |  29 ++++-
 visionstate/backend/visionstate/backbones.py  |  39 +++++-
 .../backend/visionstate/detectors.json        |  40 ++++++-
 visionstate/backend/visionstate/detectors.py  |   6 +-
 .../backend/visionstate/engine/base.py        |  12 +-
 .../backend/visionstate/engine/objects.py     |   2 +-
 .../backend/visionstate/engine/runtime.py     |   6 +
 .../backend/visionstate/engine/teaching.py    |  11 +-
 visionstate/backend/visionstate/readers.json  |  15 ++-
 visionstate/backend/visionstate/readers.py    |   8 +-
 visionstate/backend/visionstate/settings.py   |  34 ++++++
 visionstate/backend/visionstate/teach.py      |  23 ++--
 visionstate/config.yaml                       |   2 +-
 visionstate/frontend/e2e/ui.spec.ts           |  72 +++++++++++-
 visionstate/frontend/src/lib/api.ts           |   4 +
 .../src/lib/components/ModelCompare.svelte    | 105 +++++++++++++++++
 .../lib/components/TeachLimitsEditor.svelte   | 111 ++++++++++++++++++
 visionstate/frontend/src/lib/types.ts         |  29 ++++-
 visionstate/frontend/src/lib/ui.ts            |  47 ++++++++
 .../frontend/src/pages/Settings.svelte        |  57 ++++++++-
 .../src/pages/sensor/SettingsTab.svelte       |  19 ++-
 32 files changed, 1002 insertions(+), 107 deletions(-)
 create mode 100644 visionstate/backend/tests/test_models.py
 create mode 100644 visionstate/frontend/src/lib/components/ModelCompare.svelte
 create mode 100644 visionstate/frontend/src/lib/components/TeachLimitsEditor.svelte

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index aa68409..9c8ae1d 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -25,12 +25,12 @@ jobs:
           cache: pip
           cache-dependency-path: visionstate/backend/requirements*.txt
       - run: pip install -r requirements-dev.txt
-      - name: Cache AI model
+      - name: Cache AI models (all of them, for tests/test_models.py)
         uses: actions/cache@v6
         with:
           path: visionstate/backend/models
-          key: models-${{ hashFiles('visionstate/backend/visionstate/backbones.json', 'visionstate/backend/visionstate/detectors.json', 'visionstate/backend/visionstate/readers.json') }}
-      - run: python -m visionstate.backbones models
+          key: models-all-${{ hashFiles('visionstate/backend/visionstate/backbones.json', 'visionstate/backend/visionstate/detectors.json', 'visionstate/backend/visionstate/readers.json') }}
+      - run: python -m visionstate.backbones models --all
       - run: ruff check visionstate tests
       - run: ruff format --check visionstate tests
       - name: Types (pyright)
diff --git a/CLAUDE.md b/CLAUDE.md
index c7e3151..5ccf485 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -61,7 +61,7 @@ Every fact has **one** home; other files link to it instead of repeating it.
 - New engine code goes into the part (mixin) it belongs to. A long-running loop must survive any
   exception (log it once, try again) — only `CancelledError` may end it.
 - Model registries (`backbones.json`, `detectors.json`, `readers.json`): entries are never changed
-  or removed once released; a new model gets a new id, pinned by Hugging Face revision and SHA-256,
+  or removed once released (their texts and measured footprint may be); a new model gets a new id, pinned by Hugging Face revision and SHA-256,
   and is checked against real inputs in the tests (one public ONNX conversion turned out to be
   broken and only a test with real photos showed it).
 - Frontend: colours/type/spacing only in `tokens.css`, UI constants in `ui.ts`, API URLs only in
@@ -78,7 +78,8 @@ Every fact has **one** home; other files link to it instead of repeating it.
 - Backend: `cd visionstate/backend && .venv/Scripts/python -m pytest -q && .venv/Scripts/ruff check visionstate tests && .venv/Scripts/ruff format visionstate tests`
   and the types `.venv/Scripts/pyright --pythonpath .venv/Scripts/python.exe` (0 errors; on Linux and
   macOS the venv's programs are in `.venv/bin/`). Tests need
-  the bundled models once: `python -m visionstate.backbones models`. CI fails below 88 % coverage;
+  the bundled models once: `python -m visionstate.backbones models` (`--all` adds the downloadable
+  ones, so `tests/test_models.py` checks every model). CI fails below 88 % coverage;
   `--cov=visionstate --cov-report=term-missing:skip-covered` shows what is not tested.
 - Frontend: `cd visionstate/frontend && npm run check && npm run build`.
 - UI: see the routine below — every UI change, and before every beta release.
diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index 66d4e2a..42fa992 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -51,7 +51,7 @@ API as `request.app.state.runtime`.
 | `db.py` | SQLAlchemy tables and the schema migrations (`MIGRATIONS`, `SCHEMA_VERSION`) |
 | `storage.py` | Image files on disk: training samples, history frames, thumbnails |
 | `engine/` | The runtime — see below |
-| `api/` | The REST API (`/api/v1/…`), one file per topic: `sensors.py` (CRUD, quality, export), `history.py` (the history: one filter for the list, its count and facets; history frames), `samples.py` (training images), `teach.py` (taught boxes), `cameras.py` (camera and entity lists, the light, previews), `review.py` (review queue and answers), `imports.py` (import), `system.py` (UI config, status, AI models, storage, review rules, review reminder), `common.py` (input models and views shared by them) |
+| `api/` | The REST API (`/api/v1/…`), one file per topic: `sensors.py` (CRUD, quality, export), `history.py` (the history: one filter for the list, its count and facets; history frames), `samples.py` (training images), `teach.py` (taught boxes), `cameras.py` (camera and entity lists, the light, previews), `review.py` (review queue and answers), `imports.py` (import), `system.py` (UI config, status, AI models, storage, review rules, review reminder, teaching limits), `common.py` (input models and views shared by them) |
 | `sources.py` | Camera sources: Home Assistant camera, HTTP snapshot, RTSP; address checks and size limits; the Home Assistant REST client (states, services, notify services, the app's page) |
 | `mqtt.py` | MQTT topics, Home Assistant discovery messages, the bridge (connect, publish, commands) |
 | `ha_events.py` | Home Assistant WebSocket: state changes of trigger entities, entity registry |
@@ -169,8 +169,10 @@ in `lib/api.ts`, its type in `lib/types.ts`.
 `EVENT_ROWS` (`api/history.py`; every filter becomes SQL in `history_query`, nowhere else), its
 label in `HISTORY_EVENTS` in `lib/ui.ts`.
 
-**A new AI model**: a new entry in the registry JSON (never change a released one), checked
-against real inputs in the tests.
+**A new AI model**: a new entry in the registry JSON (never change a released one's file or
+inputs), with its measured footprint (`ram_mb`, `time_ms`, `accuracy`; see `backbones.Footprint`).
+`tests/test_models.py` checks every entry against real inputs once it is downloaded
+(`python -m visionstate.backbones models --all`, as CI does).
 
 **A new page or tab**: the route in `router.svelte.ts` (tab: `TABS_BY_KIND` in `ui.ts`), the page
 in `pages/`, and an e2e test in `e2e/ui.spec.ts` on all three devices.
diff --git a/docs/SCOPE.md b/docs/SCOPE.md
index f4d891a..bcc4357 100644
--- a/docs/SCOPE.md
+++ b/docs/SCOPE.md
@@ -81,8 +81,21 @@ so retraining only runs the backbone on new or changed samples.
 
 | ID | Model | Use |
 |---|---|---|
-| `dinov2-small-q8` | DINOv2 ViT-S/14, 8-bit quantised (bundled) | Default everywhere, ~25 ms per frame on a desktop CPU |
+| `dinov2-small-q8` | DINOv2 ViT-S/14, 8-bit quantised (bundled) | Default everywhere |
 | `dinov2-small` | DINOv2 ViT-S/14, fp32 (downloaded on demand, SHA-256 checked) | Optional, slightly more accurate |
+| `dinov2-base` | DINOv2 ViT-B/14, fp32 (downloaded on demand, 347 MB) | Fast PCs; separates objects best |
+
+- Each registry entry carries a rough footprint for **Compare the models** in Settings
+  (`backbones.Footprint`: extra memory, time per check on 4 threads of a desktop CPU, an accuracy
+  rank within its kind), measured when the model was added. Texts and footprint may be updated in a
+  released entry; its file and inputs never change.
+- DINOv2 base, evaluated (2026-10-11) with the crops of the 14 objects in the CC0 test photos, each
+  also changed in light, framing, blur, JPEG quality and night colours: the gap between the same
+  object changed (5th percentile) and other objects of its kind (90th percentile) was 0.12 for base
+  fp32, 0.08 for small fp32, 0.06 for small 8-bit — and 0.04 for base 8-bit, whose quantisation
+  squeezes everything together (other objects of a kind around 0.82), so base is offered in fp32
+  only; fp16 saved disk but used more memory on CPU. Base needs slightly stricter teaching limits
+  (`settings.TEACH_BY_BACKBONE`).
 
 - MobileNet was dropped: the available ONNX exports only expose classification logits.
 - Switching backbone retrains every sensor from its stored samples.
@@ -101,8 +114,14 @@ with the 80 COCO labels, their keys, groups and the popular ones):
 
 | ID | Model | Use |
 |---|---|---|
-| `dfine-s-coco` | D-FINE S, COCO, fp32 ONNX (bundled, 42 MB) | Default; ~0.1 s per check on a desktop CPU |
+| `dfine-s-coco` | D-FINE S, COCO, fp32 ONNX (bundled, 42 MB) | Default |
 | `dfine-n-coco` | D-FINE N, COCO, fp32 ONNX (downloaded on demand, 15 MB) | Faster, misses more |
+| `dfine-m-coco` | D-FINE M, COCO, fp32 ONNX (downloaded on demand, 79 MB) | Fast PCs; finds more small and hidden objects |
+| `dfine-l-coco` | D-FINE L, COCO, fp32 ONNX (downloaded on demand, 125 MB) | Fast PCs; the most accurate |
+
+D-FINE X (251 MB) was left out: twice the size of L for little more. At the default 60 % threshold
+M and L find the same objects in the test photos as S, with fewer weak false boxes, so the
+threshold is the same for every detector.
 
 1. Crop the ROI's bounding box plus a margin (`DETECTION["context_margin"]`) so edge objects are
    seen whole; stretch to 640×640 (no letterbox), scale to 0–1.
@@ -151,18 +170,28 @@ knew). Instead a second step compares boxes with boxes the user taught:
 - Nothing changes for a sensor until something is taught; **Use what you taught** off skips it.
   The analysed frames of the last checks are kept (`frames_kept`) so a box can be taught a while
   after it was shown. Export/import carries taught boxes and own labels.
+- **Tunable** (`settings.TEACH_TUNABLE`): `match_similarity`, `keep_similarity`, `ask_similarity`,
+  `rescue_similarity` and `rescue_floor` can be set by the user, globally (DB setting `teach`,
+  `GET/PUT /api/v1/teach-limits`) and per sensor (`objects["teach"]`); unset values inherit.
+  `settings.merge_teach`: the backbone's defaults (`TEACH`, with `TEACH_BY_BACKBONE` on top) <
+  global < sensor. Asking and keeping must not start above a match (refused by the API). A user's
+  value stays when the backbone changes; the UI says to look at it again.
 
 - Weights: D-FINE (Apache-2.0, COCO-trained; not the Objects365 variants, which carry other
   terms), ONNX conversions from Hugging Face pinned by revision and SHA-256. One conversion
   of D-FINE N was found broken during evaluation; tests with real CC0 photos
-  (`tests/assets`) now guard every model file. 8-bit variants were slower and worse on CPU.
+  (`tests/assets`, `tests/test_models.py`) now guard every model file. 8-bit variants were slower and worse on CPU.
 **Reading sensors** use a CTC text recognizer (registry `backend/visionstate/readers.json`):
 
 | ID | Model | Use |
 |---|---|---|
-| `ppocrv6-tiny` | PP-OCRv6 tiny rec, ONNX (bundled, 4.5 MB) | Default; ~2 ms per read on a desktop CPU |
+| `ppocrv6-tiny` | PP-OCRv6 tiny rec, ONNX (bundled, 4.5 MB) | Default |
 | `ppocrv6-small` | PP-OCRv6 small rec, ONNX (downloaded on demand, 21 MB) | Unusual fonts |
 
+PP-OCRv6 medium (77 MB) was evaluated and left out (2026-10-11): on 280 drawn LCD/LED displays
+(clean, blurred, low resolution, low contrast, noisy, tilted, heavy JPEG) and 49 drawn counters
+it read 297 of 329 right, tiny 304 and small 316; it took 9 for 3 or 8 on seven-segment digits.
+
 1. Crop the ROI's bounding box (no margin, no mask). Display *auto*: autocontrast greyscale;
    when the read is unsure (< `READING["segments_fallback_below"]`) also try the digits' own
    segments (two-stage Otsu on brightness for light-on-dark, on darkness for dark-on-light,
@@ -519,6 +548,7 @@ is part of backups and needs no restart.
 | **History** ✅ | History page with filters shared by every sensor's History tab, the images behind the Quality tab's mix-ups | 0.7.0 |
 | **Wheel reader** ✅ | Mechanical counters read wheel by wheel with a model made for VisionState; wheels mid-turn read right; export v2 of readings | 0.8.0 (betas 0.7.1b3–b6) |
 | **Review reminder** ✅ | A notification in Home Assistant (optionally a push) when frames have waited for review a long time | 0.8.0 (beta 0.7.1b1) |
+| **Larger models, tunable teaching** ✅ | D-FINE M and L, DINOv2 base, a model comparison in Settings, teaching limits globally and per sensor | beta 0.8.1b1 |
 | **Better counter readings** | Benchmark, the last value as an expectation, calibrated confidence, extra decimal, self-calibrating regions, wheels-v2, learning per sensor — status per phase in [`WHEEL_READER_PLAN.md`](WHEEL_READER_PLAN.md) | planned |
 
 **Open ideas** (not scheduled): more on the History page (export of a filtered selection, a
diff --git a/visionstate/CHANGELOG.md b/visionstate/CHANGELOG.md
index f2b0ef4..40645fc 100644
--- a/visionstate/CHANGELOG.md
+++ b/visionstate/CHANGELOG.md
@@ -1,5 +1,25 @@
 # Changelog
 
+## 0.8.1b1
+
+### New
+
+- **Larger AI models for fast PCs**, downloaded on first use under **Settings → AI model**:
+  - **D-FINE M** (79 MB) and **D-FINE L** (125 MB) for object sensors find more small, far-off and
+    partly hidden objects than the included D-FINE S, at about three and four times its time per
+    check.
+  - **DINOv2 base** (347 MB, about 400 MB of memory) for state sensors tells similar states and
+    taught objects apart best, at about three times the time of DINOv2 small.
+- **Compare the models**: under the model choices, every model with its rough memory use, time per
+  check, download size and how accurate it is compared with the others.
+- **Teaching limits you can tune.** How close a box must be to the boxes you taught an object sensor
+  — to take their answer, to keep it while it stays in place, to ask you in the review queue, to
+  find a missed object again — can now be set under **Settings → Teaching object sensors** for all
+  object sensors, and per sensor under its **Settings → What you taught**. An empty field shows in
+  grey the value it uses (the default, or on a sensor the global value) until you set your own.
+  The defaults follow the state model: with DINOv2 base a box must be a little closer to count.
+  A sensor's own values come along in its export.
+
 ## 0.8.0
 
 ### New
diff --git a/visionstate/DOCS.md b/visionstate/DOCS.md
index 415adb3..6d804e0 100644
--- a/visionstate/DOCS.md
+++ b/visionstate/DOCS.md
@@ -149,6 +149,15 @@ waiting, and no more often than the review cooldown (**Settings → Review queue
   away lately — check now and then that nothing real is filtered away.
 - **Settings → What you taught**: **Use what you taught** off lets the AI alone decide again (what
   you taught is kept); **Forget all** removes every taught box and own label.
+- **How close is close enough** can be tuned, in percent of likeness (100% = the very same
+  picture): when a box takes a taught answer, how long it keeps it while it stays in place, from
+  when it asks in the review queue, when a missed object is found again, and how unsure an AI box
+  may be to be looked at for one. **Settings → Teaching object sensors** sets them for all object
+  sensors; a sensor's **Settings → What you taught → Limits for this sensor** can set its own.
+  An empty field inherits and shows in grey what it uses: the default (which depends on the state
+  model, see below), or on a sensor the global value. Values you set yourself stay when you switch
+  the state model — look at them again after a switch. Recognised too eagerly? Raise the first
+  value; a known object not recognised in other light? Lower it a little, or teach it in that light.
 - A box drawn where the AI sees nothing at all (not even unsure) cannot be found by itself; you
   get a note when that is the case.
 - Export and import take what you taught along.
@@ -392,12 +401,16 @@ two apps at a time (both publish the same entities).
 Three models, chosen under **Settings → AI model** for all sensors of a kind:
 
 - **State sensors:** DINOv2 small, 8-bit — included, runs on any CPU including a Raspberry
-  Pi 4. A slightly more accurate full-precision version is downloaded on first use (89 MB).
-  Switching retrains every state sensor from its stored images. It is always loaded: object
-  sensors that were taught use it too, to compare boxes with the ones you taught.
+  Pi 4. A slightly more accurate full-precision version is downloaded on first use (89 MB), and
+  for a fast PC **DINOv2 base** (347 MB, about 400 MB of memory, about three times as slow), which
+  tells similar states and taught objects apart best. Switching retrains every state sensor from
+  its stored images. It is always loaded: object sensors that were taught use it too, to compare
+  boxes with the ones you taught (with DINOv2 base a box must be a little closer to count).
 - **Object sensors:** D-FINE S — included, about 0.1 s per check on a modern PC and a few
   seconds on a Raspberry Pi 4. D-FINE N is faster and lighter but misses more (downloaded on
-  first use, 15 MB). The detector is only loaded while at least one object sensor exists.
+  first use, 15 MB). For a fast PC, **D-FINE M** (79 MB) and **D-FINE L** (125 MB) find more
+  small, far-off and partly hidden objects, at about three and four times the time of S. The
+  detector is only loaded while at least one object sensor exists.
 - **Reading sensors:** PP-OCRv6 tiny (PaddleOCR) — included, a few milliseconds per reading.
   PP-OCRv6 small is larger and reads unusual fonts more reliably (downloaded on first use,
   21 MB). Mechanical counters are read by the included **VisionState wheel reader** (2 MB, a few
@@ -405,7 +418,9 @@ Three models, chosen under **Settings → AI model** for all sensors of a kind:
   is set to the text reader. There is one wheel reader so far, so there is nothing to choose.
   Only loaded while at least one reading sensor exists.
 
-A choice you made stays when a later version recommends another model.
+**Compare the models** (under the choices) lists every model with its rough memory use, time
+per check and how accurate it is compared with the others. A choice you made stays when a later
+version recommends another model.
 
 ## App options
 
diff --git a/visionstate/backend/tests/test_models.py b/visionstate/backend/tests/test_models.py
new file mode 100644
index 0000000..65958b7
--- /dev/null
+++ b/visionstate/backend/tests/test_models.py
@@ -0,0 +1,108 @@
+"""Every model in the registries, on real inputs: a broken file or conversion must fail here.
+
+Models that are downloaded on first use are only on disk after
+``python -m visionstate.backbones models --all`` (CI does that); otherwise their tests are skipped.
+"""
+
+import re
+
+import pytest
+from PIL import Image, ImageEnhance
+
+from visionstate import backbones, detectors, readers, teach
+from visionstate.settings import merge_teach
+
+from .conftest import ASSETS, MODEL_DIR
+from .displays import render
+
+
+def on_disk(spec) -> object:
+    return pytest.param(
+        spec,
+        id=spec.id,
+        marks=pytest.mark.skipif(
+            not (MODEL_DIR / spec.filename).exists(), reason=f"{spec.id} not downloaded (backbones models --all)"
+        ),
+    )
+
+
+ALL_SPECS = [
+    *backbones.BACKBONES.values(),
+    *detectors.DETECTORS.values(),
+    *readers.READERS.values(),
+    *readers.WHEEL_READERS.values(),
+]
+
+
+@pytest.mark.parametrize("spec", ALL_SPECS, ids=lambda s: s.id)
+def test_every_model_is_pinned_and_described(spec):
+    assert "/resolve/" in spec.url and re.fullmatch(r"[0-9a-f]{64}", spec.sha256)
+    assert spec.size > 0 and spec.description
+    # The model comparison in Settings shows these for every model.
+    assert spec.footprint.ram_mb > 0 and spec.footprint.time_ms > 0 and 1 <= spec.footprint.accuracy <= 5
+
+
+@pytest.mark.parametrize("registry", [backbones.BACKBONES, detectors.DETECTORS, readers.READERS])
+def test_slower_models_are_more_accurate(registry):
+    """Within a kind, a model that costs more time must rank at least as accurate (else why offer it)."""
+    by_time = sorted(registry.values(), key=lambda s: s.footprint.time_ms)
+    ranks = [s.footprint.accuracy for s in by_time]
+    assert ranks == sorted(ranks)
+
+
+def counts(found) -> dict[str, int]:
+    result: dict[str, int] = {}
+    for det in found:
+        result[det.key] = result.get(det.key, 0) + 1
+    return result
+
+
+@pytest.mark.parametrize("spec", [on_disk(s) for s in detectors.DETECTORS.values()])
+def test_every_detector_finds_objects_in_real_photos(spec):
+    """At the default threshold, on the CC0 photos (tests/assets/README.md)."""
+    detector = detectors.Detector(spec, MODEL_DIR / spec.filename)
+    driveway = counts(detector.detect(Image.open(ASSETS / "driveway.jpg"), 0.6))
+    beach = counts(detector.detect(Image.open(ASSETS / "beach.jpg"), 0.6))
+    cat = counts(detector.detect(Image.open(ASSETS / "cat.jpg"), 0.6))
+    assert driveway.get("car", 0) >= 3 and driveway.get("person") == 1
+    assert beach.get("dog", 0) >= 4 and beach.get("person") == 1
+    assert cat == {"cat": 1}
+
+
+@pytest.mark.parametrize("spec", [on_disk(s) for s in backbones.BACKBONES.values()])
+def test_every_backbone_tells_taught_objects_apart(spec):
+    """With its default teaching limits: the same dog in other light is recognised, another dog
+    and a car are not even asked about."""
+    embedder = backbones.Embedder(spec, MODEL_DIR / spec.filename)
+    limits = merge_teach(spec.id, None, None)
+    beach = Image.open(ASSETS / "beach.jpg").convert("RGB")
+    driveway = Image.open(ASSETS / "driveway.jpg").convert("RGB")
+    detector_spec = detectors.DETECTORS[detectors.DEFAULT_DETECTOR]
+    found = detectors.Detector(detector_spec, MODEL_DIR / detector_spec.filename).detect(beach, 0.6)
+    dogs = sorted((d.box for d in found if d.key == "dog"), key=lambda b: b[0])
+    car = next(d.box for d in detectors.Detector(detector_spec, MODEL_DIR / detector_spec.filename).detect(driveway, 0.6)
+               if d.key == "car")  # fmt: skip
+
+    def vector(image, box):
+        return teach.embed(embedder, [teach.crop(image, box)])[0]
+
+    dog = vector(beach, dogs[0])
+    darker = vector(ImageEnhance.Brightness(beach).enhance(0.8), dogs[0])
+    assert float(dog @ darker) >= limits["match_similarity"]
+    assert float(dog @ vector(driveway, car)) < limits["ask_similarity"]
+    assert max(float(dog @ vector(beach, other)) for other in dogs[1:]) < limits["match_similarity"]
+
+
+@pytest.mark.parametrize("spec", [on_disk(s) for s in readers.READERS.values()])
+@pytest.mark.parametrize(("text", "style"), [("74590", "lcd"), ("0123456.7", "lcd"), ("2:38", "led"), ("98765", "led")])
+def test_every_reader_reads_drawn_displays(spec, text, style):
+    reader = readers.Reader(spec, MODEL_DIR / spec.filename)
+    result, _ = reader.read_display(render(text, style), "auto")
+    assert result.text == text and result.score > 0.8
+
+
+@pytest.mark.parametrize("spec", [on_disk(s) for s in readers.WHEEL_READERS.values()])
+def test_every_wheel_reader_reads_a_real_meter(spec):
+    wheels = readers.WheelReader(spec, MODEL_DIR / spec.filename)
+    result, _ = wheels.read_counter(Image.open(ASSETS / "counter_red.jpg").convert("RGB"), 7)
+    assert result.text == "0632289"
diff --git a/visionstate/backend/tests/test_teach.py b/visionstate/backend/tests/test_teach.py
index 379f676..c5552a8 100644
--- a/visionstate/backend/tests/test_teach.py
+++ b/visionstate/backend/tests/test_teach.py
@@ -443,3 +443,83 @@ def after_a_check():
         after_a_check()
         assert client.post(f"/api/v1/review/{item['id']}", json={"action": "skip"}).status_code == 200
         assert taught() == ["rex"]
+
+
+# --- tunable limits: backbone defaults < global < sensor -----------------------------------------
+
+
+def test_teach_limits_inherit_backbone_global_and_sensor():
+    from visionstate.settings import TEACH_BY_BACKBONE, TEACH_TUNABLE, merge_teach, teach_defaults
+
+    small = teach_defaults("dinov2-small-q8")
+    assert small == {key: TEACH[key] for key in TEACH_TUNABLE}
+    base = teach_defaults("dinov2-base")
+    assert base["match_similarity"] == TEACH_BY_BACKBONE["dinov2-base"]["match_similarity"] > small["match_similarity"]
+    merged = merge_teach(
+        "dinov2-base", {"ask_similarity": 0.7, "junk": 1}, {"ask_similarity": 0.72, "rescue_floor": None}
+    )
+    assert merged["ask_similarity"] == 0.72  # the sensor's own value wins
+    assert merged["match_similarity"] == base["match_similarity"]  # nobody set it: the backbone's default
+    assert merged["rescue_floor"] == TEACH["rescue_floor"]  # None = inherit
+    assert "junk" not in merged and merged["max_labels"] == TEACH["max_labels"]  # the rest of TEACH comes along
+
+
+def test_apply_uses_the_limits_it_is_given():
+    parents = {"our_car": "car"}
+    idx = index([("our_car", "car", OUR_CAR)], parents)
+    vector = np.stack([toward(OUR_CAR, unit(0, 0, 0, 1), 0.85)])
+    found = [det("car")]
+    strict = teach.apply(found, [0], vector, [], np.zeros((0, 4)), idx, ["car"], parents)[0]
+    assert "label" not in strict  # 0.85 is below the default 0.88
+    loose = {**TEACH, "match_similarity": 0.84}
+    assert (
+        teach.apply(found, [0], vector, [], np.zeros((0, 4)), idx, ["car"], parents, None, loose)[0]["label"]
+        == "our_car"
+    )
+    no_asking = {**TEACH, "ask_similarity": 0.86}
+    assert "ask" not in teach.apply(found, [0], vector, [], np.zeros((0, 4)), idx, ["car"], parents, None, no_asking)[0]
+
+
+def test_teach_limits_api_global_sensor_and_export(settings):  # noqa: F811
+    from visionstate.settings import teach_defaults
+
+    with TestClient(create_app(settings)) as client:
+        view = client.get("/api/v1/teach-limits").json()
+        defaults = teach_defaults(view["backbone"])
+        assert view["defaults"] == defaults == view["effective"]
+        assert set(view["values"]) == set(defaults) and not any(view["values"].values())
+        config = client.get("/api/v1/config").json()["teach"]
+        assert config["tunable"] == list(defaults) and config["limits"]["match_similarity"][1] < 1
+
+        # Global: set one, the rest stay at the default; nonsense is refused.
+        view = client.put("/api/v1/teach-limits", json={"match_similarity": 0.92}).json()
+        assert view["values"]["match_similarity"] == 0.92 and view["effective"]["match_similarity"] == 0.92
+        assert view["effective"]["ask_similarity"] == defaults["ask_similarity"]
+        assert client.put("/api/v1/teach-limits", json={"ask_similarity": 0.95}).status_code == 400  # above match
+        assert client.put("/api/v1/teach-limits", json={"match_similarity": 1.5}).status_code == 422
+        rt = client.app.state.runtime
+        assert rt.global_teach == {"match_similarity": 0.92}
+
+        # A sensor: inherits, can have its own values, and loses them again.
+        sid = create(client, objects={"classes": ["dog"]}).json()["id"]
+        assert sensor(client, sid)["objects"]["teach"] == {}
+        assert rt.teach_limits(rt.load_sensor(sid))["match_similarity"] == 0.92
+        resp = client.patch(f"/api/v1/sensors/{sid}", json={"objects": {"teach": {"match_similarity": 0.8}}})
+        assert resp.status_code == 200, resp.text
+        assert sensor(client, sid)["objects"]["teach"] == {"match_similarity": 0.8}
+        assert sensor(client, sid)["objects"]["classes"] == ["dog"]  # the rest of objects stays
+        assert rt.teach_limits(rt.load_sensor(sid))["match_similarity"] == 0.8
+        bad = {"objects": {"teach": {"match_similarity": 0.8, "keep_similarity": 0.85}}}
+        assert client.patch(f"/api/v1/sensors/{sid}", json=bad).status_code == 400  # keep above match
+        assert sensor(client, sid)["objects"]["teach"] == {"match_similarity": 0.8}  # unchanged
+
+        # Export and import carry the sensor's own values.
+        exported = client.get(f"/api/v1/sensors/{sid}/export")
+        imported = client.post("/api/v1/import", files={"file": ("b.zip", exported.content, "application/zip")})
+        assert imported.status_code == 201, imported.text
+        assert sensor(client, imported.json()["id"])["objects"]["teach"] == {"match_similarity": 0.8}
+
+        client.patch(f"/api/v1/sensors/{sid}", json={"objects": {"teach": {}}})
+        assert sensor(client, sid)["objects"]["teach"] == {}
+        client.put("/api/v1/teach-limits", json={})
+        assert rt.teach_limits(rt.load_sensor(sid))["match_similarity"] == defaults["match_similarity"]
diff --git a/visionstate/backend/visionstate/api/common.py b/visionstate/backend/visionstate/api/common.py
index 5dfbd13..831b9ce 100644
--- a/visionstate/backend/visionstate/api/common.py
+++ b/visionstate/backend/visionstate/api/common.py
@@ -32,6 +32,7 @@
     REVIEW_DEFAULTS,
     REVIEW_LIMITS,
     ROI_MAX_POINTS,
+    TEACH_TUNABLE_LIMITS,
     TRIGGER_DEFAULTS,
     TRIGGER_LIMITS,
     TRIGGER_MAX_ENTITIES,
@@ -180,6 +181,30 @@ def _valid_only_states(self) -> Triggers:
 
 _olo = {k: v[0] for k, v in OBJECT_LIMITS.items()}
 _ohi = {k: v[1] for k, v in OBJECT_LIMITS.items()}
+_tlo = {k: v[0] for k, v in TEACH_TUNABLE_LIMITS.items()}
+_thi = {k: v[1] for k, v in TEACH_TUNABLE_LIMITS.items()}
+
+
+class TeachLimits(BaseModel):
+    """The tunable teaching limits (settings.TEACH_TUNABLE). None = inherit: globally the
+    backbone's default, on a sensor the global value."""
+
+    match_similarity: float | None = Field(None, ge=_tlo["match_similarity"], le=_thi["match_similarity"])
+    keep_similarity: float | None = Field(None, ge=_tlo["keep_similarity"], le=_thi["keep_similarity"])
+    ask_similarity: float | None = Field(None, ge=_tlo["ask_similarity"], le=_thi["ask_similarity"])
+    rescue_similarity: float | None = Field(None, ge=_tlo["rescue_similarity"], le=_thi["rescue_similarity"])
+    rescue_floor: float | None = Field(None, ge=_tlo["rescue_floor"], le=_thi["rescue_floor"])
+
+    def stored(self) -> dict:
+        return {k: v for k, v in self.model_dump().items() if v is not None}
+
+
+def check_teach(limits: dict) -> None:
+    """Effective teaching limits must make sense together: asking and keeping start below a match."""
+    if limits["ask_similarity"] > limits["match_similarity"]:
+        raise HTTPException(400, "“Asks you in the review queue” must not be above “Recognised as a box you taught”")
+    if limits["keep_similarity"] > limits["match_similarity"]:
+        raise HTTPException(400, "“Keeps that answer” must not be above “Recognised as a box you taught”")
 
 
 class ObjectsIn(BaseModel):
@@ -191,8 +216,15 @@ class ObjectsIn(BaseModel):
         default=OBJECT_DEFAULTS["clear_after_s"], ge=_olo["clear_after_s"], le=_ohi["clear_after_s"]
     )
     use_taught: bool = OBJECT_DEFAULTS["use_taught"]
+    teach: TeachLimits | None = None  # this sensor's own teaching limits; unset ones follow the global ones
     # Own labels ("custom") are made by teaching a box (api/teach.py), never set here.
 
+    def stored(self, exclude_unset: bool = False) -> dict:
+        data = self.model_dump(exclude_unset=exclude_unset, exclude={"teach"})
+        if self.teach is not None or not exclude_unset:
+            data["teach"] = self.teach.stored() if self.teach else {}
+        return data
+
     @field_validator("classes")
     @classmethod
     def _known_classes(cls, value: list[str]) -> list[str]:
diff --git a/visionstate/backend/visionstate/api/sensors.py b/visionstate/backend/visionstate/api/sensors.py
index 55694b8..11988a0 100644
--- a/visionstate/backend/visionstate/api/sensors.py
+++ b/visionstate/backend/visionstate/api/sensors.py
@@ -30,6 +30,8 @@
     SENSOR_LIMITS,
     SENSOR_PUBLISH_DEFAULT,
     STATE_PALETTE,
+    merge_objects,
+    merge_teach,
 )
 from ..sources import SOURCE_TYPES, SourceError
 from .common import (
@@ -40,6 +42,7 @@
     Roi,
     StateIn,
     Triggers,
+    check_teach,
     get_sensor,
     runtime,
     sample_counts,
@@ -115,7 +118,7 @@ def new_sensor(self, slug: str) -> Sensor:
             publish=self.publish,
             triggers=self.triggers.model_dump() if self.triggers else None,
             review=self.review.stored() if self.review else None,
-            objects=self.objects.model_dump() if self.objects else None,
+            objects=self.objects.stored() if self.objects else None,
             reading=self.reading.model_dump() if self.reading else None,
         )
         _apply_states(sensor, self.states)
@@ -223,7 +226,8 @@ async def update_sensor(sensor_id: int, body: SensorPatch, request: Request) ->
             _apply_states(sensor, body.states)
         if body.objects is not None:
             # Merged, so own labels (made by teaching) and fields an older UI does not send stay.
-            sensor.objects = {**(sensor.objects or {}), **body.objects.model_dump(exclude_unset=True)}
+            sensor.objects = {**(sensor.objects or {}), **body.objects.stored(exclude_unset=True)}
+            check_teach(merge_teach(rt.backbone_id, rt.global_teach, merge_objects(sensor.objects)["teach"]))
         if body.reading is not None:
             sensor.reading = body.reading.model_dump()
         s.flush()
diff --git a/visionstate/backend/visionstate/api/system.py b/visionstate/backend/visionstate/api/system.py
index d4ed18e..5fb7866 100644
--- a/visionstate/backend/visionstate/api/system.py
+++ b/visionstate/backend/visionstate/api/system.py
@@ -1,5 +1,5 @@
-"""App-wide endpoints: the UI config, status, the AI models, history storage limits, review rules
-and the review reminder.
+"""App-wide endpoints: the UI config, status, the AI models, history storage limits, review rules,
+the review reminder and the teaching limits.
 
 Cameras and previews are in cameras.py, the review queue in review.py, import in imports.py."""
 
@@ -49,6 +49,8 @@
     STORAGE_DEFAULTS,
     STORAGE_LIMITS,
     TEACH,
+    TEACH_TUNABLE,
+    TEACH_TUNABLE_LIMITS,
     TRIGGER_DEFAULTS,
     TRIGGER_LIMITS,
     TRIGGER_MAX_ENTITIES,
@@ -56,11 +58,15 @@
     VERSION,
     VIDEO,
     merge_review,
+    merge_teach,
+    teach_defaults,
 )
 from ..sources import SOURCE_TYPES, SourceError
 from .common import (
     API_PREFIX,
     ReviewRules,
+    TeachLimits,
+    check_teach,
     iso,
     runtime,
 )
@@ -100,7 +106,12 @@ def ui_config() -> dict:
         "object_max_classes": OBJECT_MAX_CLASSES,
         "object_labels": [{"key": x.key, "name": x.name, "group": x.group} for x in detectors.LABELS.labels],
         "object_popular": list(detectors.LABELS.popular),
-        "teach": {"none_label": NONE_LABEL, "max_labels": TEACH["max_labels"]},
+        "teach": {
+            "none_label": NONE_LABEL,
+            "max_labels": TEACH["max_labels"],
+            "tunable": list(TEACH_TUNABLE),
+            "limits": TEACH_TUNABLE_LIMITS,
+        },
         "reading_sensor_defaults": READING_SENSOR_DEFAULTS,
         "reading_defaults": READING_DEFAULTS,
         "reading_limits": READING_LIMITS,
@@ -173,58 +184,29 @@ class SettingsIn(BaseModel):
 def get_settings(request: Request) -> dict:
     """The chosen AI models and every model that can be chosen."""
     rt = runtime(request)
+
+    def model(spec) -> dict:
+        return {
+            "id": spec.id,
+            "name": spec.name,
+            "description": spec.description,
+            "installed": rt.model_path(spec) is not None,
+            "size": spec.size,
+            "license": spec.license,
+            "source": spec.source,
+            **spec.footprint.as_dict(),
+        }
+
     return {
         "backbone": rt.embedder.spec.id if rt.embedder else backbones.DEFAULT_BACKBONE,
-        "backbones": [
-            {
-                "id": spec.id,
-                "name": spec.name,
-                "description": spec.description,
-                "installed": rt.model_path(spec) is not None,
-                "size": spec.size,
-            }
-            for spec in backbones.BACKBONES.values()
-        ],
+        "backbones": [model(spec) for spec in backbones.BACKBONES.values()],
         "detector": rt.db.get_text("detector", detectors.DEFAULT_DETECTOR),
-        "detectors": [
-            {
-                "id": spec.id,
-                "name": spec.name,
-                "description": spec.description,
-                "installed": rt.model_path(spec) is not None,
-                "size": spec.size,
-                "license": spec.license,
-                "source": spec.source,
-            }
-            for spec in detectors.DETECTORS.values()
-        ],
+        "detectors": [model(spec) for spec in detectors.DETECTORS.values()],
         "reader": rt.db.get_text("reader", readers.DEFAULT_READER),
-        "readers": [
-            {
-                "id": spec.id,
-                "name": spec.name,
-                "description": spec.description,
-                "installed": rt.model_path(spec) is not None,
-                "size": spec.size,
-                "license": spec.license,
-                "source": spec.source,
-            }
-            for spec in readers.READERS.values()
-        ],
+        "readers": [model(spec) for spec in readers.READERS.values()],
         # Mechanical counters set to the wheel reader; one model for now, so nothing to choose.
         "wheel_reader": readers.DEFAULT_WHEEL_READER,
-        "wheel_readers": [
-            {
-                "id": spec.id,
-                "name": spec.name,
-                "description": spec.description,
-                "installed": rt.model_path(spec) is not None,
-                "size": spec.size,
-                "license": spec.license,
-                "source": spec.source,
-            }
-            for spec in readers.WHEEL_READERS.values()
-        ],
+        "wheel_readers": [model(spec) for spec in readers.WHEEL_READERS.values()],
         "options": {
             "discovery_prefix": rt.settings.discovery_prefix,
             "mqtt_host": rt.settings.mqtt_host or "(from Home Assistant)",
@@ -303,6 +285,34 @@ async def put_review_rules(body: ReviewRules, request: Request) -> dict:
     return merge_review(rt.global_review, None)
 
 
+# --- teaching limits ----------------------------------------------------------
+
+
+def _teach_view(rt) -> dict:
+    """The global teaching limits: the backbone's defaults, what the user set (None: the default)
+    and the result, which each object sensor inherits unless it has its own value."""
+    backbone = rt.backbone_id
+    return {
+        "backbone": backbone,
+        "defaults": teach_defaults(backbone),
+        "values": {key: rt.global_teach.get(key) for key in TEACH_TUNABLE},
+        "effective": {key: merge_teach(backbone, rt.global_teach, None)[key] for key in TEACH_TUNABLE},
+    }
+
+
+@router.get("/teach-limits")
+def get_teach_limits(request: Request) -> dict:
+    return _teach_view(runtime(request))
+
+
+@router.put("/teach-limits")
+async def put_teach_limits(body: TeachLimits, request: Request) -> dict:
+    rt = runtime(request)
+    check_teach(merge_teach(rt.backbone_id, body.stored(), None))
+    await asyncio.to_thread(rt.set_global_teach, body.stored())
+    return _teach_view(rt)
+
+
 # --- review reminder ----------------------------------------------------------
 
 _mlo = {k: v[0] for k, v in REMINDER_LIMITS.items()}
diff --git a/visionstate/backend/visionstate/backbones.json b/visionstate/backend/visionstate/backbones.json
index 8196384..506b26b 100644
--- a/visionstate/backend/visionstate/backbones.json
+++ b/visionstate/backend/visionstate/backbones.json
@@ -11,11 +11,14 @@
       "mean": [0.485, 0.456, 0.406],
       "std": [0.229, 0.224, 0.225],
       "pooling": "cls_mean",
-      "bundled": true
+      "bundled": true,
+      "ram_mb": 60,
+      "time_ms": 70,
+      "accuracy": 3
     },
     "dinov2-small": {
       "name": "DINOv2 small · fp32 (accurate)",
-      "description": "Full precision. Slightly more accurate, about 3x slower. Downloaded on first use (89 MB).",
+      "description": "Full precision. Slightly more accurate and slower, most of all on small CPUs such as a Raspberry Pi. Downloaded on first use (89 MB).",
       "url": "https://huggingface.co/onnx-community/dinov2-small/resolve/8b1f705a3a7f6f062f6bdd21986c1583d3ef105d/onnx/model.onnx",
       "sha256": "f22797eabf810a75e41de68d378541ebea372122b25c4ce3ef25ff618250c20a",
       "size": 88532934,
@@ -23,7 +26,27 @@
       "mean": [0.485, 0.456, 0.406],
       "std": [0.229, 0.224, 0.225],
       "pooling": "cls_mean",
-      "bundled": false
+      "bundled": false,
+      "ram_mb": 120,
+      "time_ms": 85,
+      "accuracy": 4
+    },
+    "dinov2-base": {
+      "name": "DINOv2 base · fp32 (most accurate)",
+      "description": "A larger model: tells similar states and objects apart best, but about three times as slow as DINOv2 small and needs about 400 MB of memory. For a fast PC. Downloaded on first use (347 MB).",
+      "url": "https://huggingface.co/onnx-community/dinov2-base/resolve/31ef06cac16d5d301c5930d147002a058c85a5e4/onnx/model.onnx",
+      "sha256": "320d1012a6fc65b101fc85ca30ee7a47b2e4f6a2e8bd78fb9d7036def0e30cb0",
+      "size": 346627111,
+      "input_size": 224,
+      "mean": [0.485, 0.456, 0.406],
+      "std": [0.229, 0.224, 0.225],
+      "pooling": "cls_mean",
+      "license": "Apache-2.0",
+      "source": "https://huggingface.co/facebook/dinov2-base",
+      "bundled": false,
+      "ram_mb": 390,
+      "time_ms": 245,
+      "accuracy": 5
     }
   }
 }
diff --git a/visionstate/backend/visionstate/backbones.py b/visionstate/backend/visionstate/backbones.py
index 5d8469d..9a9234a 100644
--- a/visionstate/backend/visionstate/backbones.py
+++ b/visionstate/backend/visionstate/backbones.py
@@ -3,7 +3,7 @@
 Available backbones are declared in ``backbones.json``; adding a model is a new entry there.
 Entries are never changed or removed once released, so a stored choice keeps working.
 Run ``python -m visionstate.backbones <dest>`` to download all bundled models (backbones,
-detectors, readers) for the Dockerfile and CI. ``download``/``locate`` work for every kind of spec.
+detectors, readers) for the Dockerfile and CI; ``--all`` adds the ones downloaded on first use. ``download``/``locate`` work for every kind of spec.
 """
 
 from __future__ import annotations
@@ -14,7 +14,7 @@
 import sys
 import threading
 import urllib.request
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Protocol
 
@@ -41,6 +41,27 @@ def sha256(self) -> str: ...
     def filename(self) -> str: ...
 
 
+@dataclass(frozen=True)
+class Footprint:
+    """What a model costs and gives, roughly, for the model comparison in Settings.
+
+    Measured with ONNX Runtime on 4 threads of a desktop CPU (the extra memory once loaded and
+    run, the time of one check); ``accuracy`` ranks the models of one kind from 1 to 5. Texts and
+    these numbers may be updated in a released entry; its file and inputs never change.
+    """
+
+    ram_mb: int = 0
+    time_ms: int = 0
+    accuracy: int = 0
+
+    @classmethod
+    def of(cls, entry: dict) -> Footprint:
+        return cls(int(entry.get("ram_mb", 0)), int(entry.get("time_ms", 0)), int(entry.get("accuracy", 0)))
+
+    def as_dict(self) -> dict:
+        return {"ram_mb": self.ram_mb, "time_ms": self.time_ms, "accuracy": self.accuracy}
+
+
 @dataclass(frozen=True)
 class BackboneSpec:
     id: str
@@ -54,6 +75,9 @@ class BackboneSpec:
     std: tuple[float, float, float]
     pooling: str
     bundled: bool
+    license: str = ""
+    source: str = ""
+    footprint: Footprint = field(default_factory=lambda: Footprint())
 
     @property
     def filename(self) -> str:
@@ -76,6 +100,9 @@ def load_registry() -> tuple[str, dict[str, BackboneSpec]]:
             std=tuple(v["std"]),
             pooling=v.get("pooling", "cls_mean"),
             bundled=bool(v.get("bundled", False)),
+            license=v.get("license", ""),
+            source=v.get("source", ""),
+            footprint=Footprint.of(v),
         )
         for key, v in raw["backbones"].items()
     }
@@ -172,9 +199,11 @@ def _pool(self, hidden: np.ndarray) -> np.ndarray:
     from . import detectors, readers
 
     logging.basicConfig(level=logging.INFO)
-    destination = Path(sys.argv[1] if len(sys.argv) > 1 else "models")
-    # Every bundled model: backbones (states), detectors (objects), readers and wheel readers (readings).
+    args = [a for a in sys.argv[1:] if a != "--all"]
+    destination = Path(args[0] if args else "models")
+    # Every bundled model: backbones (states), detectors (objects), readers and wheel readers
+    # (readings); with --all also the ones downloaded on first use, for the tests of every model.
     specs = [*BACKBONES.values(), *detectors.DETECTORS.values(), *readers.READERS.values()]
     for spec in [*specs, *readers.WHEEL_READERS.values()]:
-        if spec.bundled:
+        if spec.bundled or "--all" in sys.argv:
             print(download(spec, destination))
diff --git a/visionstate/backend/visionstate/detectors.json b/visionstate/backend/visionstate/detectors.json
index ba8ad12..86f7577 100644
--- a/visionstate/backend/visionstate/detectors.json
+++ b/visionstate/backend/visionstate/detectors.json
@@ -507,7 +507,10 @@
       "label_set": "coco",
       "license": "Apache-2.0",
       "source": "https://huggingface.co/ustc-community/dfine-small-coco",
-      "bundled": true
+      "bundled": true,
+      "ram_mb": 230,
+      "time_ms": 100,
+      "accuracy": 3
     },
     "dfine-n-coco": {
       "name": "D-FINE N (fast)",
@@ -519,7 +522,40 @@
       "label_set": "coco",
       "license": "Apache-2.0",
       "source": "https://huggingface.co/ustc-community/dfine-nano-coco",
-      "bundled": false
+      "bundled": false,
+      "ram_mb": 100,
+      "time_ms": 40,
+      "accuracy": 2
+    },
+    "dfine-m-coco": {
+      "name": "D-FINE M (accurate)",
+      "description": "Finds more small, far-off and partly hidden objects than D-FINE S, but is about three times as slow. For a fast PC. Downloaded on first use (79 MB).",
+      "url": "https://huggingface.co/onnx-community/dfine_m_coco-ONNX/resolve/489756db2825cb068a588fe930af239a656d1fe1/onnx/model.onnx",
+      "sha256": "70aaa837978a06ba44ad17398c7079ae5a1a7b1a9032b5d7053981e1ada02d6b",
+      "size": 78624257,
+      "input_size": 640,
+      "label_set": "coco",
+      "license": "Apache-2.0",
+      "source": "https://huggingface.co/ustc-community/dfine-medium-coco",
+      "bundled": false,
+      "ram_mb": 300,
+      "time_ms": 290,
+      "accuracy": 4
+    },
+    "dfine-l-coco": {
+      "name": "D-FINE L (most accurate)",
+      "description": "The most accurate, about four times as slow as D-FINE S. For a fast PC. Downloaded on first use (125 MB).",
+      "url": "https://huggingface.co/onnx-community/dfine_l_coco-ONNX/resolve/e09b218185400510b68d067bbd0c9379d905ffcd/onnx/model.onnx",
+      "sha256": "d678f3baebfb909d3a20f21d1d807544d0172ed47fa1ab88e7fcdec7e365b236",
+      "size": 125348332,
+      "input_size": 640,
+      "label_set": "coco",
+      "license": "Apache-2.0",
+      "source": "https://huggingface.co/ustc-community/dfine-large-coco",
+      "bundled": false,
+      "ram_mb": 350,
+      "time_ms": 420,
+      "accuracy": 5
     }
   }
 }
diff --git a/visionstate/backend/visionstate/detectors.py b/visionstate/backend/visionstate/detectors.py
index 15ccadb..695529b 100644
--- a/visionstate/backend/visionstate/detectors.py
+++ b/visionstate/backend/visionstate/detectors.py
@@ -9,13 +9,13 @@
 
 import json
 import threading
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from pathlib import Path
 
 import numpy as np
 from PIL import Image
 
-from .backbones import cpu_session
+from .backbones import Footprint, cpu_session
 from .settings import DETECTION
 
 REGISTRY_FILE = Path(__file__).with_name("detectors.json")
@@ -44,6 +44,7 @@ class DetectorSpec:
     license: str
     source: str
     bundled: bool
+    footprint: Footprint = field(default_factory=lambda: Footprint())
 
     @property
     def filename(self) -> str:
@@ -85,6 +86,7 @@ def load_registry() -> tuple[str, dict[str, DetectorSpec], dict[str, LabelSet]]:
             license=v.get("license", ""),
             source=v.get("source", ""),
             bundled=bool(v.get("bundled", False)),
+            footprint=Footprint.of(v),
         )
         for key, v in raw["detectors"].items()
     }
diff --git a/visionstate/backend/visionstate/engine/base.py b/visionstate/backend/visionstate/engine/base.py
index f584902..23f6ae0 100644
--- a/visionstate/backend/visionstate/engine/base.py
+++ b/visionstate/backend/visionstate/engine/base.py
@@ -19,7 +19,7 @@
 from ..ha_events import HaEventListener
 from ..lights import Lights
 from ..mqtt import MqttBridge
-from ..settings import RUNTIME, Settings, merge_review
+from ..settings import RUNTIME, Settings, merge_review, merge_teach
 from ..sources import FrameGrabber, HomeAssistant
 from ..storage import Storage
 from .state import LiveState, SensorConfig
@@ -44,6 +44,7 @@ def __init__(self, settings: Settings, db: Database):
         self.ha_entity_ids: dict[str, str] = {}
         self._registry_wanted = asyncio.Event()  # discovery was published: read the registry again
         self.global_review: dict = {}  # global review rules (DB setting "review")
+        self.global_teach: dict = {}  # the user's teaching limits for all sensors (DB setting "teach")
         self.storage_rules: dict = {}  # history limits (DB setting "storage"); see storage_limits()
         self.history_trimmed = False  # frames were removed to stay under the size limit (until limits change)
         self.reminder_rules: dict = {}  # the review reminder (DB setting "reminder"); see reminder_settings()
@@ -124,3 +125,12 @@ def wake(self, sensor_id: int, force: bool = False, paused_too: bool = False) ->
 
     def review_rules(self, cfg: SensorConfig) -> dict:
         return merge_review(self.global_review, cfg.review)
+
+    @property
+    def backbone_id(self) -> str:
+        """The backbone in use (or chosen, while it loads)."""
+        return self.embedder.spec.id if self.embedder else self.db.get_text("backbone", backbones.DEFAULT_BACKBONE)
+
+    def teach_limits(self, cfg: SensorConfig) -> dict:
+        """An object sensor's teaching limits: the backbone's defaults < global < the sensor's own."""
+        return merge_teach(self.backbone_id, self.global_teach, cfg.objects.get("teach"))
diff --git a/visionstate/backend/visionstate/engine/objects.py b/visionstate/backend/visionstate/engine/objects.py
index 37d0915..8c55640 100644
--- a/visionstate/backend/visionstate/engine/objects.py
+++ b/visionstate/backend/visionstate/engine/objects.py
@@ -33,7 +33,7 @@ async def _run_objects(self, cfg: SensorConfig, image: Image.Image) -> None:
         index = await self.taught_index(cfg)
         rescue = bool(index and index.rescue)
         # Once a box the detector missed was taught, its unsure boxes are looked at too.
-        floor = min(cfg.threshold, TEACH["rescue_floor"]) if rescue else cfg.threshold
+        floor = min(cfg.threshold, self.teach_limits(cfg)["rescue_floor"]) if rescue else cfg.threshold
         try:
             found = await self.detect_objects(image, cfg.roi, cfg.objects, floor, all_classes=rescue)
         except Exception as err:  # noqa: BLE001 - detector missing or failed to load
diff --git a/visionstate/backend/visionstate/engine/runtime.py b/visionstate/backend/visionstate/engine/runtime.py
index 5dd3bbc..871b67f 100644
--- a/visionstate/backend/visionstate/engine/runtime.py
+++ b/visionstate/backend/visionstate/engine/runtime.py
@@ -39,6 +39,7 @@ async def start(self) -> None:
         MQTT, the Home Assistant event stream and the housekeeping loops."""
         self._loop = asyncio.get_running_loop()
         self.global_review = await asyncio.to_thread(self.db.get_dict, "review")
+        self.global_teach = await asyncio.to_thread(self.db.get_dict, "teach")
         self.storage_rules = await asyncio.to_thread(self.db.get_dict, "storage")
         self.reminder_rules = await asyncio.to_thread(self.db.get_dict, "reminder")
         self.reminder_sent_at = await asyncio.to_thread(self.db.get_setting, "reminder_sent_at")
@@ -146,6 +147,11 @@ def set_global_review(self, rules: dict) -> None:
         self.global_review = dict(rules)
         self.db.set_setting("review", self.global_review)
 
+    def set_global_teach(self, limits: dict) -> None:
+        """The user's teaching limits for all sensors; only the ones set (not None) are kept."""
+        self.global_teach = {k: v for k, v in limits.items() if v is not None}
+        self.db.set_setting("teach", self.global_teach)
+
     def _trigger_index(self) -> dict[str, set[int]]:
         index: dict[str, set[int]] = {}
         with self.db.session() as s:
diff --git a/visionstate/backend/visionstate/engine/teaching.py b/visionstate/backend/visionstate/engine/teaching.py
index 64e6f04..b1c21dc 100644
--- a/visionstate/backend/visionstate/engine/teaching.py
+++ b/visionstate/backend/visionstate/engine/teaching.py
@@ -107,7 +107,16 @@ async def apply_taught(
             vectors = await asyncio.to_thread(teach.embed, embedder, crops)
         n = len(checked)
         return teach.apply(
-            found, checked, vectors[:n], rescue, vectors[n:], index, cfg.objects["classes"], cfg.parents, previous
+            found,
+            checked,
+            vectors[:n],
+            rescue,
+            vectors[n:],
+            index,
+            cfg.objects["classes"],
+            cfg.parents,
+            previous,
+            self.teach_limits(cfg),
         )
 
     def taught_changed(self, sensor_id: int) -> None:
diff --git a/visionstate/backend/visionstate/readers.json b/visionstate/backend/visionstate/readers.json
index e951cd0..ee3363d 100644
--- a/visionstate/backend/visionstate/readers.json
+++ b/visionstate/backend/visionstate/readers.json
@@ -12,7 +12,10 @@
       "chars": {"0": 33, "1": 34, "2": 35, "3": 36, "4": 37, "5": 38, "6": 39, "7": 40, "8": 41, "9": 42, ".": 14, ",": 12, ":": 16, "-": 13},
       "license": "Apache-2.0",
       "source": "https://huggingface.co/PaddlePaddle/PP-OCRv6_tiny_rec_onnx",
-      "bundled": true
+      "bundled": true,
+      "ram_mb": 20,
+      "time_ms": 3,
+      "accuracy": 3
     },
     "ppocrv6-small": {
       "name": "PP-OCRv6 small",
@@ -25,7 +28,10 @@
       "chars": {"0": 33, "1": 34, "2": 35, "3": 36, "4": 37, "5": 38, "6": 39, "7": 40, "8": 41, "9": 42, ".": 14, ",": 12, ":": 16, "-": 13},
       "license": "Apache-2.0",
       "source": "https://huggingface.co/PaddlePaddle/PP-OCRv6_small_rec_onnx",
-      "bundled": false
+      "bundled": false,
+      "ram_mb": 40,
+      "time_ms": 8,
+      "accuracy": 4
     }
   },
   "wheel_readers": {
@@ -42,7 +48,10 @@
         "bins": 100,
         "license": "Apache-2.0",
         "source": "https://huggingface.co/oleost/visionstate-wheel-reader",
-        "bundled": true
+        "bundled": true,
+        "ram_mb": 12,
+        "time_ms": 5,
+        "accuracy": 3
       }
     }
   }
diff --git a/visionstate/backend/visionstate/readers.py b/visionstate/backend/visionstate/readers.py
index e9cfab0..14df99d 100644
--- a/visionstate/backend/visionstate/readers.py
+++ b/visionstate/backend/visionstate/readers.py
@@ -19,13 +19,13 @@
 import re
 import threading
 from collections import deque
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from pathlib import Path
 
 import numpy as np
 from PIL import Image, ImageFilter, ImageOps
 
-from .backbones import cpu_session
+from .backbones import Footprint, cpu_session
 from .settings import READING
 
 REGISTRY_FILE = Path(__file__).with_name("readers.json")
@@ -46,6 +46,7 @@ class ReaderSpec:
     license: str
     source: str
     bundled: bool
+    footprint: Footprint = field(default_factory=lambda: Footprint())
 
     @property
     def filename(self) -> str:
@@ -69,6 +70,7 @@ def load_registry() -> tuple[str, dict[str, ReaderSpec]]:
             license=v.get("license", ""),
             source=v.get("source", ""),
             bundled=bool(v.get("bundled", False)),
+            footprint=Footprint.of(v),
         )
         for key, v in raw["readers"].items()
     }
@@ -270,6 +272,7 @@ class WheelReaderSpec:
     license: str
     source: str
     bundled: bool
+    footprint: Footprint = field(default_factory=lambda: Footprint())
 
     @property
     def filename(self) -> str:
@@ -293,6 +296,7 @@ def load_wheel_registry() -> tuple[str, dict[str, WheelReaderSpec]]:
             license=v.get("license", ""),
             source=v.get("source", ""),
             bundled=bool(v.get("bundled", False)),
+            footprint=Footprint.of(v),
         )
         for key, v in raw["readers"].items()
     }
diff --git a/visionstate/backend/visionstate/settings.py b/visionstate/backend/visionstate/settings.py
index 9e3c1bd..96c21ec 100644
--- a/visionstate/backend/visionstate/settings.py
+++ b/visionstate/backend/visionstate/settings.py
@@ -54,6 +54,7 @@
     # The user's own labels, each a kind of one of the classes: [{"key", "name", "parent"}], e.g.
     # "Our car" under "car". Each gets the same two entities as a class. Made by teaching a box.
     "custom": [],
+    "teach": {},  # this sensor's own values of the tunable teaching limits (TEACH_TUNABLE); missing = global
 }
 OBJECT_LIMITS = {
     "min_size": (0.0, 0.5),
@@ -198,6 +199,25 @@
 }
 NONE_LABEL = "none"  # taught label of a box that is not what the detector said
 
+# The limits of TEACH a user can change: globally (Settings → Teaching object sensors, DB setting
+# "teach") and per sensor (objects["teach"]); empty = inherit. Effective: merge_teach.
+TEACH_TUNABLE = ("match_similarity", "keep_similarity", "ask_similarity", "rescue_similarity", "rescue_floor")
+TEACH_TUNABLE_LIMITS = {
+    "match_similarity": (0.5, 0.99),
+    "keep_similarity": (0.5, 0.99),
+    "ask_similarity": (0.5, 0.99),
+    "rescue_similarity": (0.5, 0.99),
+    "rescue_floor": (0.05, 0.6),
+}
+# Defaults of a backbone whose similarities spread differently from DINOv2 small's (TEACH was
+# measured on that). DINOv2 base, on the CC0 test photos (14 objects; the same object with other
+# light, framing, blur, JPEG or night colours vs other objects of its kind): at 0.88 one pair of
+# different dogs counted as the same (0.891); at 0.90 none did, and 93 % of the changed copies
+# of an object were still recognised, as with DINOv2 small 8-bit at 0.88.
+TEACH_BY_BACKBONE: dict[str, dict[str, float]] = {
+    "dinov2-base": {"match_similarity": 0.9, "rescue_similarity": 0.92},
+}
+
 # --- Triggers: when a sensor checks its camera ---------------------------------
 #
 # 1. The regular interval (SENSOR_DEFAULTS["interval_s"]) is the safety net.
@@ -422,11 +442,25 @@ def merge_reminder(stored: dict | None) -> dict:
     return {**REMINDER_DEFAULTS, **{k: v for k, v in (stored or {}).items() if k in REMINDER_DEFAULTS}}
 
 
+def teach_defaults(backbone: str) -> dict:
+    """The tunable teaching limits as they are by default for ``backbone``."""
+    return {key: TEACH_BY_BACKBONE.get(backbone, {}).get(key, TEACH[key]) for key in TEACH_TUNABLE}
+
+
+def merge_teach(backbone: str, global_limits: dict | None, sensor_overrides: dict | None) -> dict:
+    """Effective teaching limits (all of TEACH): defaults of the backbone < global < sensor overrides."""
+    merged = {**TEACH, **teach_defaults(backbone)}
+    for overrides in (global_limits, sensor_overrides):
+        merged.update({k: v for k, v in (overrides or {}).items() if v is not None and k in TEACH_TUNABLE})
+    return merged
+
+
 def merge_objects(stored: dict | None) -> dict:
     """Object settings of a sensor: stored values on top of OBJECT_DEFAULTS."""
     merged = {**OBJECT_DEFAULTS, **(stored or {})}
     merged["classes"] = list(merged.get("classes") or [])
     merged["custom"] = [dict(label) for label in merged.get("custom") or []]
+    merged["teach"] = {k: v for k, v in (merged.get("teach") or {}).items() if k in TEACH_TUNABLE and v is not None}
     return merged
 
 
diff --git a/visionstate/backend/visionstate/teach.py b/visionstate/backend/visionstate/teach.py
index ae55eea..9f9879f 100644
--- a/visionstate/backend/visionstate/teach.py
+++ b/visionstate/backend/visionstate/teach.py
@@ -177,6 +177,7 @@ def apply(
     classes: list[str],
     parents: dict[str, str],
     previous: list[dict] | None = None,
+    limits: dict | None = None,
 ) -> list[dict]:
     """The boxes of a check after comparing them with the taught ones.
 
@@ -186,16 +187,18 @@ def apply(
     ``previous`` (the boxes of the last check) lets an object that stayed where it was keep its
     answer at a lower similarity (``kept``, see TEACH["keep_similarity"]). A box that may be an
     own label, but not clearly, gets ``ask`` ({id, label, similarity}; see TEACH["ask_similarity"]).
+    ``limits``: the sensor's teaching limits (settings.merge_teach); TEACH when not given.
     """
+    limits = limits or TEACH
     result = [dict(d) for d in found]
     for i, vector in zip(checked, checked_vectors, strict=True):
-        match = nearest(index, vector, TEACH["match_similarity"])
+        match = nearest(index, vector, limits["match_similarity"])
         if match is None:
-            match = _kept(index, vector, result[i]["box"], previous or [])
+            match = _kept(index, vector, result[i]["box"], previous or [], limits["keep_similarity"])
             if match is not None:
                 result[i]["kept"] = True
         if match is None:
-            if ask := _unsure(index, vector, result[i]["key"], parents):
+            if ask := _unsure(index, vector, result[i]["key"], parents, limits["ask_similarity"]):
                 result[i]["ask"] = ask
             continue
         det = result[i]
@@ -211,7 +214,7 @@ def apply(
         if label:
             det["label"] = label
     for cand, vector in zip(rescue, rescue_vectors, strict=True):
-        match = nearest(index, vector, TEACH["rescue_similarity"])
+        match = nearest(index, vector, limits["rescue_similarity"])
         if match is None:
             continue
         key, label = resolve(match.label, parents)
@@ -229,7 +232,9 @@ def apply(
     return result
 
 
-def _kept(index: TaughtIndex, vector: np.ndarray, box: Sequence[float], previous: list[dict]) -> Match | None:
+def _kept(
+    index: TaughtIndex, vector: np.ndarray, box: Sequence[float], previous: list[dict], keep_similarity: float
+) -> Match | None:
     """The answer a box got in the last check, when it is the same object at the same place and
     still close to that taught label (just below match_similarity, e.g. in other light)."""
     before = [
@@ -237,13 +242,15 @@ def _kept(index: TaughtIndex, vector: np.ndarray, box: Sequence[float], previous
         for d in previous
         if d.get("match") and not d.get("rescued") and _iou(d["box"], box) >= TEACH["keep_iou"]
     ]
-    match = nearest(index, vector, TEACH["keep_similarity"]) if before else None
+    match = nearest(index, vector, keep_similarity) if before else None
     return match if match is not None and match.label in before else None
 
 
-def _unsure(index: TaughtIndex, vector: np.ndarray, key: str, parents: dict[str, str]) -> dict | None:
+def _unsure(
+    index: TaughtIndex, vector: np.ndarray, key: str, parents: dict[str, str], ask_similarity: float
+) -> dict | None:
     """The own label (of the box's class) a box is close to but not clearly, to ask about."""
-    near = nearest(index, vector, TEACH["ask_similarity"])
+    near = nearest(index, vector, ask_similarity)
     if near is None:
         return None
     parent, label = resolve(near.label, parents)
diff --git a/visionstate/config.yaml b/visionstate/config.yaml
index ccf9e20..6eed966 100644
--- a/visionstate/config.yaml
+++ b/visionstate/config.yaml
@@ -1,5 +1,5 @@
 name: VisionState (beta)
-version: "0.7.1b6"
+version: "0.8.1b1"
 slug: visionstate
 description: Teach a local AI to recognise states (open, closed, …), find objects (people, cars, pets) or read numbers in your camera images
 url: https://github.com/oleost/VisionState
diff --git a/visionstate/frontend/e2e/ui.spec.ts b/visionstate/frontend/e2e/ui.spec.ts
index dcd222e..fdf74f4 100644
--- a/visionstate/frontend/e2e/ui.spec.ts
+++ b/visionstate/frontend/e2e/ui.spec.ts
@@ -1,5 +1,5 @@
 // UI tests, run once on desktop (mouse) and once on a phone (touch). See playwright.config.ts.
-import { expect, test, type APIRequestContext, type Page, type TestInfo } from '@playwright/test';
+import { expect, test, type APIRequestContext, type Locator, type Page, type TestInfo } from '@playwright/test';
 import path from 'node:path';
 import { CAMERA_URL, COUNTER_BOX, COUNTER_URL, DISPLAY_URL, PHOTO_URL, READING_SENSOR_NAME } from './env';
 import {
@@ -21,6 +21,12 @@ import {
   watchErrors,
 } from './helpers';
 
+/** Scrolls to the middle of the screen first, so a tap never lands under the sticky top bar. */
+async function pressCentered(locator: Locator, info: TestInfo) {
+  await locator.evaluate((el) => el.scrollIntoView({ block: 'center' }));
+  await press(locator, info);
+}
+
 /** Every page: [screenshot name, route, text that shows it is ready]. */
 async function allPages(request: APIRequestContext): Promise<[string, string, RegExp | string][]> {
   const id = await seededSensorId(request);
@@ -658,12 +664,36 @@ test('teaching an object sensor: correct a box, draw a missed one, forget it all
     await expectNoClipping(page, '.btn, .chip, .card');
     await page.screenshot({ path: path.join('test-results', 'pages', info.project.name, 'objects-quality.png'), fullPage: true });
 
-    // Settings: turn it off or forget it all; then the Quality tab goes away again.
+    // Settings: its own teaching limits. Until set, a field shows the global value in grey.
     await page.goto(`#/sensors/${id}/settings`);
     await expect(page.getByText('Use what you taught')).toBeVisible();
     // The region's camera frame loads above: wait for it, or the page moves under the tap.
     await expect(page.locator('.roi img')).toBeVisible();
     await page.waitForFunction(() => [...document.images].every((img) => img.complete));
+    const global = await (await request.get('api/v1/teach-limits')).json();
+    const limits = page.getByTestId('sensor-teach-limits');
+    const match = limits.getByRole('spinbutton', { name: 'Recognised as a box you taught from' });
+    await expect(match).toHaveValue('');
+    await expect(match).toHaveAttribute('placeholder', String(Math.round(global.effective.match_similarity * 100)));
+    await expect(limits.getByRole('button', { name: /^Use the global value/ }).first()).toBeHidden();
+    await expectNoHorizontalOverflow(page);
+    await expectNoClipping(page, '.btn, .card');
+    await match.fill('80');
+    // The toast from teaching the missed box still covers the bottom of a phone screen: let it go.
+    await expect(page.getByRole('button', { name: 'Dismiss' })).toHaveCount(0, { timeout: 20_000 });
+    await pressCentered(page.getByRole('button', { name: 'Save changes' }), info);
+    await expect(page.getByText('Settings saved')).toBeVisible();
+    expect((await (await request.get(`api/v1/sensors/${id}`)).json()).objects.teach).toEqual({ match_similarity: 0.8 });
+    await page.reload();
+    await expect(match).toHaveValue('80');
+    await match.scrollIntoViewIfNeeded();
+    await page.screenshot({ path: path.join('test-results', 'pages', info.project.name, 'objects-teach-limits.png'), fullPage: true });
+    await pressCentered(limits.getByRole('button', { name: /^Use the global value/ }), info);
+    await expect(match).toHaveValue('');
+    await pressCentered(page.getByRole('button', { name: 'Save changes' }), info);
+    await expect.poll(async () => (await (await request.get(`api/v1/sensors/${id}`)).json()).objects.teach).toEqual({});
+
+    // Turn it off or forget it all; then the Quality tab goes away again.
     await press(page.getByRole('button', { name: 'Forget all' }), info);
     await press(page.getByRole('button', { name: 'Tap again to forget everything taught' }), info);
     await expect(page.getByText('Everything taught is forgotten')).toBeVisible();
@@ -1022,6 +1052,44 @@ test('the wheel reader shows in Settings and is offered to counters read as text
   }
 });
 
+test('the AI models can be compared, and the teaching limits set for all object sensors', async ({ page, request }, info) => {
+  const errors = watchErrors(page);
+  try {
+    await page.goto('#/settings');
+    await press(page.getByRole('button', { name: 'Compare the models' }), info);
+    const compare = page.getByTestId('model-compare');
+    await expect(compare.getByText('D-FINE L (most accurate)')).toBeVisible();
+    await expect(compare.getByText('DINOv2 base · fp32 (most accurate)')).toBeVisible();
+    await expect(compare.getByText('in use')).toHaveCount(4); // one per kind
+    await expectNoHorizontalOverflow(page);
+    await expectNoClipping(page, '.btn, .card');
+
+    const view = await (await request.get('api/v1/teach-limits')).json();
+    const card = page.getByTestId('teach-limits');
+    const ask = card.getByRole('spinbutton', { name: 'Asks you in the review queue from' });
+    await expect(ask).toHaveValue('');
+    await expect(ask).toHaveAttribute('placeholder', String(Math.round(view.defaults.ask_similarity * 100)));
+    const save = card.getByRole('button', { name: 'Save teaching limits' });
+    await expect(save).toBeDisabled(); // nothing changed yet
+    await ask.fill('95'); // above "Recognised as…": not allowed
+    await expect(card.getByText('must not be above the first')).toBeVisible();
+    await ask.fill('70');
+    await expect(card.getByText('must not be above the first')).toBeHidden();
+    await pressCentered(save, info);
+    await expect(page.getByText('Teaching limits saved')).toBeVisible();
+    expect((await (await request.get('api/v1/teach-limits')).json()).values.ask_similarity).toBe(0.7);
+    await card.scrollIntoViewIfNeeded();
+    await page.screenshot({ path: path.join('test-results', 'pages', info.project.name, 'settings-models.png'), fullPage: true });
+    await pressCentered(card.getByRole('button', { name: /^Use the default of/ }), info);
+    await expect(ask).toHaveValue('');
+    await pressCentered(save, info);
+    await expect.poll(async () => (await (await request.get('api/v1/teach-limits')).json()).values.ask_similarity).toBeNull();
+    errors.expectNone();
+  } finally {
+    await request.put('api/v1/teach-limits', { data: {} });
+  }
+});
+
 test('storage limits can be changed', async ({ page }, info) => {
   const errors = watchErrors(page);
   await page.goto('#/settings');
diff --git a/visionstate/frontend/src/lib/api.ts b/visionstate/frontend/src/lib/api.ts
index dfcab90..afc7a9c 100644
--- a/visionstate/frontend/src/lib/api.ts
+++ b/visionstate/frontend/src/lib/api.ts
@@ -24,6 +24,8 @@ import type {
   Status,
   StorageInfo,
   Taught,
+  TeachLimitsInfo,
+  TeachOverrides,
   TeachInput,
 } from './types';
 
@@ -202,6 +204,8 @@ export const api = {
     `${BASE}history/${predictionId}/image?size=${size}`,
   exportUrl: (id: number) => `${BASE}sensors/${id}/export`,
 
+  teachLimits: () => request<TeachLimitsInfo>('teach-limits'),
+  saveTeachLimits: (values: TeachOverrides) => request<TeachLimitsInfo>('teach-limits', send('PUT', values)),
   reviewRules: () => request<ReviewRules>('review-rules'),
   saveReviewRules: (rules: ReviewRules) => request<ReviewRules>('review-rules', send('PUT', rules)),
   reviewReminder: () => request<ReminderInfo>('review-reminder'),
diff --git a/visionstate/frontend/src/lib/components/ModelCompare.svelte b/visionstate/frontend/src/lib/components/ModelCompare.svelte
new file mode 100644
index 0000000..f677887
--- /dev/null
+++ b/visionstate/frontend/src/lib/components/ModelCompare.svelte
@@ -0,0 +1,105 @@
+<script lang="ts">
+  // The AI models side by side, per kind: what they are for, rough memory and time, and how
+  // accurate they are compared with each other. Numbers come from the registries (GET /settings).
+  import { mb } from '../format';
+  import type { BackboneInfo, SettingsInfo } from '../types';
+  import { MODEL_COMPARE_NOTE, MODEL_KINDS } from '../ui';
+  import Icon from './Icon.svelte';
+
+  let { info }: { info: SettingsInfo } = $props();
+  let open = $state(false);
+
+  const chosen = (kind: (typeof MODEL_KINDS)[number]['key']) =>
+    ({ backbones: info.backbone, detectors: info.detector, readers: info.reader, wheel_readers: info.wheel_reader })[kind];
+  const time = (ms: number) => (ms < 1000 ? `${ms} ms` : `${(ms / 1000).toFixed(1)} s`);
+  const ram = (m: BackboneInfo) => (m.ram_mb >= 1000 ? `${(m.ram_mb / 1000).toFixed(1)} GB` : `${m.ram_mb} MB`);
+</script>
+
+<div class="col compare">
+  <button class="btn sm ghost toggle" onclick={() => (open = !open)} aria-expanded={open}>
+    <Icon name="chevron" size={14} />
+    Compare the models
+  </button>
+  {#if open}
+    <div class="col kinds" data-testid="model-compare">
+      {#each MODEL_KINDS as kind (kind.key)}
+        <div class="col kind">
+          <strong class="small">{kind.title}</strong>
+          <span class="xsmall faint">{kind.text}</span>
+          {#each info[kind.key] as m (m.id)}
+            <div class="model" class:current={m.id === chosen(kind.key)}>
+              <span class="name small">
+                {m.name}{#if m.id === chosen(kind.key)}<span class="xsmall muted"> · in use</span>{/if}
+              </span>
+              <span class="facts xsmall muted">
+                <span>~{ram(m)} RAM</span>
+                <span>~{time(m.time_ms)} per check</span>
+                <span>{m.installed ? 'on disk' : `download ${mb(m.size)}`}</span>
+                <span class="accuracy" aria-label={`Accuracy ${m.accuracy} of 5`} title="Accuracy compared with the others">
+                  {#each [1, 2, 3, 4, 5] as n (n)}<span class="pip" class:on={n <= m.accuracy}></span>{/each}
+                </span>
+              </span>
+            </div>
+          {/each}
+        </div>
+      {/each}
+      <p class="xsmall faint">{MODEL_COMPARE_NOTE}</p>
+    </div>
+  {/if}
+</div>
+
+<style>
+  .compare {
+    gap: var(--space-3);
+  }
+  .toggle {
+    align-self: flex-start;
+  }
+  .toggle :global(svg) {
+    transform: rotate(0deg);
+    transition: transform 0.15s;
+  }
+  .toggle[aria-expanded='true'] :global(svg) {
+    transform: rotate(90deg);
+  }
+  .kinds {
+    gap: var(--space-4);
+  }
+  .kind {
+    gap: var(--space-2);
+  }
+  .model {
+    display: flex;
+    flex-direction: column;
+    gap: 2px;
+    padding: var(--space-2) var(--space-3);
+    border-radius: var(--radius-md);
+    background: var(--c-surface-2);
+    border: 1px solid transparent;
+  }
+  .model.current {
+    border-color: var(--c-accent-line);
+  }
+  .name {
+    color: var(--c-text);
+  }
+  .facts {
+    display: flex;
+    flex-wrap: wrap;
+    align-items: center;
+    gap: var(--space-1) var(--space-3);
+  }
+  .accuracy {
+    display: inline-flex;
+    gap: 3px;
+  }
+  .pip {
+    width: 8px;
+    height: 8px;
+    border-radius: var(--radius-pill);
+    background: var(--c-border-strong);
+  }
+  .pip.on {
+    background: var(--c-accent);
+  }
+</style>
diff --git a/visionstate/frontend/src/lib/components/TeachLimitsEditor.svelte b/visionstate/frontend/src/lib/components/TeachLimitsEditor.svelte
new file mode 100644
index 0000000..5ee07e5
--- /dev/null
+++ b/visionstate/frontend/src/lib/components/TeachLimitsEditor.svelte
@@ -0,0 +1,111 @@
+<script lang="ts">
+  // Edits the teaching limits of object sensors. An empty field inherits (shown in grey): on the
+  // Settings page the state model's default, on a sensor the global value.
+  import { app } from '../app.svelte';
+  import type { TeachKey, TeachLimits, TeachOverrides } from '../types';
+  import { TEACH_FIELDS } from '../ui';
+  import Icon from './Icon.svelte';
+
+  let {
+    value = $bindable(),
+    inherited,
+    inheritedFrom,
+  }: {
+    value: TeachOverrides;
+    /** What an empty field uses. */
+    inherited: TeachLimits;
+    /** Where that comes from, e.g. "the global value". */
+    inheritedFrom: string;
+  } = $props();
+
+  const limits = $derived(app.config?.teach.limits);
+  const pctOf = (n: number) => Math.round(n * 100);
+  const effective = (key: TeachKey) => value[key] ?? inherited[key];
+  const conflict = $derived(
+    effective('ask_similarity') > effective('match_similarity') || effective('keep_similarity') > effective('match_similarity'),
+  );
+
+  function set(key: TeachKey, raw: string) {
+    value[key] = raw.trim() === '' ? null : Number(raw) / 100;
+  }
+</script>
+
+<div class="col limits">
+  {#if limits}
+    {#each TEACH_FIELDS as f (f.key)}
+      {@const own = value[f.key] !== null && value[f.key] !== undefined}
+      <div class="line" data-testid={`teach-${f.key}`}>
+        <label class="col name" for={`teach-${f.key}`}>
+          <span>{f.label}</span>
+          <span class="xsmall faint">{f.help}</span>
+        </label>
+        <span class="row input-row">
+          <input
+            id={`teach-${f.key}`}
+            class="input sm num"
+            class:own
+            type="number"
+            min={pctOf(limits[f.key][0])}
+            max={pctOf(limits[f.key][1])}
+            step="1"
+            value={own ? pctOf(value[f.key] as number) : ''}
+            placeholder={String(pctOf(inherited[f.key]))}
+            oninput={(e) => set(f.key, e.currentTarget.value)}
+            aria-label={f.label}
+          />
+          <span class="unit small muted">%</span>
+          <button
+            class="btn sm ghost reset"
+            class:hidden={!own}
+            disabled={!own}
+            onclick={() => (value[f.key] = null)}
+            aria-label={`Use ${inheritedFrom} (${pctOf(inherited[f.key])}%)`}
+            title={`Use ${inheritedFrom}`}
+          >
+            <Icon name="x" size={14} />
+          </button>
+        </span>
+      </div>
+    {/each}
+  {/if}
+  {#if conflict}
+    <div class="notice warn small">The second and third values must not be above the first.</div>
+  {/if}
+  <p class="xsmall faint">Empty fields (in grey) use {inheritedFrom}. 100% is the very same picture.</p>
+</div>
+
+<style>
+  .limits {
+    gap: var(--space-3);
+  }
+  .line {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    gap: var(--space-4);
+    color: var(--c-text-2);
+  }
+  .name {
+    gap: 2px;
+    min-width: 0;
+  }
+  .input-row {
+    gap: var(--space-2);
+    flex-shrink: 0;
+  }
+  .num {
+    width: 72px;
+  }
+  .num::placeholder {
+    color: var(--c-faint);
+  }
+  .num:not(.own) {
+    color: var(--c-faint);
+  }
+  .unit {
+    width: 14px;
+  }
+  .reset.hidden {
+    visibility: hidden;
+  }
+</style>
diff --git a/visionstate/frontend/src/lib/types.ts b/visionstate/frontend/src/lib/types.ts
index 7334574..1c29216 100644
--- a/visionstate/frontend/src/lib/types.ts
+++ b/visionstate/frontend/src/lib/types.ts
@@ -199,6 +199,21 @@ export interface ObjectSettings {
   clear_after_s: number;
   /** Compare boxes with what was taught; off = the detector alone. */
   use_taught: boolean;
+  /** This sensor's own teaching limits; a missing one follows the global value. */
+  teach?: TeachOverrides;
+}
+
+export type TeachKey = 'match_similarity' | 'keep_similarity' | 'ask_similarity' | 'rescue_similarity' | 'rescue_floor';
+export type TeachLimits = Record<TeachKey, number>;
+/** Teaching limits that may be unset (null): globally the state model's default, on a sensor the global value. */
+export type TeachOverrides = Partial<Record<TeachKey, number | null>>;
+
+/** GET /teach-limits: the global teaching limits. */
+export interface TeachLimitsInfo {
+  backbone: string;
+  defaults: TeachLimits;
+  values: TeachOverrides;
+  effective: TeachLimits;
 }
 
 export interface ObjectLive {
@@ -370,7 +385,7 @@ export interface AppConfig {
   object_labels: ObjectLabel[];
   object_popular: string[];
   /** Teaching object sensors: the label of a box that is not what the detector said. */
-  teach: { none_label: string; max_labels: number };
+  teach: { none_label: string; max_labels: number; tunable: TeachKey[]; limits: Record<TeachKey, [number, number]> };
   reading_sensor_defaults: { interval_s: number; threshold: number; debounce: number };
   reading_defaults: ReadingSettings;
   reading_limits: Record<'decimals' | 'digits' | 'max_step' | 'spot_rate' | 'rate_window_min', [number, number]>;
@@ -527,6 +542,13 @@ export interface BackboneInfo {
   description: string;
   installed: boolean;
   size: number;
+  license: string;
+  source: string;
+  /** Rough extra memory once loaded, and the time of one check (4 threads of a desktop CPU). */
+  ram_mb: number;
+  time_ms: number;
+  /** Rank within its kind, 1-5. */
+  accuracy: number;
 }
 
 /** GET /storage: disk use and the history limits (whichever is reached first applies). */
@@ -542,10 +564,7 @@ export interface StorageInfo {
   history_max_gb: number;
 }
 
-export interface DetectorInfo extends BackboneInfo {
-  license: string;
-  source: string;
-}
+export type DetectorInfo = BackboneInfo;
 
 export interface SettingsInfo {
   backbone: string;
diff --git a/visionstate/frontend/src/lib/ui.ts b/visionstate/frontend/src/lib/ui.ts
index 40e88f5..b257916 100644
--- a/visionstate/frontend/src/lib/ui.ts
+++ b/visionstate/frontend/src/lib/ui.ts
@@ -58,6 +58,53 @@ export const REVIEW_FIELDS = [
   { key: 'spot_rate', label: 'Random spot checks of confident frames', unit: '%', scale: 100, step: 0.5 },
 ] as const;
 
+/** The teaching limits a user can change (settings.TEACH_TUNABLE), in the order shown. */
+export const TEACH_FIELDS = [
+  {
+    key: 'match_similarity',
+    label: 'Recognised as a box you taught from',
+    help: 'How close a box must be to one you taught to take its answer.',
+  },
+  {
+    key: 'keep_similarity',
+    label: 'Keeps that answer, while it stays in place, down to',
+    help: 'A parked car stays "Our car" when the light changes a little.',
+  },
+  {
+    key: 'ask_similarity',
+    label: 'Asks you in the review queue from',
+    help: 'Between this and the first value, a box that may be one of your own labels is asked about.',
+  },
+  {
+    key: 'rescue_similarity',
+    label: 'Finds a missed object again from',
+    help: 'Once you drew a box the AI missed, unsure boxes this close to it count.',
+  },
+  {
+    key: 'rescue_floor',
+    label: 'Looks again at AI boxes down to',
+    help: 'The weakest AI confidence looked at for missed objects.',
+  },
+] as const;
+
+/** The model comparison under Settings → AI model: one row per kind, in the order shown. */
+export const MODEL_KINDS = [
+  {
+    key: 'backbones',
+    title: 'State sensors',
+    text: 'Turns the region into numbers that a small model per sensor learns your states from. A larger model tells similar states (and taught objects) apart better.',
+  },
+  {
+    key: 'detectors',
+    title: 'Object sensors',
+    text: 'Finds people, vehicles and animals. Larger models find more small, far-off and partly hidden objects.',
+  },
+  { key: 'readers', title: 'Displays', text: 'Reads the digits of a display; small helps on unusual fonts.' },
+  { key: 'wheel_readers', title: 'Mechanical counters', text: 'Reads each wheel of a counter.' },
+] as const;
+export const MODEL_COMPARE_NOTE =
+  'Rough numbers: extra memory once loaded, and time per check on 4 cores of a desktop PC. A Raspberry Pi 4 takes about 20–40 times as long; a check only runs when it is due or something changed.';
+
 export const SAMPLE_ORIGINS: Record<string, string> = {
   snapshot: 'Snapshot',
   upload: 'Upload',
diff --git a/visionstate/frontend/src/pages/Settings.svelte b/visionstate/frontend/src/pages/Settings.svelte
index d98c40d..c530e8b 100644
--- a/visionstate/frontend/src/pages/Settings.svelte
+++ b/visionstate/frontend/src/pages/Settings.svelte
@@ -2,12 +2,14 @@
   import { api } from '../lib/api';
   import { app, refreshStatus, toast, toastError } from '../lib/app.svelte';
   import Icon from '../lib/components/Icon.svelte';
+  import ModelCompare from '../lib/components/ModelCompare.svelte';
   import ReminderCard from '../lib/components/ReminderCard.svelte';
   import ReviewRulesEditor from '../lib/components/ReviewRulesEditor.svelte';
   import StorageCard from '../lib/components/StorageCard.svelte';
+  import TeachLimitsEditor from '../lib/components/TeachLimitsEditor.svelte';
   import { mb } from '../lib/format';
   import { go, paths } from '../lib/router.svelte';
-  import type { ReviewRules, SettingsInfo } from '../lib/types';
+  import type { ReviewRules, SettingsInfo, TeachLimitsInfo, TeachOverrides } from '../lib/types';
 
   let info = $state<SettingsInfo | null>(null);
   let backbone = $state('');
@@ -17,6 +19,35 @@
   let importInput: HTMLInputElement;
   let reviewRules = $state<ReviewRules | null>(null);
   let savingReview = $state(false);
+  let teach = $state<TeachLimitsInfo | null>(null);
+  let teachValues = $state<TeachOverrides>({});
+  let savingTeach = $state(false);
+  const setOnly = (v: TeachOverrides) => JSON.stringify(Object.entries(v).filter(([, x]) => x !== null && x !== undefined).sort());
+  const teachChanged = $derived(teach !== null && setOnly(teachValues) !== setOnly(teach.values));
+
+  function loadTeach() {
+    api
+      .teachLimits()
+      .then((t) => {
+        teach = t;
+        teachValues = { ...t.values };
+      })
+      .catch(toastError);
+  }
+  loadTeach();
+
+  async function saveTeach() {
+    savingTeach = true;
+    try {
+      teach = await api.saveTeachLimits(teachValues);
+      teachValues = { ...teach.values };
+      toast('Teaching limits saved');
+    } catch (err) {
+      toastError(err);
+    } finally {
+      savingTeach = false;
+    }
+  }
 
   api
     .reviewRules()
@@ -60,6 +91,7 @@
       info = await api.saveSettings({ backbone, detector, reader });
       toast(retrained ? 'AI model changed — state sensors are retraining' : 'AI models changed');
       refreshStatus();
+      if (retrained) loadTeach(); // the defaults depend on the state model
     } catch (err) {
       toastError(err);
     } finally {
@@ -199,6 +231,7 @@
           </button>
           <span class="xsmall faint">A new state model retrains every state sensor from its stored images. Object and reading sensors need no training.</span>
         </div>
+        <ModelCompare {info} />
       {/if}
     </section>
 
@@ -213,6 +246,28 @@
       {/if}
     </section>
 
+    <section class="card pad col" data-testid="teach-limits">
+      <h3>Teaching object sensors</h3>
+      <p class="small muted">
+        How closely a box must resemble the boxes you taught an object sensor. Each sensor can override these on its Settings
+        tab.
+      </p>
+      {#if teach}
+        <TeachLimitsEditor
+          bind:value={teachValues}
+          inherited={teach.defaults}
+          inheritedFrom={`the default of ${info?.backbones.find((b) => b.id === teach?.backbone)?.name ?? 'the state model'}`}
+        />
+        <p class="xsmall faint">
+          The state model also compares the taught boxes, so the defaults follow it. Values you set yourself stay when you
+          switch the state model; check them afterwards.
+        </p>
+        <button class="btn primary" style="align-self:flex-start" disabled={!teachChanged || savingTeach} onclick={saveTeach}>
+          {savingTeach ? 'Saving…' : 'Save teaching limits'}
+        </button>
+      {/if}
+    </section>
+
     <ReminderCard />
 
     <StorageCard />
diff --git a/visionstate/frontend/src/pages/sensor/SettingsTab.svelte b/visionstate/frontend/src/pages/sensor/SettingsTab.svelte
index 936770d..16902eb 100644
--- a/visionstate/frontend/src/pages/sensor/SettingsTab.svelte
+++ b/visionstate/frontend/src/pages/sensor/SettingsTab.svelte
@@ -16,12 +16,13 @@
   import SensorParams from '../../lib/components/SensorParams.svelte';
   import SourcePicker from '../../lib/components/SourcePicker.svelte';
   import StatesEditor from '../../lib/components/StatesEditor.svelte';
+  import TeachLimitsEditor from '../../lib/components/TeachLimitsEditor.svelte';
   import TriggersEditor from '../../lib/components/TriggersEditor.svelte';
   import { isObjectSensor } from '../../lib/objects';
   import { isReadingSensor } from '../../lib/reading';
   import { isPolygon, toRectangle } from '../../lib/roi';
   import { go, href, paths } from '../../lib/router.svelte';
-  import type { ReviewRules, Roi, Sensor } from '../../lib/types';
+  import type { ReviewRules, Roi, Sensor, TeachLimitsInfo, TeachOverrides } from '../../lib/types';
   import { REDACTED_MARK } from '../../lib/ui';
 
   let { sensor, onchange }: { sensor: Sensor; onchange: () => void } = $props();
@@ -44,6 +45,9 @@
   let clearAfter = $state(initial.objects?.clear_after_s ?? 0);
   let minSize = $state(initial.objects?.min_size ?? 0);
   let useTaught = $state(initial.objects?.use_taught ?? true);
+  let teachLimits = $state<TeachOverrides>({ ...(initial.objects?.teach ?? {}) });
+  let globalTeach = $state<TeachLimitsInfo | null>(null);
+  if (objectSensor) api.teachLimits().then((t) => (globalTeach = t)).catch(toastError);
   const taughtCount = $derived(sensor.objects?.taught ?? 0);
   const taughtSomething = $derived(!!sensor.objects && (taughtCount > 0 || sensor.objects.custom.length > 0));
   const readingSensor = isReadingSensor(initial);
@@ -62,7 +66,7 @@
         source,
         ...(roi ? { roi } : { clear_roi: true }),
         ...(objectSensor
-          ? { objects: { classes, clear_after_s: clearAfter, min_size: minSize, use_taught: useTaught } }
+          ? { objects: { classes, clear_after_s: clearAfter, min_size: minSize, use_taught: useTaught, teach: teachLimits } }
           : readingSensor
             ? { reading }
             : { states, review }),
@@ -187,6 +191,12 @@
             </ConfirmButton>
             <span class="xsmall faint">Every taught box and own label, with its entities.</span>
           </div>
+          {#if globalTeach}
+            <div class="col teach-limits" data-testid="sensor-teach-limits">
+              <strong class="small">Limits for this sensor</strong>
+              <TeachLimitsEditor bind:value={teachLimits} inherited={globalTeach.effective} inheritedFrom="the global value" />
+            </div>
+          {/if}
         </section>
       {/if}
     {:else}
@@ -244,6 +254,11 @@
   .taught {
     align-items: flex-start;
   }
+  .teach-limits {
+    gap: var(--space-3);
+    padding-top: var(--space-3);
+    border-top: 1px solid var(--c-border);
+  }
   .taught input {
     margin-top: 3px;
   }

From f197e1c1032af5882bbaa13658ae3e46fdf47eb8 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Sun, 11 Oct 2026 10:31:26 +0200
Subject: [PATCH 07/28] 0.8.1b2: switching AI models gives the memory back

A dropped ONNX Runtime session is freed, but glibc kept the memory, so every model switch left the
app a few hundred MB larger until a restart. After a switch (and when the last object or reading
sensor is deleted) the app now collects garbage and calls malloc_trim. Measured on the test Home
Assistant: light -> heavy -> light went 506 -> 1357 -> 1290 MB before, 535 -> 999 -> 539 MB after.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 visionstate/CHANGELOG.md                          |  9 +++++++++
 visionstate/backend/visionstate/backbones.py      | 15 +++++++++++++++
 visionstate/backend/visionstate/engine/models.py  |  3 +++
 visionstate/backend/visionstate/engine/runtime.py |  1 +
 visionstate/config.yaml                           |  2 +-
 5 files changed, 29 insertions(+), 1 deletion(-)

diff --git a/visionstate/CHANGELOG.md b/visionstate/CHANGELOG.md
index 40645fc..4f51696 100644
--- a/visionstate/CHANGELOG.md
+++ b/visionstate/CHANGELOG.md
@@ -1,5 +1,14 @@
 # Changelog
 
+## 0.8.1b2
+
+### Fixed
+
+- **Switching AI models gives the memory back.** After choosing another model under **Settings → AI
+  model**, the memory of the old one now goes back to the system instead of staying with the app
+  until a restart: switching between the included and the larger models back and forth made the app
+  a few hundred MB larger every time.
+
 ## 0.8.1b1
 
 ### New
diff --git a/visionstate/backend/visionstate/backbones.py b/visionstate/backend/visionstate/backbones.py
index 9a9234a..f018bbe 100644
--- a/visionstate/backend/visionstate/backbones.py
+++ b/visionstate/backend/visionstate/backbones.py
@@ -8,6 +8,8 @@
 
 from __future__ import annotations
 
+import ctypes
+import gc
 import hashlib
 import json
 import logging
@@ -159,6 +161,19 @@ def cpu_session(model_path: Path):
     return ort.InferenceSession(str(model_path), sess_options=options, providers=["CPUExecutionProvider"])
 
 
+def release_memory() -> None:
+    """Give the memory of models that are no longer used back to the system.
+
+    A dropped ONNX Runtime session is freed, but glibc keeps the freed memory for later use, so
+    switching models left the app hundreds of MB larger every time (until a restart).
+    ``malloc_trim`` returns it; on other C libraries (Windows, macOS) there is nothing to do."""
+    gc.collect()
+    try:
+        ctypes.CDLL("libc.so.6").malloc_trim(0)
+    except OSError, AttributeError:
+        pass
+
+
 class Embedder:
     """Turns images into L2-normalised feature vectors with one ONNX backbone."""
 
diff --git a/visionstate/backend/visionstate/engine/models.py b/visionstate/backend/visionstate/engine/models.py
index d036fe3..5f34b11 100644
--- a/visionstate/backend/visionstate/engine/models.py
+++ b/visionstate/backend/visionstate/engine/models.py
@@ -34,6 +34,7 @@ async def load_embedder(self, backbone_id: str) -> None:
 
     async def set_backbone(self, backbone_id: str) -> None:
         await self.load_embedder(backbone_id)
+        await asyncio.to_thread(backbones.release_memory)
         self.db.set_setting("backbone", backbone_id)
         for sensor_id in await asyncio.to_thread(self._sensor_ids, KIND_STATES):
             self.schedule_retrain(sensor_id, delay=0)
@@ -70,6 +71,7 @@ async def set_detector(self, detector_id: str) -> None:
         if self.detector is not None or object_sensors:
             async with self._detector_lock:
                 await self.load_detector(detector_id)
+            await asyncio.to_thread(backbones.release_memory)
         await asyncio.to_thread(self.db.set_setting, "detector", detector_id)
         for sensor_id in object_sensors:
             self.wake(sensor_id, force=True)
@@ -102,6 +104,7 @@ async def set_reader(self, reader_id: str) -> None:
         if self.reader is not None or reading_sensors:
             async with self._reader_lock:
                 await self.load_reader(reader_id)
+            await asyncio.to_thread(backbones.release_memory)
         await asyncio.to_thread(self.db.set_setting, "reader", reader_id)
         for sensor_id in reading_sensors:
             self.wake(sensor_id, force=True)
diff --git a/visionstate/backend/visionstate/engine/runtime.py b/visionstate/backend/visionstate/engine/runtime.py
index 871b67f..7b4166a 100644
--- a/visionstate/backend/visionstate/engine/runtime.py
+++ b/visionstate/backend/visionstate/engine/runtime.py
@@ -142,6 +142,7 @@ async def sensor_deleted(self, sensor_id: int, descriptor: SensorDescriptor) ->
             self.detector = None  # the last object sensor is gone: free the memory
         if descriptor.kind == KIND_READING and not await asyncio.to_thread(self._sensor_ids, KIND_READING):
             self.reader = None
+        await asyncio.to_thread(backbones.release_memory)
 
     def set_global_review(self, rules: dict) -> None:
         self.global_review = dict(rules)
diff --git a/visionstate/config.yaml b/visionstate/config.yaml
index 6eed966..2825f3e 100644
--- a/visionstate/config.yaml
+++ b/visionstate/config.yaml
@@ -1,5 +1,5 @@
 name: VisionState (beta)
-version: "0.8.1b1"
+version: "0.8.1b2"
 slug: visionstate
 description: Teach a local AI to recognise states (open, closed, …), find objects (people, cars, pets) or read numbers in your camera images
 url: https://github.com/oleost/VisionState

From caa1cf054ca3b063dd176c72e0b1050f3cb04e0e Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Sun, 11 Oct 2026 11:03:41 +0200
Subject: [PATCH 08/28] Docs: try changes on a real Home Assistant as a local
 app

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 CLAUDE.md | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/CLAUDE.md b/CLAUDE.md
index 5ccf485..cb00f11 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -110,6 +110,15 @@ itself (`__main__.py`).
 - **The Home Assistant side:** any local MQTT broker (e.g. Mosquitto on 1883) and
   `VISIONSTATE_MQTT_HOST=127.0.0.1`: discovery configs appear under `homeassistant/…`, values under
   `visionstate/<slug>/…`.
+- **On a real Home Assistant, without a release:** build the working copy as a *local app*. Copy the
+  `visionstate/` folder (without `node_modules`, `dist`, `.venv`, `models`) into the `local_apps`
+  folder of Home Assistant (older versions: `addons`; reach it with the Samba or Terminal & SSH app),
+  and in that copy's `config.yaml` delete `image:` and give it its own `name`, `slug`, `panel_title`
+  and `VISIONSTATE_MEDIA` folder. Home Assistant then builds the Dockerfile itself, the same image
+  as a release (*Local apps* in the app store; after a change: *Rebuild*). It shares MQTT topics and
+  unique IDs with an installed VisionState, so never run both at once; it has its own data (move
+  sensors with export/import). This is how changes are tried on Home Assistant during development;
+  a beta is released when the maintainer asks for one.
 
 ## UI testing routine
 

From 8250396ffab48c4866ce70fd0a3d7c8b67c07a04 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Sun, 11 Oct 2026 20:46:40 +0200
Subject: [PATCH 09/28] 0.8.1b3: recognise people by their face, shared by
 every camera

Object sensors that look for Person can name the people of a household
(YuNet + SFace or AuraFace, downloaded on demand, off until turned on):
shared people with a People page and an entity per person saying which
camera sees them, unknown person for clearly seen strangers, face limits
globally and per sensor, unnamed faces kept in the history, and the face
option in the wizard. Settings: compare and manage the models (delete
downloaded ones not in use); the wizard warns about low-resolution streams.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 CLAUDE.md                                     |   4 +-
 README.md                                     |  16 +-
 docs/ARCHITECTURE.md                          |  17 +-
 docs/FACE_RECOGNITION_PLAN.md                 |  63 ++-
 docs/SCOPE.md                                 |  67 ++-
 visionstate/CHANGELOG.md                      |  33 ++
 visionstate/DOCS.md                           | 112 +++-
 visionstate/README.md                         |   5 +-
 visionstate/backend/tests/assets/README.md    |  11 +
 .../backend/tests/assets/face_crew.jpg        | Bin 0 -> 132441 bytes
 .../backend/tests/assets/face_group.jpg       | Bin 0 -> 228727 bytes
 .../tests/assets/face_williams_2006.jpg       | Bin 0 -> 67705 bytes
 .../tests/assets/face_williams_2018.jpg       | Bin 0 -> 46800 bytes
 .../backend/tests/assets/face_wilmore.jpg     | Bin 0 -> 62568 bytes
 visionstate/backend/tests/conftest.py         |  10 +-
 visionstate/backend/tests/test_faces.py       | 489 ++++++++++++++++
 visionstate/backend/visionstate/api/common.py |  71 ++-
 visionstate/backend/visionstate/api/faces.py  | 391 +++++++++++++
 .../backend/visionstate/api/history.py        |   1 +
 .../backend/visionstate/api/imports.py        |  36 +-
 visionstate/backend/visionstate/api/review.py |  75 ++-
 .../backend/visionstate/api/sensors.py        |  12 +-
 visionstate/backend/visionstate/api/system.py | 137 ++++-
 visionstate/backend/visionstate/api/teach.py  |  55 +-
 visionstate/backend/visionstate/backbones.py  |  16 +-
 visionstate/backend/visionstate/bundle.py     |  32 +-
 visionstate/backend/visionstate/db.py         |  44 +-
 .../backend/visionstate/engine/__init__.py    |   3 +-
 .../backend/visionstate/engine/base.py        |  22 +-
 .../backend/visionstate/engine/faces.py       | 468 ++++++++++++++++
 .../backend/visionstate/engine/models.py      |   4 +
 .../backend/visionstate/engine/objects.py     |  44 +-
 .../backend/visionstate/engine/runtime.py     |  26 +-
 .../backend/visionstate/engine/state.py       |  40 +-
 visionstate/backend/visionstate/faces.json    |  62 +++
 visionstate/backend/visionstate/faces.py      | 527 ++++++++++++++++++
 visionstate/backend/visionstate/main.py       |   4 +-
 visionstate/backend/visionstate/mqtt.py       |  50 +-
 visionstate/backend/visionstate/settings.py   | 100 +++-
 visionstate/backend/visionstate/storage.py    |  25 +-
 visionstate/config.yaml                       |   2 +-
 visionstate/frontend/e2e/env.ts               |   2 +
 visionstate/frontend/e2e/helpers.ts           |   3 +-
 visionstate/frontend/e2e/seed.setup.ts        |  37 ++
 visionstate/frontend/e2e/ui.spec.ts           | 125 ++++-
 visionstate/frontend/src/App.svelte           |   3 +
 visionstate/frontend/src/lib/api.ts           |  51 +-
 .../src/lib/components/DetectionBoxes.svelte  |  26 +-
 .../src/lib/components/ExportButton.svelte    |  54 ++
 .../src/lib/components/FaceConsent.svelte     | 125 +++++
 .../lib/components/FaceLimitsEditor.svelte    |  39 ++
 .../src/lib/components/FaceQuestion.svelte    | 231 ++++++++
 .../src/lib/components/FaceStats.svelte       |  51 ++
 .../src/lib/components/FacesCard.svelte       | 172 ++++++
 .../src/lib/components/LimitsEditor.svelte    | 120 ++++
 .../src/lib/components/ModelCompare.svelte    |  55 +-
 .../frontend/src/lib/components/Nav.svelte    |   7 +-
 .../src/lib/components/PeopleCards.svelte     | 238 ++++++++
 .../lib/components/TeachLimitsEditor.svelte   | 113 +---
 .../src/lib/components/TeachSheet.svelte      | 109 +++-
 .../lib/components/history/ObjectRow.svelte   |  12 +-
 visionstate/frontend/src/lib/format.ts        |   3 +
 visionstate/frontend/src/lib/objects.ts       |  52 +-
 visionstate/frontend/src/lib/router.svelte.ts |   1 +
 visionstate/frontend/src/lib/types.ts         | 133 ++++-
 visionstate/frontend/src/lib/ui.ts            |  41 ++
 .../frontend/src/pages/NewSensor.svelte       |  34 +-
 visionstate/frontend/src/pages/People.svelte  |  65 +++
 visionstate/frontend/src/pages/Review.svelte  |  18 +-
 .../frontend/src/pages/SensorPage.svelte      |   3 +-
 .../frontend/src/pages/Settings.svelte        | 109 +++-
 .../frontend/src/pages/sensor/LiveTab.svelte  |  22 +-
 .../src/pages/sensor/ObjectQualityTab.svelte  |  40 +-
 .../src/pages/sensor/SettingsTab.svelte       |   9 +-
 .../src/pages/wizard/ObjectsStep.svelte       |  58 +-
 75 files changed, 4949 insertions(+), 286 deletions(-)
 create mode 100644 visionstate/backend/tests/assets/face_crew.jpg
 create mode 100644 visionstate/backend/tests/assets/face_group.jpg
 create mode 100644 visionstate/backend/tests/assets/face_williams_2006.jpg
 create mode 100644 visionstate/backend/tests/assets/face_williams_2018.jpg
 create mode 100644 visionstate/backend/tests/assets/face_wilmore.jpg
 create mode 100644 visionstate/backend/tests/test_faces.py
 create mode 100644 visionstate/backend/visionstate/api/faces.py
 create mode 100644 visionstate/backend/visionstate/engine/faces.py
 create mode 100644 visionstate/backend/visionstate/faces.json
 create mode 100644 visionstate/backend/visionstate/faces.py
 create mode 100644 visionstate/frontend/src/lib/components/ExportButton.svelte
 create mode 100644 visionstate/frontend/src/lib/components/FaceConsent.svelte
 create mode 100644 visionstate/frontend/src/lib/components/FaceLimitsEditor.svelte
 create mode 100644 visionstate/frontend/src/lib/components/FaceQuestion.svelte
 create mode 100644 visionstate/frontend/src/lib/components/FaceStats.svelte
 create mode 100644 visionstate/frontend/src/lib/components/FacesCard.svelte
 create mode 100644 visionstate/frontend/src/lib/components/LimitsEditor.svelte
 create mode 100644 visionstate/frontend/src/lib/components/PeopleCards.svelte
 create mode 100644 visionstate/frontend/src/pages/People.svelte

diff --git a/CLAUDE.md b/CLAUDE.md
index cb00f11..337c18f 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -27,7 +27,7 @@ Every fact has **one** home; other files link to it instead of repeating it.
 | How to work: principles, checks, testing, pitfalls | this file |
 | The wheel reader model: data, licences, results, how to train it | [`tools/wheelreader/README.md`](tools/wheelreader/README.md) |
 | The plan for better counter readings, and its status | [`docs/WHEEL_READER_PLAN.md`](docs/WHEEL_READER_PLAN.md) |
-| Face recognition: models, licences, privacy and the plan (not built) | [`docs/FACE_RECOGNITION_PLAN.md`](docs/FACE_RECOGNITION_PLAN.md) |
+| Face recognition: models, licences, privacy, the plan and the decisions made building it | [`docs/FACE_RECOGNITION_PLAN.md`](docs/FACE_RECOGNITION_PLAN.md) |
 | What changed in a version | `visionstate/CHANGELOG.md` (written at release, never edited afterwards) |
 | Test image sources | `visionstate/backend/tests/assets/README.md` |
 
@@ -60,7 +60,7 @@ Every fact has **one** home; other files link to it instead of repeating it.
   which kind(s) it applies to.
 - New engine code goes into the part (mixin) it belongs to. A long-running loop must survive any
   exception (log it once, try again) — only `CancelledError` may end it.
-- Model registries (`backbones.json`, `detectors.json`, `readers.json`): entries are never changed
+- Model registries (`backbones.json`, `detectors.json`, `readers.json`, `faces.json`): entries are never changed
   or removed once released (their texts and measured footprint may be); a new model gets a new id, pinned by Hugging Face revision and SHA-256,
   and is checked against real inputs in the tests (one public ONNX conversion turned out to be
   broken and only a test with real photos showed it).
diff --git a/README.md b/README.md
index 421e1b6..a778716 100644
--- a/README.md
+++ b/README.md
@@ -43,6 +43,10 @@ without writing code or leaving Home Assistant:
 - 🐕 **Find objects without training** — people, cars, bicycles, cats, dogs and 75 more, with a
   count and an on/off sensor for each. Pick them, done. Wrong about your garden statue? Tap the
   box: *not a person*. Want *our car* apart from any car? Give it its own label.
+- 🙂 **Know who is there** — name the people of your household once, by tapping them in the live
+  image, and every camera knows them: an on/off sensor per person and camera, and one per person
+  that says which camera sees them now. *Unknown person* turns on for a face it was not taught.
+  Off until you turn it on, and the faces never leave your Home Assistant.
 - 🔢 **Read numbers** — power meters, the rolling digits of water and gas meters (read wheel by
   wheel, also while a wheel is half way between two digits), prices, the minutes left on the
   washing machine. Counters only go up and land straight in the Energy dashboard; implausible readings are rejected, and a
@@ -171,6 +175,13 @@ Apache-2.0, trained on the COCO objects). It finds every object in the region; e
 picked is reported with a count and cleared a while after it was last seen. Boxes you corrected
 teach it your camera: later boxes that clearly look like one you taught get your answer.
 
+Object sensors that recognise faces look for a face in the upper part of each person
+([YuNet](https://github.com/opencv/opencv_zoo), MIT), skip faces that are too small, turned away,
+cut off or blurry, and compare the rest with the faces you named
+([SFace](https://github.com/opencv/opencv_zoo), Apache-2.0, or for a fast PC
+[AuraFace](https://huggingface.co/fal/AuraFace-v1), Apache-2.0). Only a face seen clearly can make
+an unknown person, so someone walking past side-on is not reported as a stranger.
+
 Reading sensors read the digits in the region with a small text recognizer
 ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR), Apache-2.0) that may only output digits,
 then check the value (a counter never goes down) before publishing it. Mechanical counters are
@@ -186,6 +197,8 @@ the app's data folder (included in Home Assistant backups).
 - 100 % local — no cloud services, no telemetry.
 - Only reachable through Home Assistant Ingress (requires a Home Assistant login).
 - Camera passwords are hidden in logs and removed from exported sensor bundles.
+- Face recognition is off until you turn it on for a camera. The faces you teach stay in the app's
+  own storage (not the media folder); an export includes them only if you tick it.
 
 ## Development
 
@@ -207,6 +220,7 @@ Issues and ideas are welcome in [GitHub Issues](https://github.com/oleost/Vision
 ## Licence
 
 [Apache-2.0](LICENSE). The bundled models are Apache-2.0 as well: DINOv2 (Meta), D-FINE,
-PaddleOCR PP-OCRv6 and the VisionState wheel reader.
+PaddleOCR PP-OCRv6 and the VisionState wheel reader. The face models are downloaded only when a
+sensor recognises faces: YuNet (MIT) with SFace or AuraFace (Apache-2.0).
 
 If VisionState is useful to you, you can [buy me a coffee](https://buymeacoffee.com/o1ep) ☕
diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index 42fa992..5d989b8 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -51,12 +51,13 @@ API as `request.app.state.runtime`.
 | `db.py` | SQLAlchemy tables and the schema migrations (`MIGRATIONS`, `SCHEMA_VERSION`) |
 | `storage.py` | Image files on disk: training samples, history frames, thumbnails |
 | `engine/` | The runtime — see below |
-| `api/` | The REST API (`/api/v1/…`), one file per topic: `sensors.py` (CRUD, quality, export), `history.py` (the history: one filter for the list, its count and facets; history frames), `samples.py` (training images), `teach.py` (taught boxes), `cameras.py` (camera and entity lists, the light, previews), `review.py` (review queue and answers), `imports.py` (import), `system.py` (UI config, status, AI models, storage, review rules, review reminder, teaching limits), `common.py` (input models and views shared by them) |
+| `api/` | The REST API (`/api/v1/…`), one file per topic: `sensors.py` (CRUD, quality, export), `history.py` (the history: one filter for the list, its count and facets; history frames), `samples.py` (training images), `teach.py` (taught boxes), `faces.py` (teaching a face on a sensor; the people and their faces, shared by every sensor), `cameras.py` (camera and entity lists, the light, previews), `review.py` (review queue and answers), `imports.py` (import), `system.py` (UI config, status, AI models and deleting downloaded ones, storage, review rules, review reminder, teaching and face limits), `common.py` (input models and views shared by them) |
 | `sources.py` | Camera sources: Home Assistant camera, HTTP snapshot, RTSP; address checks and size limits; the Home Assistant REST client (states, services, notify services, the app's page) |
 | `mqtt.py` | MQTT topics, Home Assistant discovery messages, the bridge (connect, publish, commands) |
 | `ha_events.py` | Home Assistant WebSocket: state changes of trigger entities, entity registry |
 | `lights.py` | A sensor's light: switched on for checks and while someone looks, shared by both |
 | `backbones.py`, `detectors.py`, `readers.py` | The three AI models and their registries (`*.json`): embedding backbone (state sensors), object detector, number reader (and the wheel reader for mechanical counters, `readers.WheelReader`; trained with `tools/wheelreader`) |
+| `faces.py` | Faces in person boxes (registry `faces.json`, never bundled): finding them (YuNet), the quality gate, aligning, embedding, telling who it is |
 | `classifier.py` | The small per-sensor classifier ("head") on top of the backbone |
 | `teach.py` | Object sensors: comparing detected boxes with the boxes the user taught |
 | `imaging.py` | Decoding, regions (ROI), crops, change signatures, drawing boxes |
@@ -70,7 +71,7 @@ API as `request.app.state.runtime`.
 parts it uses — its class line says what it depends on:
 
 ```
-Runtime ── ChecksMixin ──┬─ ObjectChecksMixin ── TeachingMixin ── ModelsMixin ── TrainingMixin
+Runtime ── ChecksMixin ──┬─ ObjectChecksMixin ── FacesMixin, TeachingMixin ── ModelsMixin ── TrainingMixin
         │                │                    └─ PublishingMixin
         │                └─ ReadingChecksMixin ── ModelsMixin, PublishingMixin
         ├─ HistoryMixin ── PublishingMixin                (all of them on RuntimeBase)
@@ -87,6 +88,7 @@ Runtime ── ChecksMixin ──┬─ ObjectChecksMixin ── TeachingMixin 
 | `objects.py`, `reading.py` | The check of an object sensor and of a reading sensor |
 | `models.py` | Loading and switching the three AI models; `detect_objects`, `read_number` |
 | `teaching.py` | Taught boxes of object sensors (index, embeddings) |
+| `faces.py` | Faces on object sensors: the face model (downloaded and loaded in the background, let go when unused), the index of the people's taught faces (shared by every sensor), the face step of a check (`apply_faces`), each person's own entity (where they are seen) |
 | `training.py` | Training the state sensors' heads; embeddings of samples |
 | `publishing.py` | Discovery and values to Home Assistant, the review queue entity, commands from it |
 | `history.py` | History clean-up by age and size, disk use |
@@ -103,7 +105,7 @@ sensor loop (checks._sensor_loop)          woken by: its interval, a trigger ent
   ├─ decode (imaging.decode)
   └─ by kind:
       state sensor   → checks._run_states  backbone embedding → head → probabilities
-      object sensor  → objects._run_objects detector → taught boxes (teaching) → tracks (logic)
+      object sensor  → objects._run_objects detector → taught boxes (teaching) → faces → tracks (logic)
       reading sensor → reading._run_reading reader → parse → plausibility → debounce
         │
         ├─ publish (publishing._send → mqtt.MqttBridge.publish)   state, attributes, image
@@ -117,7 +119,10 @@ error is kept in `LiveState.error` and shown in the UI.
 
 - **SQLite** (`<data>/visionstate.db`): `sensor` (+ `state`), `sample` (+ `sample_label`,
   `embedding` cache), `prediction` (the history and the review queue), `reading_stat`,
-  `model_info` (per sensor), `setting` (global settings as JSON).
+  `model_info` (per sensor), `setting` (global settings as JSON), `person` (+ `face`,
+  `face_embedding` cache: the people taught by face, shared by every sensor).
+- **Faces** (`<data>/faces/`): crops of the people's taught faces — in the app's own storage, not
+  the media folder (see `storage.Storage.face_path`).
 - **Files** (`<media>/`): `samples/<sensor>/` (training images; for object sensors the crops of
   taught boxes), `history/<sensor>/`, `thumbs/`; `<data>/heads/` (trained heads), `<data>/models/`
   (downloaded models, left out of backups).
@@ -139,9 +144,9 @@ error is kept in `LiveState.error` and shown in the UI.
 | `lib/history.ts` | The history filter: from and to the page URL, and as the API takes it |
 | `lib/ui.ts` | UI constants (tabs per sensor kind, wizard texts); `tokens.css` colours, type, spacing |
 | `lib/objects.ts`, `lib/reading.ts`, `lib/roi.ts`, `lib/format.ts` | Helpers: names and colours of objects and own labels, reading values and units, region geometry, formatting |
-| `pages/` | Dashboard, the new sensor wizard (`NewSensor.svelte`; its Detect step per kind in `pages/wizard/`), Review, History, Settings, and `SensorPage` with one file per tab in `pages/sensor/` (its History tab is `HistoryView`) |
+| `pages/` | Dashboard, the new sensor wizard (`NewSensor.svelte`; its Detect step per kind in `pages/wizard/`), Review, History, People, Settings, and `SensorPage` with one file per tab in `pages/sensor/` (its History tab is `HistoryView`) |
 | `lib/components/history/` | `HistoryView` (filters, list, paging, new rows) shared by the History page and the History tabs, and one row component per sensor kind |
-| `lib/components/` | Building blocks (region editor, frames with boxes, editors for triggers, reading, states…) |
+| `lib/components/` | Building blocks (region editor, frames with boxes, editors for triggers, reading, states, limits that inherit (`LimitsEditor`)…) |
 
 ## Recipes
 
diff --git a/docs/FACE_RECOGNITION_PLAN.md b/docs/FACE_RECOGNITION_PLAN.md
index 50180bc..419ceed 100644
--- a/docs/FACE_RECOGNITION_PLAN.md
+++ b/docs/FACE_RECOGNITION_PLAN.md
@@ -1,10 +1,10 @@
 # Face recognition: research and plan
 
-A plan, not yet built: recognising **who** a person is (`Ole`, `Kari`, *someone unknown*) when an
-object sensor sees a person. It applies to the `objects` sensor kind only. The general design is in
-[`SCOPE.md`](SCOPE.md), the code map in [`ARCHITECTURE.md`](ARCHITECTURE.md). Research done
-2026-10-10; licence statements were read from the model pages that day and must be checked again
-before a model is chosen (§4).
+Recognising **who** a person is (`Ole`, `Kari`, *someone unknown*) when an object sensor sees a
+person. It applies to the `objects` sensor kind only. Phases 1–3 are built (§12 says what was
+decided and where the build differs from this plan); how it works now is in [`SCOPE.md`](SCOPE.md)
+§5, the code map in [`ARCHITECTURE.md`](ARCHITECTURE.md). Research done 2026-10-10; licence
+statements were read from the model pages that day and again on 2026-10-11 (§4).
 
 ## 1. The idea in one paragraph
 
@@ -443,3 +443,56 @@ Collected from the research, from how others built it, and from VisionState's ow
 - **Polling while a face model loads.** A large model loaded in the event loop would freeze the
   UI; load it in a worker thread like the others, and never let a failed download stop the
   sensor's loop (CLAUDE.md: long-running loops survive any exception).
+
+## 12. Decisions and the build
+
+The maintainer's answers to §10 (2026-10-11):
+
+1. **Licence bar:** both, the user chooses. **SFace** (default, small) and **AuraFace v1** (the
+   only one whose makers say its data allows commercial use; for a fast PC) are recognisers in
+   `faces.json`; **YuNet** finds the faces for both (the AuraFace repository's SCRFD detector is
+   not covered by its licence). None is bundled; the confirmation sheet and Settings → AI model
+   state each one's licence and training data. BlazeFace (a detector with Google's own data)
+   needs an ONNX conversion checked on real photos — not done.
+2. **Raspberry Pi 4 is the target** for the default: SFace with YuNet, at most
+   `FACES["max_faces"]` faces per check in one detector run.
+3. **Phase 0 was skipped**: no household frames yet. Thresholds were set from the NASA test photos
+   (`tests/assets`, both recognisers: same person 0.59–0.95, different people at most 0.30) and the
+   minimum face from the recognisers' input (the eyes are about 35 px apart in a 112 px aligned
+   face, so a default of 20 px). They are tried on real cameras on the dev app and adjusted there.
+   The first try (a phone as a 480 × 640 RTSP camera in a living room) changed three things: the
+   smallest face is the width of the face box (`min_face_px`), as the eyes come together when a
+   head turns and a turned face was called too small; faces a bit more turned may be named
+   (`FACES["max_turn"]`), while only a clearly seen face can make an *unknown person*; and the
+   sharpness limit was lowered, as at 112 px it also rejected sharp faces about 14 px between the
+   eyes, where the recogniser still told the person from a stranger. A second try (the phone
+   moved around, someone leaning into it) added *cut*: a face cut by the frame's edge got eyes
+   guessed outside the picture and looked like nobody (0.15), which could have made the user an
+   unknown person.
+4. **Unknown person** is an entity; "Who is this?" is a review item only with **Ask me about faces
+   I do not know** on (off by default).
+5. **Only on the sensor's Settings tab**, after the confirmation; the wizard only says it exists
+   (changed later: the wizard can turn it on too, after the same confirmation — see below).
+6. Built as phases 1–3 together.
+
+Where the build differs from §3–§9:
+
+- YuNet's ONNX file has a fixed 640 × 640 input: four head regions are tiled into one run.
+- A face found from two overlapping person boxes counts for the first one only.
+- The face close-up in the teach sheet and the review card is cut from the frame by the UI; a
+  taught face's crop is not linked back to the frame it came from (§9.5 "tap one to see the
+  frame" is not built), and a history row of a person shows the frame, not the face.
+- History has no extra filter for people: people are own labels, so the *What* filter already
+  lists them, and *Unknown person* with them.
+- Separate day and night faces per person (§11) and phase 4 are not built.
+- After the first tries the maintainer asked for: the faces' limits (smallest face and the four
+  similarities) set for all sensors under **Settings → Recognising faces** and per sensor, like the
+  teaching limits (§9.6 had only *Smallest face*, per sensor); turning faces on in the wizard too
+  (§5 had it on the Settings tab only); and the face model listed right after the object detector,
+  its file deleted like any other downloaded model under *Compare and manage the models* (§9.7's
+  own *Delete downloaded file* button is gone).
+- **The people are shared** by every sensor (the maintainer, after the first doorbell test): a
+  person taught on one camera is known on all that recognise faces, listed on a **People** page
+  (renamed and forgotten there, and on each sensor's Quality tab), and has their own entity saying
+  which sensor sees them now. Deleting a sensor keeps the faces taught on it. §9's per-sensor
+  people and *Forget all faces* on the Faces card are replaced by this.
diff --git a/docs/SCOPE.md b/docs/SCOPE.md
index bcc4357..8be10df 100644
--- a/docs/SCOPE.md
+++ b/docs/SCOPE.md
@@ -85,7 +85,7 @@ so retraining only runs the backbone on new or changed samples.
 | `dinov2-small` | DINOv2 ViT-S/14, fp32 (downloaded on demand, SHA-256 checked) | Optional, slightly more accurate |
 | `dinov2-base` | DINOv2 ViT-B/14, fp32 (downloaded on demand, 347 MB) | Fast PCs; separates objects best |
 
-- Each registry entry carries a rough footprint for **Compare the models** in Settings
+- Each registry entry carries a rough footprint for **Compare and manage the models** in Settings
   (`backbones.Footprint`: extra memory, time per check on 4 threads of a desktop CPU, an accuracy
   rank within its kind), measured when the model was added. Texts and footprint may be updated in a
   released entry; its file and inputs never change.
@@ -177,6 +177,60 @@ knew). Instead a second step compares boxes with boxes the user taught:
   global < sensor. Asking and keeping must not start above a match (refused by the API). A user's
   value stays when the backbone changes; the UI says to look at it again.
 
+**Recognising people by their face** (`faces.py`, `engine/faces.py`, `api/faces.py`; registry
+`faces.json`, tunables `settings.FACES`, per sensor `objects["faces"]` with `settings.FACE_DEFAULTS`;
+the user's limits `settings.FACE_TUNABLE`).
+Why it is a part of object sensors and not a kind, the research, the privacy rules and what is
+left are in [`FACE_RECOGNITION_PLAN.md`](FACE_RECOGNITION_PLAN.md). In short:
+
+- Off by default; on only for a sensor that looks for `person` (its Settings tab, or the wizard),
+  after a confirmation that names what is stored and downloaded. The face models are never
+  bundled: the first sensor that turns it on downloads them (`backbones.download` with progress)
+  and loads them in the background — the sensor runs without faces meanwhile; a failed download waits for **Try again**. The last sensor
+  turning it off lets the model go; its files can then be deleted (like any downloaded model not
+  in use: `DELETE /api/v1/models/{kind}/{id}`, *Compare and manage the models*).
+- The user's limits (`settings.FACE_TUNABLE`: the smallest face and the four similarities) work
+  like the teaching limits: defaults (`FACE_DEFAULTS`, the similarities from the face model) <
+  global (Settings, DB setting `faces`) < the sensor's own (`objects["faces"]`); an unset value
+  inherits.
+- Each check: person boxes (at most `FACES["max_faces"]`, new boxes first, then the ones searched
+  longest ago) are cropped from the full frame in their upper part and tiled
+  four to one YuNet run (640 × 640). One face counts for one person box. The quality gate (not
+  cut by the frame's edge — the detector then guesses the eyes —, the face's width `min_face_px` —
+  not the distance between the eyes, which shrinks as a head turns —, detector score, how far it
+  is turned, sharpness) makes a bad face *no face*, never *unknown*. A usable face is aligned on
+  its five points (Umeyama + Pillow, no OpenCV) and embedded.
+- A person's score is the mean similarity to their `top_k` closest taught faces. Thresholds belong
+  to the recogniser's registry entry (cosine values of one model mean nothing for another): a
+  match needs `match_similarity` and `FACES["margin"]` over the next person, and is published at
+  once from `sure_similarity`, else when a second check of the same box agrees; between
+  `ask_similarity` and a match the box is `ask` (review: "Is this Kari?"); a box that stays keeps
+  its name while no usable face is seen or the face is above `keep_similarity`. A sure name is
+  carried for `recheck_every` checks without searching again. A box whose usable face matched
+  nobody `unknown_after` checks in a row and never had a name counts for `unknown_person`
+  (`settings.FACE_UNKNOWN_KEY`, its own entities). Only faces seen clearly count:
+  `FACES["unknown_size"]` times the smallest face, turned at most `FACES["unknown_max_turn"]`,
+  detector score at least `FACES["unknown_min_score"]`; a smaller, more turned or less certain face
+  can be named but is otherwise *not sure who*, as not looking like anyone may only be the angle.
+  Measured on the NASA test photos: the same person 0.64–0.95 (SFace) and 0.59–0.93 (AuraFace),
+  different people at most 0.30 and 0.22.
+- The people are shared by every sensor (a household, not a camera): `person` rows, each with
+  `face` rows (`db.Face`: {box, points, score} in the crop, and the sensor it was taught on, null
+  once that sensor is deleted — the face stays). The crop (the face with a margin, not aligned, so
+  another recogniser can align it its own way) lives in `<data>/faces/`, not in the media folder;
+  embeddings are cached per face model (`face_embedding`), deleted with the face, never exported.
+  A sensor that recognises faces gets every person as an own label (`engine.with_people`, not
+  stored on the sensor): its entities, and a name its faces can get. One index of all taught
+  faces serves every sensor. Only a human adds a face (teach sheet, review answer), on any sensor.
+- Each person also has their own entity on the app's device, `sensor.visionstate_<key>`
+  (`mqtt.person_discovery_messages`): the slug of the sensor that sees them now — the one that saw
+  them last when several do — or `settings.PERSON_NOT_SEEN`; when and where they were last seen
+  are attributes. Kept in memory: after a restart everyone is `not_seen` until seen again.
+- `GET /people` and `/faces/{id}` (`api/faces.py`) list, rename, move and forget for all sensors;
+  teaching stays on a sensor (`/sensors/{id}/faces`). Export carries the faces taught on that
+  sensor, with their people's keys and names, only with `?faces=true`; import adds them to the
+  person with that key or name (or a new one) and turns face recognition off.
+
 - Weights: D-FINE (Apache-2.0, COCO-trained; not the Objects365 variants, which carry other
   terms), ONNX conversions from Hugging Face pinned by revision and SHA-256. One conversion
   of D-FINE N was found broken during evaluation; tests with real CC0 photos
@@ -420,9 +474,9 @@ Principle: **easy by default, details on demand.** Dark theme, responsive.
     object class or own label — the row's `state_key`; never a reading), event, time window and
     *waiting for review*, newest or oldest first. Each filter takes several values (any of them
     matches); filters combine with *and*. Events are a registry per kind
-    (`settings.HISTORY_EVENTS`: `change`, `flagged`; `appeared`, `cleared`, `filtered`, `asked`;
-    `value`, `rejected`, `verified`), sent to the UI in `/config`; `api/history.py` says which rows
-    each matches. One function (`history_query`) turns a filter into SQL for the list
+    (`settings.HISTORY_EVENTS`: `change`, `flagged`; `appeared`, `cleared`, `filtered`, `asked`,
+    `face_seen`; `value`, `rejected`, `verified`), sent to the UI in `/config`; `api/history.py`
+    says which rows each matches. One function (`history_query`) turns a filter into SQL for the list
     (`GET /history`, paged by a cursor on `(created_at, id)` so rows arriving at the top never
     repeat a row), its count and the facets (`GET /history/facets`: rows per sensor, key and event,
     each counted without its own filter so other choices stay offered). The filter lives in the
@@ -479,6 +533,7 @@ loads.
 | Settings, DB, embeddings, trained heads | `/data` (per app) | Yes |
 | Downloaded backbone models | `/data/models` | Excluded (re-downloadable) |
 | Training images (and boxes taught to object sensors, as crops) | `/media/visionstate/samples/<sensor>/` (beta: `/media/visionstate_beta`) | With the media folder |
+| The people's taught faces (crops, shared by every sensor) | `/data/faces/` — not in the media folder, which every Home Assistant user can browse | Yes |
 | History frames | `/media/…/history/` | Limits: 7 days and 2 GB by default, whichever comes first (see below) |
 
 History frames are full camera frames (JPEG 90). They are removed by age (`history_days`;
@@ -549,6 +604,7 @@ is part of backups and needs no restart.
 | **Wheel reader** ✅ | Mechanical counters read wheel by wheel with a model made for VisionState; wheels mid-turn read right; export v2 of readings | 0.8.0 (betas 0.7.1b3–b6) |
 | **Review reminder** ✅ | A notification in Home Assistant (optionally a push) when frames have waited for review a long time | 0.8.0 (beta 0.7.1b1) |
 | **Larger models, tunable teaching** ✅ | D-FINE M and L, DINOv2 base, a model comparison in Settings, teaching limits globally and per sensor | beta 0.8.1b1 |
+| **Recognising faces** | Faces in person boxes (YuNet), people taught by face and shared by every camera (SFace, or AuraFace for a fast PC), a People page, an entity per person saying where they are, unknown person, review questions, export opt-in — phases 1–3 of [`FACE_RECOGNITION_PLAN.md`](FACE_RECOGNITION_PLAN.md) | in development |
 | **Better counter readings** | Benchmark, the last value as an expectation, calibrated confidence, extra decimal, self-calibrating regions, wheels-v2, learning per sensor — status per phase in [`WHEEL_READER_PLAN.md`](WHEEL_READER_PLAN.md) | planned |
 
 **Open ideas** (not scheduled): more on the History page (export of a filtered selection, a
@@ -560,6 +616,5 @@ Assistant; mechanical counters beyond [`WHEEL_READER_PLAN.md`](WHEEL_READER_PLAN
 dials and gauges; several readings per sensor (a sign with four prices, a
 counter plus its dials); per-sensor model
 choice with unloading of idle models (DINOv2 stays loaded: object sensors that were taught use
-it); zones and line crossing for object sensors; recognising people by their face on object sensors
-(research, models, privacy and a phased plan: [`FACE_RECOGNITION_PLAN.md`](FACE_RECOGNITION_PLAN.md)); classes outside COCO (an open-vocabulary
+it); zones and line crossing for object sensors; classes outside COCO (an open-vocabulary
 detector) — until then, a state sensor covers many of them ("parcel on the doorstep").
diff --git a/visionstate/CHANGELOG.md b/visionstate/CHANGELOG.md
index 4f51696..1e521d8 100644
--- a/visionstate/CHANGELOG.md
+++ b/visionstate/CHANGELOG.md
@@ -1,5 +1,38 @@
 # Changelog
 
+## 0.8.1b3
+
+### New
+
+- **Recognise people by their face** on object sensors that look for Person. Off until you turn it
+  on, per sensor, after a sheet that says what is stored and what is downloaded (once, from Hugging
+  Face); it can also be turned on in the new sensor wizard. The faces never leave your Home
+  Assistant.
+  - **Name the people of your household** by tapping them on the Live tab, in a history frame or in
+    the review queue: *Who is this?* Only a usable face can be taught, so a back or a jacket never
+    becomes someone.
+  - **Shared by every camera:** a person named on one camera is known on all that recognise faces.
+    Each gets an on/off sensor and a count per camera, and their own sensor
+    (`sensor.visionstate_<name>`) that says which camera sees them now, or `not_seen`.
+  - **Unknown person** turns on for a face seen clearly that matches nobody you named; a face
+    further away or turned to the side can still be named, but never makes someone a stranger.
+  - **People** (in the menu) lists everyone with their faces and the camera each was taught on,
+    where they were last seen, people the AI finds alike and faces that may carry the wrong name;
+    rename, move or forget faces and people there.
+  - **Does my camera see faces well enough?** The Live tab draws each face and says why one cannot
+    be used (too small, turned away, cut off by the edge, blurry), and how many of the last people
+    had a usable face.
+  - A face nobody is named for keeps a frame in the history (*Face seen, not named*), so someone
+    who was only at the door for a moment can be named afterwards.
+  - **Limits** for faces (smallest face, how close a face must be to be named, asked about or kept)
+    under **Settings → Recognising faces** for all sensors, and per sensor.
+  - Two face models: **SFace** (small, fine on a Raspberry Pi 4) and **AuraFace** (for a fast PC).
+- **Compare and manage the models** (Settings → AI model): every model from the smallest to the
+  largest, with the face models next to the object detector; a downloaded model that is not in use
+  can be deleted there.
+- **The wizard warns about a small picture**: it may be the camera's low-resolution stream, while
+  the main stream is often hidden in Home Assistant.
+
 ## 0.8.1b2
 
 ### Fixed
diff --git a/visionstate/DOCS.md b/visionstate/DOCS.md
index 6d804e0..b07d4c6 100644
--- a/visionstate/DOCS.md
+++ b/visionstate/DOCS.md
@@ -29,7 +29,9 @@ number** from a display, without any training. Everything runs on this machine;
       inside the box, which makes it far more accurate. For an object that sits at an angle,
       shape the box: drag a corner to move it, drag a **+** on an edge to add a corner, and
       double-click / double-tap (or press and hold) a corner to remove it. Everything outside
-      the shape is ignored. **Reset to rectangle** goes back to a plain box.
+      the shape is ignored. **Reset to rectangle** goes back to a plain box. A small picture
+      gets a warning: it may be the camera's low-resolution stream, and its main stream is often
+      hidden in Home Assistant.
    3. Choose what it detects: **States** (name them, for example *Open*, *Closed*, *Partial*),
       **Objects** (see *Object sensors* below) or **Reading** (see *Reading sensors* below).
       This can not be changed later.
@@ -162,6 +164,90 @@ waiting, and no more often than the review cooldown (**Settings → Review queue
   get a note when that is the case.
 - Export and import take what you taught along.
 
+### Recognising people by their face
+
+An object sensor that looks for **Person** can tell *who* it is: the people you name each get an
+on/off sensor and a count in Home Assistant, like own labels, and **Unknown person** turns on for
+someone it has not been taught. It is **off** until you turn it on. The people are **shared**:
+every camera that recognises faces knows everyone you named on any of them, and each person also
+gets their own sensor (for Kari: `sensor.visionstate_kari`) whose value is the sensor that sees
+them now — the one that saw them last, when several do — or `not_seen`, with when and where they
+were last seen as attributes (after a restart of the app: `not_seen` until they are seen again).
+
+**Before you turn it on.** Faces are biometric data. VisionState keeps them on your Home
+Assistant only — no face ever leaves it, as nothing else does — but the people you teach should
+know about it, children included. A camera that sees a street, a path others use or a
+neighbour's door may need their consent where you live (in Norway, Datatilsynet's rules for
+private cameras: your own house and garden only). It is **not a lock**: a photo of a face looks
+like that face, so never unlock a door or disarm an alarm on it alone. Recognition is weaker for
+children (their faces change quickly: add recent faces now and then), in infrared night images,
+with masks, hats or sunglasses, and — as for every public face model — not equally good for
+everyone.
+
+- **Turn it on** under the sensor's **Settings → Faces → Turn on…**, or when you make the sensor
+  (**Recognise people by their face** under the objects, once Person is picked). A sheet says
+  what is stored and which face model is downloaded (once, from Hugging Face), and nothing
+  happens until you confirm. While it downloads the sensor keeps working without faces; the card
+  shows the progress, and **Try again** if it failed. Without internet, put the file the message
+  names in the app's `models` folder by hand.
+- **Does my camera see faces well enough?** The **Live** tab draws a small rounded box around
+  each face it finds in a person, and says for each person what it saw: a name, *face too small*,
+  *face turned away*, *face cut off by the edge*, *face blurry* or *no face seen*, and how many of
+  the last people it looked at had a usable face. A face it cannot use counts as no face, never as
+  an unknown person. When faces are rarely usable, the card says the likely reason (for faces too
+  small, also the size of the camera's pictures).
+- **Limits** — **Settings → Recognising faces** sets them for all sensors; a sensor's **Faces →
+  Limits for this sensor** can set its own (an empty field follows the global value, shown in
+  grey). **Smallest face** (its width in pixels) sets how small a face may be; lower it only if
+  the names stay right, as small faces are easily mistaken. The similarities (in percent) say how
+  close a face must be to the faces you taught to be named, to be named at once without a second
+  check, to be asked about in the review queue, and to keep a name while the person stays. Their
+  defaults belong to the face model; values you set yourself stay when you switch it.
+- **Which camera works?** What counts is how many pixels a face gets, not the camera's megapixels
+  on the box. Use the camera's **main stream** over RTSP: many cameras and doorbells give Home
+  Assistant a small sub-stream or snapshot (640×480), where a face is only usable within a few
+  metres. Roughly, at the default *Smallest face*, for a sharp picture in good light:
+  - a camera of 640×480 with a normal lens (an ESP32 camera, a sub-stream): up to about 2–3 m;
+  - 1920×1080: about 5–8 m (less with a wider lens);
+  - a doorbell (very wide angle) on its main stream: about 1–1.5 m — enough for someone at the
+    door, but faces at the edge of the picture are stretched, and a doorbell sees people from below.
+  *Unknown person* needs a bigger face than a name (about two thirds of these distances).
+  A camera where people walk towards it at head height sees the most faces; one high up or from
+  the side mostly sees the tops and sides of heads.
+- **Teach a person**: tap a person on the **Live** tab or in a history frame. *Who is this?* comes
+  first, with a close-up of the face: tap a name, or **New person…**. Only a usable face can be
+  taught, so a back or a jacket never becomes someone. A few faces per person, in other light, from
+  other angles and on each camera, make it reliable; the message after teaching says how many a
+  person has. Someone who was only there for a moment (at a doorbell) can be named afterwards: a
+  face nobody is named for keeps a frame in the history (*Face seen, not named*; at most one a
+  minute, or a closer one).
+- **How it decides**: each face is compared with the faces you taught for each person. A clear
+  match names the person; a less certain one needs a second check that agrees. A person who stays
+  where they were keeps the name while their face is turned away. Close to someone, but not
+  clearly, it asks in the review queue: *Is this Kari?* — **Yes**, another person, a new person or
+  *No, someone I do not know*. A face seen clearly (close enough, and nearly straight at the
+  camera) that matched nobody in two checks in a row turns **Unknown person** on. A face further
+  away or more turned can still be named, but if it looks like nobody it is only *not sure who*:
+  a face seen from the side often does not look like the faces you taught. **Ask me about faces I
+  do not know** (off by default) also asks *Who is this?* about strangers; that keeps frames of
+  strangers in the history longer.
+- It never learns by itself: only faces you name are taught.
+- **People** (in the menu, once there are people or a sensor recognises faces) shows every person
+  with their faces and the camera each was taught on (× forgets one), where they were last seen,
+  people the AI finds alike (*teach both in more light and from more angles*) and faces that look
+  more like someone else than the person they were taught as (**move** gives them that name).
+  **Rename** changes the name (the entity IDs stay), **Remove person** removes their faces and
+  their entities on every camera, **Forget everyone** all of it. A sensor's **Quality** tab shows
+  the same people.
+- **Off** (the switch on the Faces card) removes the people's entities from that sensor; the people
+  stay. Deleting a sensor keeps the faces taught on it. Turned off on every sensor, the face model
+  is let go from memory; **Settings → AI model → Compare and manage the models** can then delete
+  its file.
+- **Where faces are kept**: in the app's own storage, not in the media folder (which Home
+  Assistant's media browser shows to every user) — so they are in Home Assistant backups: keep
+  those encrypted. The numbers the AI computes from a face are kept with them and deleted with the
+  person; an export never contains them.
+
 ## Reading sensors
 
 A reading sensor reads a **number** from a display or counter — the kWh on a power meter, a fuel
@@ -352,7 +438,8 @@ automations.
 ## History
 
 **History** at the top lists what every sensor recorded: state changes, objects that appeared,
-cleared or were filtered away, new values and rejected readings, and frames sent to review. Each
+cleared or were filtered away, faces nobody is named for, new values and rejected readings, and
+frames sent to review. Each
 sensor's **History** tab is the same list for that sensor alone; **Open in all history** there
 takes its filter to the History page.
 
@@ -384,6 +471,9 @@ How long history is kept is set under **Settings → Storage** (below).
 - **Export** (on a sensor) downloads a ZIP with the sensor's settings (region, states, objects or
   reading settings, triggers, review overrides) and all its images with labels — for an object
   sensor, the boxes it was taught and its own labels. Camera passwords are removed from the file.
+  For a sensor where faces were taught it asks first whether to **include the faces** taught on it
+  (unticked by default), with their people's names. An import adds them to the people of the same
+  name (or makes them), but face recognition stays off until you turn it on there.
 - **Import** (dashboard or Settings) adds it as a new sensor (named "… (2)" when the name is
   taken) — also on another installation — and
   trains it automatically. If the camera URL needed a password, enter it again on the sensor's
@@ -398,7 +488,7 @@ two apps at a time (both publish the same entities).
 
 ## AI models
 
-Three models, chosen under **Settings → AI model** for all sensors of a kind:
+The models, chosen under **Settings → AI model** for all sensors of a kind (smallest first):
 
 - **State sensors:** DINOv2 small, 8-bit — included, runs on any CPU including a Raspberry
   Pi 4. A slightly more accurate full-precision version is downloaded on first use (89 MB), and
@@ -411,6 +501,15 @@ Three models, chosen under **Settings → AI model** for all sensors of a kind:
   first use, 15 MB). For a fast PC, **D-FINE M** (79 MB) and **D-FINE L** (125 MB) find more
   small, far-off and partly hidden objects, at about three and four times the time of S. The
   detector is only loaded while at least one object sensor exists.
+- **Faces** (object sensors that recognise faces): never included — downloaded when a sensor
+  first turns face recognition on. Faces are found with **YuNet** (OpenCV Zoo, MIT) and told
+  apart by **SFace** (Apache-2.0, 39 MB with YuNet, about 10 ms per face; fine on a Raspberry Pi 4)
+  or, for a fast PC, **AuraFace v1** (Apache-2.0, 261 MB, about 600 MB of memory, about six
+  times as slow per face), which tells faces apart better in poor light and at an angle. YuNet
+  was trained on a research data set and SFace's paper names research data sets too (their
+  terms are not commercial); AuraFace's makers say its data allows commercial use. Switching
+  compares every taught face again with the new model; the people are kept. Only loaded while a
+  sensor recognises faces.
 - **Reading sensors:** PP-OCRv6 tiny (PaddleOCR) — included, a few milliseconds per reading.
   PP-OCRv6 small is larger and reads unusual fonts more reliably (downloaded on first use,
   21 MB). Mechanical counters are read by the included **VisionState wheel reader** (2 MB, a few
@@ -418,8 +517,11 @@ Three models, chosen under **Settings → AI model** for all sensors of a kind:
   is set to the text reader. There is one wheel reader so far, so there is nothing to choose.
   Only loaded while at least one reading sensor exists.
 
-**Compare the models** (under the choices) lists every model with its rough memory use, time
-per check and how accurate it is compared with the others. A choice you made stays when a later
+**Compare and manage the models** (under the choices) lists every model, from the smallest to
+the largest, with its rough memory use, time per check, how accurate it is compared with the
+others and whether it is included, downloaded or still to download. A downloaded model that is
+not in use can be deleted there to free the disk (it is downloaded again when chosen); a face
+model only while no sensor recognises faces with it. A choice you made stays when a later
 version recommends another model.
 
 ## App options
diff --git a/visionstate/README.md b/visionstate/README.md
index 7f0a9bd..fc36a31 100644
--- a/visionstate/README.md
+++ b/visionstate/README.md
@@ -1,8 +1,9 @@
 # VisionState
 
 Teach a local AI what your camera sees — garage door open, closed or halfway, gate shut — and
-use it as a Home Assistant sensor. Find people, cars and animals without any training, or read the
-number on a power, water or gas meter. Runs 100 % locally on any CPU.
+use it as a Home Assistant sensor. Find people, cars and animals without any training, tell the
+people of your household by their face, or read the number on a power, water or gas meter. Runs
+100 % locally on any CPU.
 
 ![VisionState dashboard](https://raw.githubusercontent.com/oleost/VisionState/main/docs/promo/dashboard.png)
 
diff --git a/visionstate/backend/tests/assets/README.md b/visionstate/backend/tests/assets/README.md
index b3551bc..a198e78 100644
--- a/visionstate/backend/tests/assets/README.md
+++ b/visionstate/backend/tests/assets/README.md
@@ -10,6 +10,17 @@ down to 640 px.
 | `beach.jpg` | one person and several dogs | [Dog owner walking his dogs on the beach..JPG](https://commons.wikimedia.org/wiki/File:Dog_owner_walking_his_dogs_on_the_beach..JPG) |
 | `cat.jpg` | one cat | [House cat sitting next to apartment entrance door looking off to side.jpg](https://commons.wikimedia.org/wiki/File:House_cat_sitting_next_to_apartment_entrance_door_looking_off_to_side.jpg) |
 
+The face tests use NASA portraits, works of the US federal government and so in the public domain,
+from Wikimedia Commons, scaled down:
+
+| File | Who | Source |
+|---|---|---|
+| `face_williams_2006.jpg` | Suni Williams | [Sunita Williams.jpg](https://commons.wikimedia.org/wiki/File:Sunita_Williams.jpg) |
+| `face_williams_2018.jpg` | Suni Williams, twelve years later | [Sunita Williams in 2018.jpg](https://commons.wikimedia.org/wiki/File:Sunita_Williams_in_2018.jpg) |
+| `face_wilmore.jpg` | Butch Wilmore | [Barry Wilmore.jpg](https://commons.wikimedia.org/wiki/File:Barry_Wilmore.jpg) |
+| `face_crew.jpg` | Williams (left) and Wilmore, 2024 | [Boeing CFT Crew Portrait-Sunita Williams and Barry Wilmore (KSC-20240404-PH-NAS01 0001).jpg](https://commons.wikimedia.org/wiki/File:Boeing_CFT_Crew_Portrait-Sunita_Williams_and_Barry_Wilmore_(KSC-20240404-PH-NAS01_0001).jpg) |
+| `face_group.jpg` | A crew of nine with both of them | [The official Expedition 71 crew portrait with CFT crew.jpg](https://commons.wikimedia.org/wiki/File:The_official_Expedition_71_crew_portrait_with_CFT_crew.jpg) |
+
 `counter_red.jpg` is the counter of a real water meter (seven wheels, the last three red, read
 as `0632289`): one image from the checked readings a user exported from VisionState and shared
 in [issue #40](https://github.com/oleost/VisionState/issues/40) under CC0 (the export's
diff --git a/visionstate/backend/tests/assets/face_crew.jpg b/visionstate/backend/tests/assets/face_crew.jpg
new file mode 100644
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diff --git a/visionstate/backend/tests/conftest.py b/visionstate/backend/tests/conftest.py
index 5ffb0d5..9da5af7 100644
--- a/visionstate/backend/tests/conftest.py
+++ b/visionstate/backend/tests/conftest.py
@@ -3,7 +3,7 @@
 
 import pytest
 
-from visionstate import backbones, detectors, readers
+from visionstate import backbones, detectors, faces, readers
 
 MODEL_DIR = Path(os.environ.get("VISIONSTATE_TEST_MODELS", Path(__file__).parent.parent / "models"))
 ASSETS = Path(__file__).parent / "assets"  # real photos (CC0), see assets/README.md
@@ -47,3 +47,11 @@ def init(self, path):
     yield
     for db in opened:
         db.engine.dispose()
+
+
+_sface = faces.RECOGNISERS[faces.DEFAULT_RECOGNISER]
+# Face models are never bundled: on disk after `python -m visionstate.backbones models --all` (as in CI).
+requires_faces = pytest.mark.skipif(
+    not all((MODEL_DIR / spec.filename).exists() for spec in (_default, _detector, faces.DETECTOR, _sface)),
+    reason=f"Face models not downloaded; run `python -m visionstate.backbones {MODEL_DIR} --all`",
+)
diff --git a/visionstate/backend/tests/test_faces.py b/visionstate/backend/tests/test_faces.py
new file mode 100644
index 0000000..4502e40
--- /dev/null
+++ b/visionstate/backend/tests/test_faces.py
@@ -0,0 +1,489 @@
+"""Recognising people by their face: the face pipeline on real photos, matching, and the sensor flow.
+
+The photos are NASA portraits (public domain, tests/assets/README.md): Suni Williams in 2006 and
+2018, Butch Wilmore, the two of them together, and a crew of nine with both of them in it.
+"""
+
+import io
+import json
+import zipfile
+
+import numpy as np
+import pytest
+from fastapi.testclient import TestClient
+from PIL import Image, ImageFilter
+
+from visionstate import detectors, faces
+from visionstate.main import create_app
+from visionstate.settings import FACE_UNKNOWN_KEY, FACES, merge_objects
+
+from .conftest import ASSETS, MODEL_DIR, requires_faces
+from .test_integration import wait_for
+from .test_objects import PhotoCamera, create, settings  # noqa: F401 - fixture
+
+# --- geometry and matching (no models) ---------------------------------------------------------
+
+
+FRAME = (640, 480)  # the frame the drawn faces are in
+
+
+def face_at(points, score=0.9) -> faces.Face:
+    xs, ys = [p[0] for p in points], [p[1] for p in points]
+    return faces.Face((min(xs) - 10, min(ys) - 20, max(xs) + 10, max(ys) + 10), tuple(points), score)
+
+
+def test_align_puts_the_five_points_on_the_template():
+    # The template turned by 20°, scaled by 2.5 and moved: aligning must undo exactly that.
+    angle = np.radians(20)
+    rot = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])
+    points = (faces.TEMPLATE_112 @ rot.T) * 2.5 + np.array([200, 150])
+    m = faces.similarity_transform(points, faces.TEMPLATE_112)
+    mapped = points @ m[:, :2].T + m[:, 2]
+    assert np.allclose(mapped, faces.TEMPLATE_112, atol=1e-6)
+    # Drawn dots at the points land on the template's points in the aligned face.
+    image = Image.new("RGB", (500, 500))
+    for x, y in points:
+        image.paste((255, 255, 255), (round(x) - 5, round(y) - 5, round(x) + 6, round(y) + 6))
+    aligned = np.asarray(faces.align(image, face_at([tuple(p) for p in points])).convert("L"))
+    for x, y in faces.TEMPLATE_112:
+        assert aligned[round(y), round(x)] > 128
+
+
+def test_turn_eye_distance_and_quality_gate():
+    straight = face_at([(40, 50), (80, 50), (60, 70), (45, 90), (75, 90)])  # 60 px wide
+    half = face_at([(40, 50), (80, 50), (75, 70), (45, 90), (75, 90)])  # turned, but can be named
+    sideways = face_at([(40, 50), (80, 50), (85, 70), (45, 90), (75, 90)])
+    assert straight.eye_distance == 40 and straight.width == 60
+    assert faces.turn(straight) == 0 and faces.turn(sideways) > FACES["max_turn"]
+    sharp = Image.effect_noise((112, 112), 60).convert("RGB")
+    assert faces.problem(straight, sharp, 30, FRAME) is None
+    assert faces.problem(straight, sharp, 61, FRAME) == "small"  # the face's width, not its eyes
+    assert faces.problem(half, sharp, 30, FRAME) is None
+    assert faces.problem(sideways, sharp, 30, FRAME) == "turned"
+    # A profile: eyes close together for the width of the face is turned, however big it is.
+    profile = faces.Face((10, 10, 70, 80), ((40, 40), (44, 40), (30, 55), (38, 65), (42, 65)), 0.9)
+    assert faces.problem(profile, sharp, 30, FRAME) == "turned"
+    assert faces.problem(profile, sharp, 61, FRAME) == "small"
+    assert faces.problem(face_at(straight.points, score=0.5), sharp, 30, FRAME) == "unsure"
+    assert faces.problem(straight, Image.new("RGB", (112, 112), (90, 90, 90)), 30, FRAME) == "blurry"
+    # Matching nobody makes a stranger only of a face seen clearly: big enough and nearly straight.
+    assert faces.clearly_seen(straight, 30) and not faces.clearly_seen(straight, 45)
+    assert not faces.clearly_seen(half, 30)
+    assert not faces.clearly_seen(face_at(straight.points, score=0.8), 30)
+    # Cut by the edge of the frame (a person leaning into the camera): the eyes are guessed.
+    assert faces.problem(straight, sharp, 30, (200, 85)) == "cut"
+    top = faces.Face((143, 0, 258, 74), ((174, -4), (225, 0), (198, 17), (175, 35), (214, 38)), 0.79)
+    assert faces.cut_off(top, (480, 640)) and not faces.cut_off(straight, FRAME)
+
+
+def test_head_region_is_the_top_of_a_standing_person_and_all_of_a_close_up():
+    standing = faces.head_region((0.4, 0.1, 0.5, 0.9), 1000, 1000)  # 100 × 800 px
+    assert standing[1] < 100 and standing[3] < 100 + 1.3 * 100 + 20
+    close_up = faces.head_region((0.2, 0.2, 0.6, 0.6), 1000, 1000)  # 400 × 400 px: taken whole
+    assert close_up[3] >= 600
+
+
+def unit(*values):
+    v = np.array(values, dtype=np.float32)
+    return v / np.linalg.norm(v)
+
+
+def index(entries) -> faces.FaceIndex:
+    return faces.FaceIndex(
+        "test", list(range(1, len(entries) + 1)), [e[0] for e in entries], np.stack([e[1] for e in entries])
+    )
+
+
+LIMITS = faces.RECOGNISERS[faces.DEFAULT_RECOGNISER].thresholds
+
+
+def test_a_person_is_the_mean_of_their_closest_faces():
+    kari, eva = unit(1, 0, 0), unit(0, 1, 0)
+    # One of Kari's faces was taught as Eva by mistake: Eva's score is her mean, not that face.
+    idx = index([("kari", kari), ("kari", kari), ("kari", kari), ("eva", eva), ("eva", eva), ("eva", kari)])
+    who = faces.identify(idx, kari)
+    assert who.label == "kari" and who.similarity == 1.0 and who.margin == pytest.approx(1 - 1 / 3, abs=1e-3)
+    assert faces.decide(who, LIMITS) == "match"
+    assert faces.decide(faces.identify(idx, unit(0, 0, 1)), LIMITS) == "unknown"
+    assert faces.decide(faces.Identity(None, 0, 0, None), LIMITS) == "unknown"
+    between = faces.Identity("kari", (LIMITS["ask_similarity"] + LIMITS["match_similarity"]) / 2, 0.3, 1)
+    assert faces.decide(between, LIMITS) == "ask"
+    # Close to two people at once: not a match.
+    assert faces.decide(faces.Identity("kari", 0.9, 0.01, 1), LIMITS) == "ask"
+    # A person who stays keeps their name down to keep_similarity.
+    staying = faces.Identity("kari", LIMITS["keep_similarity"] + 0.01, 0.3, 1)
+    assert faces.decide(staying, LIMITS, kept="kari") == "match"
+    assert faces.decide(staying, LIMITS) != "match"
+
+
+def test_only_a_clearly_seen_face_that_matches_nobody_is_a_stranger():
+    from visionstate.engine.faces import FacesMixin
+
+    idx = index([("kari", unit(1, 0, 0))])
+    box = [0.1, 0.1, 0.5, 0.9]
+
+    def check(clear: bool, before: list[dict]) -> dict:
+        det = {"key": "person", "box": box, "face": {"problem": None}}
+        face_settings = {"ask_unknown": False}
+        FacesMixin._name_face(None, det, before, idx, unit(0, 0, 1), LIMITS, face_settings, clear)  # type: ignore[arg-type]
+        return det
+
+    # Turned or small: matching nobody may be the angle, so it never counts towards a stranger.
+    det = check(False, [])
+    assert det["face"]["state"] == "unclear" and "label" not in det
+    det = check(False, [det])
+    assert "label" not in det
+    # Seen clearly twice: a stranger, who stays one while turned away.
+    det = check(True, [check(True, [])])
+    assert det["label"] == FACE_UNKNOWN_KEY
+    assert check(False, [det])["label"] == FACE_UNKNOWN_KEY
+
+
+def test_every_face_model_is_pinned_and_described():
+    for spec in (faces.DETECTOR, *faces.RECOGNISERS.values()):
+        assert "/resolve/" in spec.url and len(spec.sha256) == 64 and spec.size > 0
+        assert spec.description and spec.license and spec.data and spec.source
+        assert not spec.bundled  # downloaded only when a sensor turns faces on
+    for spec in faces.RECOGNISERS.values():
+        t = spec.thresholds
+        assert t["ask_similarity"] < t["keep_similarity"] < t["match_similarity"] < t["sure_similarity"]
+        assert spec.footprint.ram_mb > 0 and spec.footprint.time_ms > 0
+    ranks = [s.footprint.accuracy for s in sorted(faces.RECOGNISERS.values(), key=lambda s: s.footprint.time_ms)]
+    assert ranks == sorted(ranks)
+
+
+# --- the models on real photos ---------------------------------------------------------------------
+
+
+def on_disk(spec):
+    return pytest.param(
+        spec,
+        id=spec.id,
+        marks=pytest.mark.skipif(
+            not all((MODEL_DIR / s.filename).exists() for s in (spec, faces.DETECTOR)),
+            reason=f"{spec.id} not downloaded (backbones models --all)",
+        ),
+    )
+
+
+def photo_faces(recogniser, name):
+    """(x of the person box, embedding, face) of every usable face in a photo."""
+    image = Image.open(ASSETS / f"{name}.jpg").convert("RGB")
+    spec = detectors.DETECTORS[detectors.DEFAULT_DETECTOR]
+    people = [d for d in detectors.Detector(spec, MODEL_DIR / spec.filename).detect(image, 0.6) if d.key == "person"]
+    finder = faces.FaceDetector(faces.DETECTOR, MODEL_DIR / faces.DETECTOR.filename)
+    regions = [faces.head_region(p.box, image.width, image.height) for p in people]
+    result = []
+    for person, region, face in zip(people, regions, finder.detect([image.crop(r) for r in regions]), strict=True):
+        assert face is not None, f"no face in a person box of {name}"
+        moved = face.moved(region[0], region[1], 1.0)
+        aligned = faces.align(image, moved)
+        assert faces.problem(moved, aligned, 30, image.size) is None
+        result.append((person.box[0], recogniser.embed([aligned])[0], moved))
+    return result
+
+
+@pytest.mark.parametrize("spec", [on_disk(s) for s in faces.RECOGNISERS.values()])
+def test_every_face_model_tells_people_apart_in_real_photos(spec):
+    recogniser = faces.Recogniser(spec, MODEL_DIR / spec.filename)
+    limits = spec.thresholds
+    [(_, williams_2006, _)] = photo_faces(recogniser, "face_williams_2006")
+    [(_, williams_2018, _)] = photo_faces(recogniser, "face_williams_2018")
+    [(_, wilmore, _)] = photo_faces(recogniser, "face_wilmore")
+    assert williams_2006 @ williams_2018 >= limits["match_similarity"]  # twelve years apart
+    assert williams_2006 @ wilmore < limits["ask_similarity"]
+    idx = index([("williams", williams_2006), ("williams", williams_2018), ("wilmore", wilmore)])
+    # Both of them together (2024): each found, the right way round.
+    crew = sorted(photo_faces(recogniser, "face_crew"), key=lambda c: c[0])
+    assert [faces.identify(idx, v).label for _, v, _ in crew] == ["williams", "wilmore"]
+    assert all(faces.decide(faces.identify(idx, v), limits) == "match" for _, v, _ in crew)
+    # A crew of nine: the two of them, and nobody else is taken for them.
+    named = [faces.decide(faces.identify(idx, v), limits) for _, v, _ in photo_faces(recogniser, "face_group")]
+    assert named.count("ask") == 0 and named.count("match") >= 2
+
+
+@requires_faces
+def test_a_blurred_or_small_face_is_no_face():
+    image = Image.open(ASSETS / "face_williams_2018.jpg").convert("RGB")
+    finder = faces.FaceDetector(faces.DETECTOR, MODEL_DIR / faces.DETECTOR.filename)
+    [face] = finder.detect([image])
+    assert face is not None and faces.problem(face, faces.align(image, face), 30, image.size) is None
+    blurred = image.filter(ImageFilter.GaussianBlur(6))
+    [soft] = finder.detect([blurred])
+    assert soft is None or faces.problem(soft, faces.align(blurred, soft), 30, blurred.size) in ("blurry", "unsure")
+    tiny = image.resize((image.width // 8, image.height // 8))
+    [small] = finder.detect([tiny])
+    assert small is None or faces.problem(small, faces.align(tiny, small), 30, tiny.size) == "small"
+
+
+# --- the sensor --------------------------------------------------------------------------------------
+
+
+def view(client, sid):
+    return client.get(f"/api/v1/sensors/{sid}").json()
+
+
+def live(client, sid):
+    return {c["key"]: c for c in view(client, sid)["objects"]["live"]}
+
+
+def person_with_face(client, sid):
+    """The frame of the last check and its person box with a usable face (or None)."""
+    v = view(client, sid)
+    for d in v["objects"]["detections"]:
+        if d["key"] == "person" and (d.get("face") or {}).get("box") and not d["face"].get("problem"):
+            return v["live"]["frame_id"], d
+    return None
+
+
+def faces_patch(enabled, **extra):
+    return {"objects": {"classes": ["person"], "clear_after_s": 0, "faces": {"enabled": enabled, **extra}}}
+
+
+@requires_faces
+def test_people_are_taught_recognised_forgotten_exported(settings):  # noqa: F811
+    camera = PhotoCamera("face_williams_2006")
+    with TestClient(create_app(settings)) as client:
+        rt = client.app.state.runtime
+        rt.grabber = camera
+        sid = create(client, objects={"classes": ["person"], "clear_after_s": 0}, interval_s=1).json()["id"]
+        # Off by default: no model, no face search, no "unknown person".
+        assert view(client, sid)["objects"]["faces"]["enabled"] is False
+        assert wait_for(lambda: live(client, sid)["person"]["on"], timeout=60)
+        assert rt.face_model is None and FACE_UNKNOWN_KEY not in live(client, sid)
+        body = {"frame_id": view(client, sid)["live"]["frame_id"], "box": [0, 0, 1, 1], "new_person": "Suni"}
+        assert client.post(f"/api/v1/sensors/{sid}/faces", json=body).status_code == 400
+
+        # Turned on: the model loads, faces are looked for, and "unknown person" is an entity.
+        assert client.patch(f"/api/v1/sensors/{sid}", json=faces_patch(True)).status_code == 200
+        assert wait_for(lambda: client.get("/api/v1/status").json()["faces"]["loaded"], timeout=60)
+        assert "binary_sensor.beach_unknown_person" in view(client, sid)["entity_ids"]
+        assert wait_for(lambda: person_with_face(client, sid), timeout=30)
+        stats = view(client, sid)["objects"]["face_stats"]
+        assert stats["looked"] >= 1 and stats["usable"] >= 1
+        assert stats["frame"] == list(Image.open(ASSETS / "face_williams_2006.jpg").size)
+        # A face nobody is named for keeps its frame in the history (once, not every check).
+        seen = {"sensor": sid, "event": "face_seen", "limit": 10}
+        assert wait_for(lambda: client.get("/api/v1/history", params=seen).json()["total"] == 1, timeout=30)
+        row = client.get("/api/v1/history", params=seen).json()["items"][0]
+        assert row["published_key"] == "face" and row["has_frame"] and not row["is_change"]
+
+        def teach(name):
+            frame_id, person = person_with_face(client, sid)
+            body = {"frame_id": frame_id, "box": person["box"], "score": person["score"], "new_person": name}
+            resp = client.post(f"/api/v1/sensors/{sid}/faces", json=body)
+            assert resp.status_code == 201, resp.text
+            return resp.json()
+
+        suni = teach("Suni")
+        assert suni["label"] == "suni" and suni["faces"] == 1
+        assert "binary_sensor.beach_suni" in view(client, sid)["entity_ids"]
+        assert wait_for(lambda: live(client, sid).get("suni", {}).get("on"), timeout=30)
+        # A box without a face cannot be taught: a back or a jacket never becomes someone.
+        frame_id = view(client, sid)["live"]["frame_id"]
+        check = client.post(f"/api/v1/sensors/{sid}/faces/check", json={"frame_id": frame_id, "box": [0, 0.8, 0.3, 1]})
+        assert check.status_code == 200 and check.json()["problem"] == "none" and check.json()["message"]
+        nobody = {"frame_id": frame_id, "box": [0, 0.8, 0.3, 1], "person": "suni"}
+        assert client.post(f"/api/v1/sensors/{sid}/faces", json=nobody).status_code == 400
+
+        camera.name = "face_wilmore"
+        assert wait_for(lambda: not live(client, sid)["suni"]["on"] and person_with_face(client, sid), timeout=30)
+        teach("Butch")
+
+        # Both together, and in a crew of nine: each is named, the others are unknown people.
+        camera.name = "face_crew"
+        assert wait_for(lambda: live(client, sid)["suni"]["on"] and live(client, sid)["butch"]["on"], timeout=30)
+        camera.name = "face_group"
+        assert wait_for(lambda: live(client, sid)[FACE_UNKNOWN_KEY]["on"], timeout=60)
+        assert live(client, sid)[FACE_UNKNOWN_KEY]["count"] <= 7
+        assert wait_for(lambda: live(client, sid)["suni"]["on"] and live(client, sid)["butch"]["on"], timeout=30)
+
+        people = client.get("/api/v1/people").json()
+        assert [p["name"] for p in people["people"]] == ["Suni", "Butch"] and people["sensors"] == [sid]
+        face_id = people["people"][0]["faces"][0]["id"]
+        first = people["people"][0]["faces"][0]
+        assert first["suspect"] is None and first["sensor_id"] == sid and people["look_alikes"] == []
+        # Each person's own entity says where they are seen: on this sensor now.
+        assert people["people"][0]["seen"]["now"] == "beach" and people["people"][0]["seen"]["sensor_id"] == sid
+        image = client.get(f"/api/v1/faces/{face_id}/image")
+        assert image.status_code == 200 and image.headers["content-type"] == "image/jpeg"
+        # Faces are kept in the app's own storage, never in the media folder.
+        assert list((settings.data_dir / "faces").glob("*.jpg"))
+        assert not list((settings.media_dir / "samples").rglob("*.jpg"))
+        # Not among the boxes taught by their look.
+        assert client.get(f"/api/v1/sensors/{sid}/taught").json()["examples"] == []
+        assert view(client, sid)["objects"]["taught"] == 0 and view(client, sid)["objects"]["taught_faces"] == 2
+
+        # Shared: another sensor that recognises faces knows them without being taught.
+        other = create(client, name="Door", objects={"classes": ["person"], "clear_after_s": 0}, interval_s=1)
+        door = other.json()["id"]
+        assert client.patch(f"/api/v1/sensors/{door}", json=faces_patch(True)).status_code == 200
+        assert "binary_sensor.door_suni" in view(client, door)["entity_ids"]
+        assert wait_for(lambda: live(client, door).get("suni", {}).get("on"), timeout=30)
+        # Deleting a sensor keeps the faces taught on it.
+        assert client.delete(f"/api/v1/sensors/{door}").status_code == 204
+
+        # Export: the faces taught on this camera only when asked for, with their people's names.
+        plain = zipfile.ZipFile(io.BytesIO(client.get(f"/api/v1/sensors/{sid}/export").content))
+        manifest = json.loads(plain.read("manifest.json"))
+        assert manifest["samples"] == [] and manifest["faces"] == []
+        with_faces = client.get(f"/api/v1/sensors/{sid}/export", params={"faces": True}).content
+        stored = json.loads(zipfile.ZipFile(io.BytesIO(with_faces)).read("manifest.json"))["faces"]
+        assert sorted(f["person"] for f in stored) == ["butch", "suni"]
+        imported = client.post("/api/v1/import", files={"file": ("b.zip", with_faces, "application/zip")})
+        assert imported.status_code == 201, imported.text
+        copy = imported.json()["id"]
+        assert view(client, copy)["objects"]["faces"]["enabled"] is False  # turned on there, by the user
+        # The same people (no copies of them), each with the face again, from the new sensor.
+        people = client.get("/api/v1/people").json()["people"]
+        assert [(p["key"], len(p["faces"])) for p in people] == [("suni", 2), ("butch", 2)]
+
+        # Renamed: the key (and entity IDs) stay. A face under the wrong name is moved; forgetting
+        # a person removes their faces and entities everywhere.
+        assert client.patch("/api/v1/people/suni", json={"name": "Sunita"}).json() == {"key": "suni", "name": "Sunita"}
+        assert client.patch("/api/v1/people/suni", json={"name": "butch"}).status_code == 400  # taken
+        assert client.patch(f"/api/v1/faces/{face_id}", json={"person": "butch"}).status_code == 200
+        assert client.delete(f"/api/v1/faces/{face_id}").status_code == 204
+        assert client.delete("/api/v1/people/suni").status_code == 204
+        assert "binary_sensor.beach_suni" not in view(client, sid)["entity_ids"]
+        assert client.delete("/api/v1/people").status_code == 204
+        assert client.get("/api/v1/people").json()["people"] == []
+        assert not list((settings.data_dir / "faces").glob("*.jpg"))
+
+        # Turned off everywhere: the face model is let go; the files can then be deleted.
+        assert client.delete(f"/api/v1/models/face_models/{faces.DEFAULT_RECOGNISER}").status_code == 409
+        assert client.patch(f"/api/v1/sensors/{sid}", json=faces_patch(False)).status_code == 200
+        assert wait_for(lambda: rt.face_model is None, timeout=10)
+        assert FACE_UNKNOWN_KEY not in live(client, sid)
+
+
+@requires_faces
+def test_a_face_question_is_answered_in_the_review_queue(settings):  # noqa: F811
+    camera = PhotoCamera("face_williams_2006")
+    with TestClient(create_app(settings)) as client:
+        rt = client.app.state.runtime
+        rt.grabber = camera
+        patch = faces_patch(True, ask_unknown=True)["objects"]
+        sid = create(client, objects=patch, interval_s=1).json()["id"]
+        assert wait_for(lambda: person_with_face(client, sid), timeout=90)
+        frame_id, person = person_with_face(client, sid)
+        body = {"frame_id": frame_id, "box": person["box"], "new_person": "Suni"}
+        assert client.post(f"/api/v1/sensors/{sid}/faces", json=body).status_code == 201
+        # Butch is nobody yet: "Who is this?" (asked about faces nobody was taught for).
+        camera.name = "face_wilmore"
+
+        def question():
+            items = client.get("/api/v1/review").json()["items"]
+            return next((i for i in items if (i["probs"].get("ask") or {}).get("face")), None)
+
+        assert wait_for(question, timeout=60)
+        item = question()
+        assert item["probs"]["ask"]["label"] == FACE_UNKNOWN_KEY and item["probs"]["ask"]["face"]
+        assert {"key": "suni", "name": "Suni", "parent": "person", "face": True} in item["sensor"]["labels"]
+        bad = client.post(f"/api/v1/review/{item['id']}", json={"action": "new"})
+        assert bad.status_code == 400
+        resp = client.post(f"/api/v1/review/{item['id']}", json={"action": "new", "value": "Butch"})
+        assert resp.status_code == 200, resp.text
+        people = client.get("/api/v1/people").json()["people"]
+        assert {p["name"]: len(p["faces"]) for p in people} == {"Suni": 1, "Butch": 1}
+        assert wait_for(lambda: live(client, sid).get("butch", {}).get("on"), timeout=30)
+        assert merge_objects(None)["faces"]["ask_unknown"] is False  # off by default
+
+
+def test_a_face_model_that_cannot_be_downloaded_leaves_the_sensor_working(settings, monkeypatch, tmp_path):  # noqa: F811
+    from visionstate import backbones
+
+    def offline(*_args, **_kwargs):
+        raise OSError("no internet")
+
+    settings.bundled_models_dir = tmp_path / "empty"
+    monkeypatch.setattr(backbones, "download", offline)
+    with TestClient(create_app(settings)) as client:
+        rt = client.app.state.runtime
+        assert client.post("/api/v1/faces/retry").status_code == 200
+        assert wait_for(lambda: rt.face_status()["error"], timeout=10)
+        error = rt.face_status()["error"]
+        assert faces.DETECTOR.filename in error and "by hand" in error
+        assert rt.face_model is None and not rt.face_status()["loading"]
+        # No retry by itself: only when asked.
+        assert rt.wanted_faces() is None and rt.face_status()["error"] == error
+        assert client.post("/api/v1/faces/retry").status_code == 200
+        assert wait_for(lambda: rt.face_status()["error"], timeout=10)
+        settings_view = client.get("/api/v1/settings").json()
+        assert [m["id"] for m in settings_view["face_models"]] == list(faces.RECOGNISERS)
+        assert all(not m["installed"] and m["data"] for m in settings_view["face_models"])
+        assert (
+            client.put(
+                "/api/v1/settings", json={"backbone": settings_view["backbone"], "face_model": "nope"}
+            ).status_code
+            == 400
+        )
+        resp = client.put("/api/v1/settings", json={"backbone": settings_view["backbone"], "face_model": "auraface-v1"})
+        assert resp.status_code == 200 and resp.json()["face_model"] == "auraface-v1"
+
+
+def test_face_limits_global_and_per_sensor(settings):  # noqa: F811
+    with TestClient(create_app(settings)) as client:
+        rt = client.app.state.runtime
+        view = client.get("/api/v1/face-limits").json()
+        defaults = faces.tunable_defaults(view["model"])
+        assert view["defaults"] == defaults == view["effective"] and not any(view["values"].values())
+        assert defaults["match_similarity"] == faces.recogniser_spec(view["model"]).match_similarity
+        assert set(client.get("/api/v1/config").json()["face_limits"]) == set(defaults)
+
+        # Global: one set, the rest follow the face model; nonsense is refused.
+        view = client.put("/api/v1/face-limits", json={"min_face_px": 40}).json()
+        assert view["effective"]["min_face_px"] == 40 and rt.global_faces == {"min_face_px": 40}
+        assert client.put("/api/v1/face-limits", json={"ask_similarity": 0.9}).status_code == 400  # above match
+        assert client.put("/api/v1/face-limits", json={"min_face_px": 5}).status_code == 422
+
+        # A sensor follows the global value until it has its own.
+        sid = create(client, objects={"classes": ["person"]}).json()["id"]
+        assert rt.face_limits(rt.load_sensor(sid))["min_face_px"] == 40
+        on = {"objects": {"faces": {"enabled": False, "ask_unknown": False, "min_face_px": 24}}}
+        assert client.patch(f"/api/v1/sensors/{sid}", json=on).status_code == 200
+        assert rt.face_limits(rt.load_sensor(sid))["min_face_px"] == 24
+        assert client.get(f"/api/v1/sensors/{sid}").json()["objects"]["faces"]["min_face_px"] == 24
+        bad = {"objects": {"faces": {"match_similarity": 0.9, "sure_similarity": 0.8}}}
+        assert client.patch(f"/api/v1/sensors/{sid}", json=bad).status_code == 400  # match above sure
+        back = {"objects": {"faces": {"enabled": False, "ask_unknown": False}}}
+        client.patch(f"/api/v1/sensors/{sid}", json=back)
+        assert rt.face_limits(rt.load_sensor(sid))["min_face_px"] == 40
+
+
+def test_a_downloaded_model_not_in_use_can_be_deleted(settings):  # noqa: F811
+    with TestClient(create_app(settings)) as client:
+        info = client.get("/api/v1/settings").json()
+        in_use = next(b for b in info["backbones"] if b["id"] == info["backbone"])
+        assert in_use["in_use"] and not in_use["downloaded"]  # included in the app, and in use
+        assert client.delete(f"/api/v1/models/backbones/{info['backbone']}").status_code == 409
+        other = next(d for d in info["detectors"] if d["id"] != info["detector"])
+        assert not other["in_use"]
+        assert client.delete(f"/api/v1/models/detectors/{other['id']}").status_code == 404  # not downloaded
+        assert client.delete("/api/v1/models/detectors/nope").status_code == 404
+
+        # Downloaded and not in use: deleted.
+        spec = detectors.DETECTORS[other["id"]]
+        settings.models_dir.mkdir(parents=True, exist_ok=True)
+        (settings.models_dir / spec.filename).write_bytes(b"model")
+        assert next(d for d in client.get("/api/v1/settings").json()["detectors"] if d["id"] == spec.id)["downloaded"]
+        resp = client.delete(f"/api/v1/models/detectors/{spec.id}")
+        assert resp.status_code == 200 and not (settings.models_dir / spec.filename).exists()
+
+        # A face model goes with the face detector once it is the last one.
+        recogniser = faces.RECOGNISERS["auraface-v1"]
+        for name in (recogniser.filename, faces.DETECTOR.filename):
+            (settings.models_dir / name).write_bytes(b"model")
+        assert client.delete("/api/v1/models/face_models/auraface-v1").status_code == 200
+        assert not (settings.models_dir / recogniser.filename).exists()
+        assert not (settings.models_dir / faces.DETECTOR.filename).exists()
+
+
+def test_each_person_has_an_entity_saying_where_they_are():
+    from visionstate.mqtt import person_discovery_messages, person_topics
+
+    [(topic, config)] = person_discovery_messages("homeassistant", [("ole", "Ole")])
+    assert topic == "homeassistant/sensor/visionstate/person_ole/config"
+    assert config["default_entity_id"] == "sensor.visionstate_ole" and config["name"] == "Ole"
+    assert config["unique_id"] == "visionstate_person_ole" and config["device"]["identifiers"] == ["visionstate"]
+    assert config["state_topic"] == person_topics("ole")["state"] == "visionstate/people/ole/state"
diff --git a/visionstate/backend/visionstate/api/common.py b/visionstate/backend/visionstate/api/common.py
index 831b9ce..ec44e7b 100644
--- a/visionstate/backend/visionstate/api/common.py
+++ b/visionstate/backend/visionstate/api/common.py
@@ -12,11 +12,14 @@
 from sqlalchemy.orm import Session
 
 from .. import detectors, imaging, teach
-from ..db import ModelInfo, Sample, SampleLabel, Sensor
-from ..engine import LiveState, ObjectTrack, Runtime, SensorConfig
+from ..db import Face, ModelInfo, Sample, SampleLabel, Sensor
+from ..engine import LiveState, ObjectTrack, Runtime, SensorConfig, with_people
 from ..mqtt import main_entities
 from ..settings import (
     COUNTER_READERS,
+    FACE_DEFAULTS,
+    FACE_LIMITS,
+    FACE_UNKNOWN_KEY,
     KIND_OBJECTS,
     KIND_READING,
     LIGHT_DOMAINS,
@@ -39,6 +42,7 @@
     TRIGGER_STATE_MAX_LENGTH,
     UNKNOWN_STATE,
     active_custom,
+    faces_on,
     merge_objects,
     merge_reading,
     merge_review,
@@ -207,6 +211,43 @@ def check_teach(limits: dict) -> None:
         raise HTTPException(400, "“Keeps that answer” must not be above “Recognised as a box you taught”")
 
 
+_flo = {k: v[0] for k, v in FACE_LIMITS.items()}
+_fhi = {k: v[1] for k, v in FACE_LIMITS.items()}
+
+
+class FaceLimits(BaseModel):
+    """The tunable face limits (settings.FACE_TUNABLE). None = inherit: globally the default (the
+    face model's similarities), on a sensor the global value."""
+
+    min_face_px: float | None = Field(None, ge=_flo["min_face_px"], le=_fhi["min_face_px"])
+    match_similarity: float | None = Field(None, ge=_flo["match_similarity"], le=_fhi["match_similarity"])
+    sure_similarity: float | None = Field(None, ge=_flo["sure_similarity"], le=_fhi["sure_similarity"])
+    ask_similarity: float | None = Field(None, ge=_flo["ask_similarity"], le=_fhi["ask_similarity"])
+    keep_similarity: float | None = Field(None, ge=_flo["keep_similarity"], le=_fhi["keep_similarity"])
+
+    def stored(self) -> dict:
+        return {k: v for k, v in self.model_dump().items() if v is not None}
+
+
+def check_faces(limits: dict) -> None:
+    """Effective face limits must make sense together: asking and keeping below a match, a match
+    not above being sure at once."""
+    if limits["ask_similarity"] > limits["match_similarity"]:
+        raise HTTPException(400, "“Asks you in the review queue” must not be above “Recognised”")
+    if limits["keep_similarity"] > limits["match_similarity"]:
+        raise HTTPException(400, "“Keeps the name” must not be above “Recognised”")
+    if limits["match_similarity"] > limits["sure_similarity"]:
+        raise HTTPException(400, "“Recognised” must not be above “Sure at once”")
+
+
+class FacesIn(FaceLimits):
+    """Recognising people by their face on an object sensor: on or off, asking about strangers,
+    and its own face limits (unset ones follow the global ones). Defaults: settings.FACE_*."""
+
+    enabled: bool = FACE_DEFAULTS["enabled"]
+    ask_unknown: bool = FACE_DEFAULTS["ask_unknown"]
+
+
 class ObjectsIn(BaseModel):
     """What an object sensor looks for. Defaults and limits: settings.OBJECT_*."""
 
@@ -217,12 +258,15 @@ class ObjectsIn(BaseModel):
     )
     use_taught: bool = OBJECT_DEFAULTS["use_taught"]
     teach: TeachLimits | None = None  # this sensor's own teaching limits; unset ones follow the global ones
-    # Own labels ("custom") are made by teaching a box (api/teach.py), never set here.
+    faces: FacesIn | None = None  # recognising people by their face; unset keeps what the sensor has
+    # Own labels ("custom") are made by teaching a box (api/teach.py) or a face (api/faces.py), never set here.
 
     def stored(self, exclude_unset: bool = False) -> dict:
-        data = self.model_dump(exclude_unset=exclude_unset, exclude={"teach"})
+        data = self.model_dump(exclude_unset=exclude_unset, exclude={"teach", "faces"})
         if self.teach is not None or not exclude_unset:
             data["teach"] = self.teach.stored() if self.teach else {}
+        if self.faces is not None:
+            data["faces"] = self.faces.model_dump(exclude_none=True)
         return data
 
     @field_validator("classes")
@@ -372,14 +416,16 @@ def entity_ids(rt: Runtime, sensor: Sensor) -> list[str]:
 
 
 def objects_view(session: Session, sensor: Sensor, live: LiveState | None) -> dict | None:
-    """An object sensor's part of its view: each class and own label, live, and what was taught."""
+    """An object sensor's part of its view: each class and own label (with the people while it
+    recognises faces), live, and what was taught."""
     if sensor.kind != KIND_OBJECTS:
         return None
-    settings = merge_objects(sensor.objects)
+    settings = with_people(merge_objects(sensor.objects), session)
     parents = {label["key"]: label["parent"] for label in active_custom(settings)}
     tracks = live.tracks if live else {}
     per_class = []
-    for key in [*settings["classes"], *parents]:
+    unknown = [FACE_UNKNOWN_KEY] if faces_on(settings) else []  # recognising faces: "unknown person"
+    for key in [*settings["classes"], *parents, *unknown]:
         track = tracks.get(key) or ObjectTrack()
         per_class.append(
             {
@@ -397,12 +443,23 @@ def objects_view(session: Session, sensor: Sensor, live: LiveState | None) -> di
         )
     ).all()
     usable = [(label, detected) for label, detected in taught if label is not None and teach.usable(label, parents)]
+    taught_faces = session.scalar(select(func.count()).select_from(Face).where(Face.sensor_id == sensor.id)) or 0
+    stats = list(live.face_stats) if live else []
     return {
         **settings,
         "live": per_class,
         "detections": live.detections if live else [],
         "taught": len(taught),  # boxes taught (Quality tab, first-time question)
+        "taught_faces": taught_faces,  # faces taught on this camera (export)
         "taught_keys": sorted(teach.compared_keys(usable, parents)),  # classes compared with them
+        # Faces: of the last person boxes looked at, how many had a usable face, and why not.
+        "face_stats": {
+            "looked": len(stats),
+            "usable": stats.count("ok"),
+            "problems": {p: stats.count(p) for p in sorted(set(stats) - {"ok"})},
+            "frame": list(live.face_frame) if live and live.face_frame else None,  # width, height
+            "min_face_px": live.face_min_px if live else None,  # the smallest face it was looked at with
+        },
     }
 
 
diff --git a/visionstate/backend/visionstate/api/faces.py b/visionstate/backend/visionstate/api/faces.py
new file mode 100644
index 0000000..5c66059
--- /dev/null
+++ b/visionstate/backend/visionstate/api/faces.py
@@ -0,0 +1,391 @@
+"""Recognising people by their face (docs/FACE_RECOGNITION_PLAN.md). The people and their faces
+are shared by every object sensor that recognises faces: a face is taught on any of them (a
+person box in a frame of that sensor), and the people are listed, renamed and forgotten for all
+of them at once. Turning it on and off is a setting of each sensor (``objects.faces``); the face
+model is chosen in /settings."""
+
+from __future__ import annotations
+
+import asyncio
+from datetime import datetime
+
+import numpy as np
+from fastapi import APIRouter, HTTPException, Request, Response
+from fastapi.responses import FileResponse
+from PIL import Image
+from pydantic import BaseModel, Field, field_validator
+from sqlalchemy import func, select
+from sqlalchemy.orm import Session
+
+from .. import detectors, faces, imaging
+from ..db import Face, Person, Sensor
+from ..engine import Runtime, SensorConfig
+from ..settings import FACES, KIND_OBJECTS, TEACH, merge_objects
+from .common import API_PREFIX, iso, runtime, slugify
+from .teach import RESERVED_KEYS, frame_image
+
+router = APIRouter(prefix=f"{API_PREFIX}/sensors", tags=["faces"])
+people_router = APIRouter(prefix=API_PREFIX, tags=["faces"])
+
+# Why a face cannot be taught, in words (faces.problem).
+PROBLEMS = {
+    "none": "No face found here. Pick a moment where the face is visible.",
+    "cut": "The edge of the picture cuts the face here. Pick a moment where the whole face is in view.",
+    "small": "The face is too small here. Pick a moment where the person is closer to the camera.",
+    "turned": "The face is turned away too far here. Pick a moment where they look towards the camera.",
+    "blurry": "The face is blurry here. Pick a sharper moment.",
+    "unsure": "This does not clearly look like a face. Pick another moment.",
+}
+
+
+class FaceBoxIn(BaseModel):
+    """A person box on the frame a check analysed (``frame_id``) or on a history frame."""
+
+    frame_id: str | None = Field(default=None, max_length=64)
+    history_id: int | None = None
+    box: list[float] = Field(min_length=4, max_length=4)  # x1, y1, x2, y2 normalised to the frame
+
+    @field_validator("box")
+    @classmethod
+    def _valid_box(cls, value: list[float]) -> list[float]:
+        x1, y1, x2, y2 = (min(max(float(v), 0.0), 1.0) for v in value)
+        if x2 - x1 < TEACH["min_box"] or y2 - y1 < TEACH["min_box"]:
+            raise ValueError("The box is too small")
+        return [x1, y1, x2, y2]
+
+
+class FaceTeachIn(FaceBoxIn):
+    """Who the face in a person box is: one of the people, or a new person."""
+
+    score: float | None = Field(default=None, ge=0, le=1)
+    person: str | None = Field(default=None, max_length=64)
+    new_person: str | None = Field(default=None, min_length=1, max_length=64)
+
+
+class FaceMoveIn(BaseModel):
+    person: str = Field(max_length=64)
+
+
+class PersonIn(BaseModel):
+    name: str = Field(min_length=1, max_length=64)
+
+
+# --- The people -----------------------------------------------------------------------------------
+
+
+def person_by_key(s: Session, key: str) -> Person:
+    person = s.scalar(select(Person).where(Person.key == key))
+    if person is None:
+        raise HTTPException(404, f"No person called {key!r}")
+    return person
+
+
+def _check_name(s: Session, name: str, keep: int | None = None) -> str:
+    name = name.strip()
+    if not name:
+        raise HTTPException(400, "Give the person a name")
+    for other in s.scalars(select(Person)):
+        if other.id != keep and other.name.lower() == name.lower():
+            raise HTTPException(400, f"There is already a person called {name}")
+    return name
+
+
+def taken_keys(s: Session) -> set[str]:
+    """Keys a new person or own label may not have: objects VisionState knows, every own label of
+    every sensor and every person (all can be entities of one sensor)."""
+    taken = set(detectors.LABELS.by_key) | RESERVED_KEYS
+    taken |= set(s.scalars(select(Person.key)))
+    for objects in s.scalars(select(Sensor.objects).where(Sensor.kind == KIND_OBJECTS)):
+        taken |= {label["key"] for label in merge_objects(objects)["custom"]}
+    return taken
+
+
+def new_person(s: Session, name: str) -> Person:
+    """A new person; their key never clashes with an object, an own label or another person."""
+    if (s.scalar(select(func.count()).select_from(Person)) or 0) >= FACES["max_people"]:
+        raise HTTPException(400, f"VisionState can know at most {FACES['max_people']} people")
+    name = _check_name(s, name)
+    taken = taken_keys(s)
+    base = slugify(name, "person")
+    key, n = base, 2
+    while key in taken:
+        key, n = f"{base}_{n}", n + 1
+    person = Person(key=key, name=name)
+    s.add(person)
+    s.flush()
+    return person
+
+
+# --- Teaching a face on a sensor ------------------------------------------------------------------
+
+
+async def _face_model(rt: Runtime) -> faces.FaceModel:
+    model = rt.wanted_faces()
+    if model is None:
+        status = rt.face_status()
+        raise HTTPException(409, status["error"] or "The face model is still loading. Try again in a moment.")
+    return model
+
+
+async def _find(
+    rt: Runtime, sensor_id: int, body: FaceBoxIn, cfg: SensorConfig
+) -> tuple[Image.Image, str, faces.Face | None, str | None]:
+    """The frame, its origin, the face in the person box (frame pixels) and why it cannot be used."""
+    model = await _face_model(rt)
+    image, origin = await frame_image(rt, sensor_id, body.frame_id, body.history_id)
+    [(face, aligned)] = await rt.find_faces(model, image, [body.box])
+    if face is None or aligned is None:
+        return image, origin, None, "none"
+    min_px = rt.face_limits(cfg)["min_face_px"]
+    return image, origin, face, faces.problem(face, aligned, min_px, (image.width, image.height))
+
+
+def _config(rt: Runtime, sensor_id: int) -> SensorConfig:
+    cfg = rt.load_sensor(sensor_id)
+    if cfg is None or not cfg.is_objects:
+        raise HTTPException(404, "Sensor not found")
+    if not cfg.faces_on:
+        raise HTTPException(400, "Turn on face recognition for this sensor first (its Settings tab)")
+    return cfg
+
+
+@router.post("/{sensor_id}/faces/check")
+async def check_face(sensor_id: int, body: FaceBoxIn, request: Request) -> dict:
+    """Whether there is a face in a person box that can be taught: {face (normalised box), problem, message}."""
+    rt = runtime(request)
+    cfg = await asyncio.to_thread(_config, rt, sensor_id)
+    image, _origin, face, problem = await _find(rt, sensor_id, body, cfg)
+    box = None
+    if face is not None:
+        w, h = image.width, image.height
+        box = [
+            round(face.box[0] / w, 4),
+            round(face.box[1] / h, 4),
+            round(face.box[2] / w, 4),
+            round(face.box[3] / h, 4),
+        ]
+    return {"face": box, "problem": problem, "message": PROBLEMS.get(problem or "", "")}
+
+
+@router.post("/{sensor_id}/faces", status_code=201)
+async def teach_face(sensor_id: int, body: FaceTeachIn, request: Request) -> dict:
+    """Teach the face in a person box as one of the people, or as a new person; every sensor
+    that recognises faces knows it from then on.
+
+    Only a face that passes the quality gate is taught: a back or a jacket never becomes someone."""
+    rt = runtime(request)
+    cfg = await asyncio.to_thread(_config, rt, sensor_id)
+    if not body.person and not body.new_person:
+        raise HTTPException(400, "Say who it is: a person or a new person")
+    with rt.db.session() as s:
+        if body.person:
+            person = person_by_key(s, body.person)
+            count = s.scalar(select(func.count()).select_from(Face).where(Face.person_id == person.id)) or 0
+            if count >= FACES["max_faces_per_person"]:
+                raise HTTPException(400, f"A person can have at most {FACES['max_faces_per_person']} faces")
+        else:
+            # Checked now, made only once the face is good.
+            if (s.scalar(select(func.count()).select_from(Person)) or 0) >= FACES["max_people"]:
+                raise HTTPException(400, f"VisionState can know at most {FACES['max_people']} people")
+            _check_name(s, body.new_person or "")
+    image, origin, face, problem = await _find(rt, sensor_id, body, cfg)
+    if problem or face is None:
+        raise HTTPException(400, PROBLEMS.get(problem or "none", PROBLEMS["none"]))
+    crop, local = faces.face_crop(image, face)
+    filename = await asyncio.to_thread(rt.storage.save_face, crop)
+    with rt.db.session() as s:
+        person = person_by_key(s, body.person) if body.person else new_person(s, body.new_person or "")
+        face_id = add_face_row(s, person.id, sensor_id, filename, crop, faces.face_to_dict(local), origin)
+        key, name = person.key, person.name
+        n = s.scalar(select(func.count()).select_from(Face).where(Face.person_id == person.id)) or 0
+    if body.new_person:
+        await rt.people_changed()  # the person's entities on every sensor that recognises faces
+    else:
+        rt.faces_changed()
+    return {"id": face_id, "label": key, "name": name, "faces": n}
+
+
+def add_face_row(
+    s: Session, person_id: int, sensor_id: int | None, filename: str, crop: Image.Image, face: dict, origin: str
+) -> int:
+    row = Face(
+        person_id=person_id,
+        sensor_id=sensor_id,
+        filename=filename,
+        origin=origin,
+        is_night=imaging.is_night(crop),
+        face=face,
+    )
+    s.add(row)
+    s.flush()
+    return row.id
+
+
+# --- Shared: the people and their faces -----------------------------------------------------------
+
+
+@people_router.get("/people")
+async def people(request: Request) -> dict:
+    """Every person with their faces (and the camera each was taught on), where each was last
+    seen; look-alikes and faces that may have the wrong name (only while the face model is
+    loaded: they need its embeddings)."""
+    rt = runtime(request)
+
+    def read() -> tuple[list[tuple[str, str]], list[tuple], dict[int, str]]:
+        with rt.db.session() as s:
+            persons = [(p.key, p.name) for p in s.scalars(select(Person).order_by(Person.created_at))]
+            rows = s.execute(
+                select(Face.id, Person.key, Face.origin, Face.is_night, Face.created_at, Face.sensor_id)
+                .join(Person, Face.person_id == Person.id)
+                .order_by(Face.created_at.desc())
+            ).all()
+            names: dict[int, str] = {i: n for i, n in s.execute(select(Sensor.id, Sensor.name)).all()}
+        return persons, [tuple(r) for r in rows], names
+
+    persons, rows, sensor_names = await asyncio.to_thread(read)
+    suspects: dict[int, str] = {}
+    look_alikes: list[dict] = []
+    if rt.face_model is not None:
+        index = await rt.face_index_for(rt.face_model)
+        suspects, look_alikes = _quality(index, rt.face_limits(None))
+    oldest: dict[str, datetime] = {}
+    for _id, key, _origin, _night, created, _sensor in rows:
+        if key not in oldest or created < oldest[key]:
+            oldest[key] = created
+    return {
+        "model": rt.face_status(),
+        "sensors": await asyncio.to_thread(rt.face_sensor_ids),
+        "people": [
+            {
+                "key": key,
+                "name": name,
+                "seen": {**(seen := rt.person_seen(key)), "sensor_name": sensor_names.get(seen["sensor_id"] or 0)},
+                "oldest": iso(oldest.get(key)),
+                "faces": [
+                    {
+                        "id": fid,
+                        "origin": origin,
+                        "is_night": night,
+                        "created_at": iso(created),
+                        "sensor_id": sensor_id,
+                        "sensor_name": sensor_names.get(sensor_id) if sensor_id is not None else None,
+                        "suspect": suspects.get(fid),
+                    }
+                    for fid, person, origin, night, created, sensor_id in rows
+                    if person == key
+                ],
+            }
+            for key, name in persons
+        ],
+        "look_alikes": look_alikes,
+    }
+
+
+def _quality(index: faces.FaceIndex, limits: dict[str, float]) -> tuple[dict[int, str], list[dict]]:
+    """Faces closer to another person than to their own (id -> that person), and pairs of people
+    whose closest faces come near the match threshold (FACES["look_alike"])."""
+    suspects: dict[int, str] = {}
+    pairs: dict[tuple[str, str], float] = {}
+    if not index.ids:
+        return suspects, []
+    sims = index.vectors @ index.vectors.T
+    labels = np.array(index.labels)
+    for i, face_id in enumerate(index.ids):
+        own = labels == labels[i]
+        own[i] = False
+        per: dict[str, float] = {}
+        for label in set(index.labels):
+            mask = (labels == label) if label != labels[i] else own
+            values = np.sort(sims[i][mask])[::-1][: FACES["top_k"]]
+            if len(values):
+                per[label] = float(values.mean())
+        mine = per.get(str(labels[i]))
+        others = {k: v for k, v in per.items() if k != labels[i]}
+        if mine is not None and others:
+            closest, value = max(others.items(), key=lambda kv: kv[1])
+            if value > mine:
+                suspects[face_id] = closest
+        for label in others:
+            a, b = sorted((str(labels[i]), label))
+            pairs[(a, b)] = max(pairs.get((a, b), -1.0), float(sims[i][labels == label].max()))
+    limit = limits["match_similarity"] * FACES["look_alike"]
+    look_alikes = [{"a": a, "b": b, "similarity": round(v, 3)} for (a, b), v in pairs.items() if v >= limit]
+    return suspects, look_alikes
+
+
+@people_router.patch("/people/{key}")
+async def rename_person(key: str, body: PersonIn, request: Request) -> dict:
+    """A person's name (their entities' names follow; their key and entity IDs stay)."""
+    rt = runtime(request)
+    with rt.db.session() as s:
+        person = person_by_key(s, key)
+        person.name = _check_name(s, body.name, keep=person.id)
+        result = {"key": person.key, "name": person.name}
+    await rt.people_changed()
+    return result
+
+
+@people_router.delete("/people/{key}", status_code=204)
+async def forget_person(key: str, request: Request) -> Response:
+    """Forget a person: their faces, embeddings and entities on every sensor."""
+    rt = runtime(request)
+    await asyncio.to_thread(_forget_people, rt, key)
+    await rt.people_changed()
+    return Response(status_code=204)
+
+
+@people_router.delete("/people", status_code=204)
+async def forget_everyone(request: Request) -> Response:
+    """Forget every person and every face."""
+    rt = runtime(request)
+    await asyncio.to_thread(_forget_people, rt, None)
+    await rt.people_changed()
+    return Response(status_code=204)
+
+
+def _forget_people(rt: Runtime, key: str | None) -> None:
+    with rt.db.session() as s:
+        persons = [person_by_key(s, key)] if key is not None else list(s.scalars(select(Person)))
+        for person in persons:
+            for face in s.scalars(select(Face).where(Face.person_id == person.id)):
+                rt.storage.delete_face(face.filename)
+            s.delete(person)  # their faces and embeddings go with them (foreign keys)
+
+
+@people_router.get("/faces/{face_id}/image")
+def face_image(face_id: int, request: Request) -> FileResponse:
+    rt = runtime(request)
+    with rt.db.session() as s:
+        face = s.get(Face, face_id)
+        if face is None:
+            raise HTTPException(404, "Face not found")
+        path = rt.storage.face_path(face.filename)
+    if not path.exists():
+        raise HTTPException(404, "Image file missing")
+    return FileResponse(path, media_type="image/jpeg", headers={"Cache-Control": "private, max-age=3600"})
+
+
+@people_router.patch("/faces/{face_id}")
+def move_face(face_id: int, body: FaceMoveIn, request: Request) -> dict:
+    """Give a taught face another person's name (it was taught under the wrong one)."""
+    rt = runtime(request)
+    with rt.db.session() as s:
+        face = s.get(Face, face_id)
+        if face is None:
+            raise HTTPException(404, "Face not found")
+        face.person_id = person_by_key(s, body.person).id
+    rt.faces_changed()
+    return {"id": face_id, "label": body.person}
+
+
+@people_router.delete("/faces/{face_id}", status_code=204)
+def forget_face(face_id: int, request: Request) -> Response:
+    rt = runtime(request)
+    with rt.db.session() as s:
+        face = s.get(Face, face_id)
+        if face is None:
+            raise HTTPException(404, "Face not found")
+        rt.storage.delete_face(face.filename)
+        s.delete(face)  # its cached embeddings go with it (foreign key)
+    rt.faces_changed()
+    return Response(status_code=204)
diff --git a/visionstate/backend/visionstate/api/history.py b/visionstate/backend/visionstate/api/history.py
index 7864bc9..fecf717 100644
--- a/visionstate/backend/visionstate/api/history.py
+++ b/visionstate/backend/visionstate/api/history.py
@@ -30,6 +30,7 @@
     "cleared": Prediction.published_key == "off",
     "filtered": Prediction.published_key == "filtered",
     "asked": Prediction.published_key == "ask",
+    "face_seen": Prediction.published_key == "face",
     "value": Prediction.published_key.is_not(None),
     "rejected": Prediction.published_key.is_(None),
     "verified": Prediction.read_ok.is_not(None),
diff --git a/visionstate/backend/visionstate/api/imports.py b/visionstate/backend/visionstate/api/imports.py
index 8a3cd92..95ba043 100644
--- a/visionstate/backend/visionstate/api/imports.py
+++ b/visionstate/backend/visionstate/api/imports.py
@@ -9,9 +9,10 @@
 from pathlib import Path
 
 from fastapi import APIRouter, File, HTTPException, Request, UploadFile
+from sqlalchemy import select
 
-from .. import bundle, detectors, imaging
-from ..db import keep_text_reader
+from .. import bundle, detectors, faces, imaging
+from ..db import Person, keep_text_reader
 from ..engine import Runtime
 from ..redact import redact
 from ..settings import (
@@ -23,9 +24,11 @@
 from .common import (
     API_PREFIX,
     runtime,
+    slugify,
     unique_name,
     unique_slug,
 )
+from .faces import add_face_row, new_person, taken_keys
 from .samples import copy_limited
 
 log = logging.getLogger(__name__)
@@ -49,6 +52,7 @@ async def import_bundle(request: Request, file: UploadFile = File(...)) -> dict:
     finally:
         path.unlink(missing_ok=True)
     await rt.sensor_created(sensor_id)
+    await rt.people_changed()  # people that came with their faces
     cfg = await asyncio.to_thread(rt.load_sensor, sensor_id)
     if cfg and cfg.kind == KIND_STATES:
         rt.schedule_retrain(sensor_id, delay=0)
@@ -93,6 +97,10 @@ def _import_sync(rt: Runtime, path: Path) -> tuple[int, int]:
         raise ValueError(f"invalid sensor in bundle: {err.errors()[0].get('msg')}") from err
     except HTTPException as err:
         raise ValueError(f"invalid sensor in bundle: {err.detail}") from err
+    if spec.objects is not None and spec.objects.faces is not None:
+        # Taught faces come along, face recognition does not: it is turned on there, with its
+        # own confirmation (and download).
+        spec.objects.faces.enabled = False
     samples = manifest.get("samples", []) if spec.kind in (KIND_STATES, KIND_OBJECTS) else []
     if not isinstance(samples, list) or len(samples) > UPLOAD_LIMITS["max_zip_members"]:
         raise ValueError("too many samples in bundle")
@@ -121,9 +129,33 @@ def _import_sync(rt: Runtime, path: Path) -> tuple[int, int]:
         except (ValueError, KeyError, TypeError, AttributeError, OSError) as err:
             skipped += 1
             log.warning("Import: skipping a sample: %s", err)
+    stored = manifest.get("faces", []) if spec.kind == KIND_OBJECTS else []
+    for item in stored if isinstance(stored, list) else []:
+        try:
+            _import_face(rt, path, sensor_id, item)
+        except (ValueError, KeyError, TypeError, AttributeError, OSError, HTTPException) as err:
+            skipped += 1
+            log.warning("Import: skipping a face: %s", getattr(err, "detail", err))
     return sensor_id, skipped
 
 
+def _import_face(rt: Runtime, path: Path, sensor_id: int, item: dict) -> None:
+    """A taught face from a bundle, for its person: the one with that key (or name), else a new one."""
+    face = faces.face_to_dict(faces.face_from_dict(item["face"]))
+    image = imaging.decode(bundle.read_sample_bytes(path, str(item["file"]), "faces"))
+    key, name = str(item["person"]), str(item.get("name") or item["person"]).strip()[:64]
+    with rt.db.session() as s:
+        person = s.scalar(select(Person).where(Person.key == key))
+        if person is None:
+            person = next((p for p in s.scalars(select(Person)) if p.name.lower() == name.lower()), None)
+        if person is None:
+            person = new_person(s, name)
+            if key not in taken_keys(s) and slugify(key, "") == key:
+                person.key = key  # the same entity IDs as where it was exported
+        filename = rt.storage.save_face(image)
+        add_face_row(s, person.id, sensor_id, filename, image, face, "import")
+
+
 def _import_sample(rt: Runtime, path: Path, sensor_id: int, kind: str, item: dict, own: set, state_ids: dict) -> None:
     if kind == KIND_OBJECTS:
         if not teach.importable_label(item.get("object_label"), own):
diff --git a/visionstate/backend/visionstate/api/review.py b/visionstate/backend/visionstate/api/review.py
index e22f538..e775e55 100644
--- a/visionstate/backend/visionstate/api/review.py
+++ b/visionstate/backend/visionstate/api/review.py
@@ -11,15 +11,16 @@
 from sqlalchemy import CursorResult, delete, func, select, update
 
 from .. import imaging, readers
-from ..db import Prediction, Sample, SampleLabel, Sensor
-from ..engine import Runtime
-from ..settings import KIND_OBJECTS, KIND_READING, merge_objects, merge_reading
+from ..db import Face, Prediction, Sample, SampleLabel, Sensor
+from ..engine import Runtime, with_people
+from ..settings import FACE_UNKNOWN_KEY, KIND_OBJECTS, KIND_READING, merge_objects, merge_reading
 from .common import (
     API_PREFIX,
     get_sensor,
     runtime,
     state_id_for,
 )
+from .faces import FaceTeachIn, teach_face
 from .history import prediction_view
 from .teach import TeachIn, teach_box
 
@@ -29,10 +30,12 @@
 
 class ReviewIn(BaseModel):
     # State sensors: confirm | label (state_key) | skip. Reading sensors: read_ok | misread
-    # (optionally the value that was right) | skip. Object sensors ("Is this Our car?"): yes | no | skip.
+    # (optionally the value that was right) | skip. Object sensors ("Is this Our car?"): yes | no | skip;
+    # about a face ("Is this Kari?", "Who is this?") also person (state_key: who it is) and new
+    # (value: the new person's name).
     action: str
     state_key: str | None = None
-    value: str | None = Field(None, max_length=32)
+    value: str | None = Field(None, max_length=64)
 
 
 @router.get("/review")
@@ -65,8 +68,10 @@ def review_queue(request: Request, limit: int = 50) -> dict:
                         "roi": sensor.roi,
                         "states": [{"key": st.key, "name": st.name, "color": st.color} for st in sensor.states],
                         "reading": merge_reading(sensor.reading) if sensor.kind == KIND_READING else None,
-                        # object sensors: the own labels a question can be about
-                        "labels": merge_objects(sensor.objects)["custom"] if sensor.kind == KIND_OBJECTS else [],
+                        # object sensors: the own labels (and people) a question can be about
+                        "labels": with_people(merge_objects(sensor.objects), s)["custom"]
+                        if sensor.kind == KIND_OBJECTS
+                        else [],
                     },
                 }
             )
@@ -106,7 +111,7 @@ async def review_answer(prediction_id: int, body: ReviewIn, request: Request) ->
         sensor = get_sensor(s, row.sensor_id)
         kind = sensor.kind
     if kind == KIND_OBJECTS:
-        return await _answer_object(rt, request, prediction_id, body.action)
+        return await _answer_object(rt, request, prediction_id, body)
     with rt.db.session() as s:
         row = s.get(Prediction, prediction_id)
         if row is None:
@@ -166,11 +171,19 @@ async def review_answer(prediction_id: int, body: ReviewIn, request: Request) ->
     return {"ok": True}
 
 
-async def _answer_object(rt: Runtime, request: Request, prediction_id: int, action: str) -> dict:
+async def _answer_object(rt: Runtime, request: Request, prediction_id: int, body: ReviewIn) -> dict:
     """ "Is this Our car?" — yes teaches the box as the own label, no as its plain class (both in
     the light of that frame, so later boxes like it are told apart); skip only takes it out of the
-    queue. Answering again replaces the box the first answer taught."""
-    if action not in ("yes", "no", "skip"):
+    queue. Answering again replaces the box the first answer taught.
+
+    About a face: yes teaches the face as that person, person as the person named in
+    ``state_key``, new as a new person called ``value``; no and skip teach nothing (a face is
+    never taught as "not someone")."""
+    action = body.action
+    with rt.db.session() as s:
+        row = s.get(Prediction, prediction_id)
+        is_face = bool(row is not None and ((row.probs or {}).get("ask") or {}).get("face"))
+    if action not in (("yes", "no", "skip", "person", "new") if is_face else ("yes", "no", "skip")):
         raise HTTPException(400, "Answer yes, no or skip")
     with rt.db.session() as s:
         row = s.get(Prediction, prediction_id)
@@ -182,19 +195,49 @@ async def _answer_object(rt: Runtime, request: Request, prediction_id: int, acti
         sensor_id, score, taught = row.sensor_id, row.confidence, row.sample_id
         row.reviewed = True
         row.sample_id = None
-        if taught is not None and (sample := s.get(Sample, taught)) is not None:
+        if taught is not None and is_face and (face := s.get(Face, taught)) is not None:
+            rt.storage.delete_face(face.filename)  # the face the first answer taught
+            s.delete(face)
+        elif taught is not None and not is_face and (sample := s.get(Sample, taught)) is not None:
             rt.storage.delete_sample(sensor_id, sample.id, sample.filename)
             s.delete(sample)
     if taught is not None:
-        rt.taught_changed(sensor_id)
-    if action != "skip":
+        if is_face:
+            rt.faces_changed()
+        else:
+            rt.taught_changed(sensor_id)
+    if is_face:
+        await _answer_face(rt, request, prediction_id, sensor_id, question, score, body)
+    elif action != "skip":
         label = question["label"] if action == "yes" else question["detected"]
-        body = TeachIn(
+        taught_box = TeachIn(
             history_id=prediction_id, box=question["box"], label=label, detected=question["detected"], score=score
         )
-        result = await teach_box(sensor_id, body, request)
+        result = await teach_box(sensor_id, taught_box, request)
         with rt.db.session() as s:
             if (answered := s.get(Prediction, prediction_id)) is not None:
                 answered.sample_id = result["id"]
     await rt.publish_review_count()
     return {"ok": True}
+
+
+async def _answer_face(
+    rt: Runtime, request: Request, prediction_id: int, sensor_id: int, question: dict, score: float, body: ReviewIn
+) -> None:
+    """Teach the face of a question when the answer says who it is (see _answer_object)."""
+    person, new_person = None, None
+    if body.action == "yes" and question["label"] != FACE_UNKNOWN_KEY:
+        person = question["label"]
+    elif body.action == "person":
+        person = body.state_key
+    elif body.action == "new":
+        new_person = (body.value or "").strip() or None
+        if new_person is None:
+            raise HTTPException(400, "Give the new person a name")
+    if person is None and new_person is None:
+        return
+    face = FaceTeachIn(history_id=prediction_id, box=question["box"], score=score, person=person, new_person=new_person)
+    result = await teach_face(sensor_id, face, request)
+    with rt.db.session() as s:
+        if (answered := s.get(Prediction, prediction_id)) is not None:
+            answered.sample_id = result["id"]
diff --git a/visionstate/backend/visionstate/api/sensors.py b/visionstate/backend/visionstate/api/sensors.py
index 11988a0..6bed45b 100644
--- a/visionstate/backend/visionstate/api/sensors.py
+++ b/visionstate/backend/visionstate/api/sensors.py
@@ -14,7 +14,7 @@
 from sqlalchemy import func, select
 from sqlalchemy.orm import Session
 
-from .. import bundle, readers
+from .. import bundle, faces, readers
 from ..db import ModelInfo, Prediction, ReadingStat, Sample, Sensor, State
 from ..redact import redact
 from ..settings import (
@@ -42,6 +42,7 @@
     Roi,
     StateIn,
     Triggers,
+    check_faces,
     check_teach,
     get_sensor,
     runtime,
@@ -227,7 +228,9 @@ async def update_sensor(sensor_id: int, body: SensorPatch, request: Request) ->
         if body.objects is not None:
             # Merged, so own labels (made by teaching) and fields an older UI does not send stay.
             sensor.objects = {**(sensor.objects or {}), **body.objects.stored(exclude_unset=True)}
-            check_teach(merge_teach(rt.backbone_id, rt.global_teach, merge_objects(sensor.objects)["teach"]))
+            merged = merge_objects(sensor.objects)
+            check_teach(merge_teach(rt.backbone_id, rt.global_teach, merged["teach"]))
+            check_faces(faces.merge_limits(rt.face_choice, rt.global_faces, merged["faces"]))
         if body.reading is not None:
             sensor.reading = body.reading.model_dump()
         s.flush()
@@ -517,13 +520,14 @@ async def reading_export(
 
 
 @router.get("/{sensor_id}/export")
-async def export(sensor_id: int, request: Request, background: BackgroundTasks) -> FileResponse:
+async def export(sensor_id: int, request: Request, background: BackgroundTasks, faces: bool = False) -> FileResponse:
+    """A sensor as a bundle to import elsewhere; ``faces``: with the faces taught on it."""
     rt = runtime(request)
     fd, name = tempfile.mkstemp(suffix=".zip")
     os.close(fd)
     tmp = Path(name)
     try:
-        filename = await asyncio.to_thread(bundle.export_sensor, rt.db, rt.storage, sensor_id, tmp)
+        filename = await asyncio.to_thread(bundle.export_sensor, rt.db, rt.storage, sensor_id, tmp, faces)
     except KeyError as err:
         tmp.unlink(missing_ok=True)
         raise HTTPException(404, "Sensor not found") from err
diff --git a/visionstate/backend/visionstate/api/system.py b/visionstate/backend/visionstate/api/system.py
index 5fb7866..c297e69 100644
--- a/visionstate/backend/visionstate/api/system.py
+++ b/visionstate/backend/visionstate/api/system.py
@@ -13,11 +13,16 @@
 from pydantic import BaseModel, Field
 from sqlalchemy import func, select
 
-from .. import backbones, detectors, readers
-from ..db import Prediction, Sensor
+from .. import backbones, detectors, faces, readers
+from ..db import Person, Prediction, Sensor
 from ..redact import redact
 from ..settings import (
     COUNTER_READERS,
+    FACE_DEFAULTS,
+    FACE_LIMITS,
+    FACE_TUNABLE,
+    FACE_UNKNOWN_KEY,
+    FACES,
     HISTORY,
     HISTORY_EVENTS,
     LIGHT_DOMAINS,
@@ -64,8 +69,10 @@
 from ..sources import SOURCE_TYPES, SourceError
 from .common import (
     API_PREFIX,
+    FaceLimits,
     ReviewRules,
     TeachLimits,
+    check_faces,
     check_teach,
     iso,
     runtime,
@@ -112,6 +119,10 @@ def ui_config() -> dict:
             "tunable": list(TEACH_TUNABLE),
             "limits": TEACH_TUNABLE_LIMITS,
         },
+        "face_defaults": {k: FACE_DEFAULTS[k] for k in ("enabled", "ask_unknown")},  # the limits: face-limits
+        "face_limits": FACE_LIMITS,
+        "face_unknown_key": FACE_UNKNOWN_KEY,
+        "faces": {k: FACES[k] for k in ("max_people", "max_faces_per_person", "suggest_faces", "stats_window")},
         "reading_sensor_defaults": READING_SENSOR_DEFAULTS,
         "reading_defaults": READING_DEFAULTS,
         "reading_limits": READING_LIMITS,
@@ -138,9 +149,11 @@ def counts():
             return (
                 s.scalar(select(func.count()).select_from(Sensor)) or 0,
                 s.scalar(select(func.count()).select_from(Prediction).where(Prediction.reviewed.is_(False))) or 0,
+                s.scalar(select(func.count()).select_from(Person)) or 0,
             )
 
-    sensors, review = await asyncio.to_thread(counts)
+    sensors, review, people = await asyncio.to_thread(counts)
+    face_sensors = await asyncio.to_thread(rt.face_sensor_ids)
     mqtt = rt.mqtt
     return {
         "version": VERSION,
@@ -158,6 +171,8 @@ def counts():
         "wheel_reader": rt.wheel_reader.spec.id if rt.wheel_reader else None,
         "wheel_reader_name": rt.wheel_reader.spec.name if rt.wheel_reader else None,
         "wheel_reader_error": rt.wheel_reader_error,
+        # The face model, the people and how many sensors recognise them (the People page).
+        "faces": {**rt.face_status(), "people": people, "sensors": len(face_sensors)},
         "mqtt": {
             "connected": mqtt.connected,
             "host": mqtt.config.host if mqtt.config else None,
@@ -178,19 +193,46 @@ class SettingsIn(BaseModel):
     backbone: str
     detector: str | None = None  # object sensors; None keeps the current one
     reader: str | None = None  # reading sensors; None keeps the current one
+    face_model: str | None = None  # face recognition on object sensors; None keeps the current one
+
+
+# The models a user chooses, per kind (the keys of their lists in GET /settings).
+MODELS = {
+    "backbones": backbones.BACKBONES,
+    "detectors": detectors.DETECTORS,
+    "readers": readers.READERS,
+    "face_models": faces.RECOGNISERS,
+}
+
+
+def _in_use(rt, kind: str, model_id: str, face_sensors: bool) -> str:
+    """Why a model cannot be deleted now ("" if it can)."""
+    if kind == "backbones" and model_id == rt.backbone_id:
+        return "It is the state model in use. Choose another one first."
+    if kind == "detectors" and model_id == rt.db.get_text("detector", detectors.DEFAULT_DETECTOR):
+        return "It is the object model in use. Choose another one first."
+    if kind == "readers" and model_id == rt.db.get_text("reader", readers.DEFAULT_READER):
+        return "It is the display reader in use. Choose another one first."
+    if kind == "face_models" and model_id == rt.face_choice and face_sensors:
+        return "A sensor recognises faces with it. Turn that off there first, or choose another face model."
+    return ""
 
 
 @router.get("/settings")
 def get_settings(request: Request) -> dict:
-    """The chosen AI models and every model that can be chosen."""
+    """The chosen AI models and every model that can be chosen, with whether each was downloaded
+    and can be deleted again (``in_use`` says why not)."""
     rt = runtime(request)
+    face_sensors = bool(rt.face_sensor_ids())
 
-    def model(spec) -> dict:
+    def model(spec, kind: str = "") -> dict:
         return {
             "id": spec.id,
             "name": spec.name,
             "description": spec.description,
             "installed": rt.model_path(spec) is not None,
+            "downloaded": rt.downloaded(spec),
+            "in_use": _in_use(rt, kind, spec.id, face_sensors),
             "size": spec.size,
             "license": spec.license,
             "source": spec.source,
@@ -199,14 +241,32 @@ def model(spec) -> dict:
 
     return {
         "backbone": rt.embedder.spec.id if rt.embedder else backbones.DEFAULT_BACKBONE,
-        "backbones": [model(spec) for spec in backbones.BACKBONES.values()],
+        "backbones": [model(spec, "backbones") for spec in backbones.BACKBONES.values()],
         "detector": rt.db.get_text("detector", detectors.DEFAULT_DETECTOR),
-        "detectors": [model(spec) for spec in detectors.DETECTORS.values()],
+        "detectors": [model(spec, "detectors") for spec in detectors.DETECTORS.values()],
         "reader": rt.db.get_text("reader", readers.DEFAULT_READER),
-        "readers": [model(spec) for spec in readers.READERS.values()],
+        "readers": [model(spec, "readers") for spec in readers.READERS.values()],
         # Mechanical counters set to the wheel reader; one model for now, so nothing to choose.
         "wheel_reader": readers.DEFAULT_WHEEL_READER,
         "wheel_readers": [model(spec) for spec in readers.WHEEL_READERS.values()],
+        # Faces: the recogniser to choose (one face detector goes with every one); downloaded when
+        # a sensor turns face recognition on. "size" and "installed" count the detector too.
+        "face_model": rt.face_choice,
+        "face_models": [
+            {
+                **model(spec, "face_models"),
+                "size": spec.size + (0 if rt.model_path(faces.DETECTOR) else faces.DETECTOR.size),
+                "installed": rt.face_files_present(spec.id),
+                "data": spec.data,
+                "detector": {
+                    "name": faces.DETECTOR.name,
+                    "license": faces.DETECTOR.license,
+                    "data": faces.DETECTOR.data,
+                    "source": faces.DETECTOR.source,
+                },
+            }
+            for spec in faces.RECOGNISERS.values()
+        ],
         "options": {
             "discovery_prefix": rt.settings.discovery_prefix,
             "mqtt_host": rt.settings.mqtt_host or "(from Home Assistant)",
@@ -224,6 +284,8 @@ async def put_settings(body: SettingsIn, request: Request) -> dict:
         raise HTTPException(400, "Unknown detector")
     if body.reader is not None and body.reader not in readers.READERS:
         raise HTTPException(400, "Unknown reader")
+    if body.face_model is not None and body.face_model not in faces.RECOGNISERS:
+        raise HTTPException(400, "Unknown face model")
     current = get_settings(request)
     # Only reload what changed: a new backbone retrains every state sensor.
     if body.backbone != current["backbone"]:
@@ -241,9 +303,41 @@ async def put_settings(body: SettingsIn, request: Request) -> dict:
             await rt.set_reader(body.reader)
         except Exception as err:  # noqa: BLE001
             raise HTTPException(500, f"Could not load reader: {redact(str(err))}") from err
+    if body.face_model is not None and body.face_model != current["face_model"]:
+        await rt.set_face_model(body.face_model)  # loads in the background when a sensor uses faces
     return get_settings(request)
 
 
+@router.post("/faces/retry")
+async def faces_retry(request: Request) -> dict:
+    """Try to download and load the face model again after it failed."""
+    rt = runtime(request)
+    rt.retry_faces()
+    return rt.face_status()
+
+
+@router.delete("/models/{kind}/{model_id}")
+async def delete_model(kind: str, model_id: str, request: Request) -> dict:
+    """Delete a downloaded AI model that is not in use (it is downloaded again when chosen)."""
+    rt = runtime(request)
+    spec = MODELS.get(kind, {}).get(model_id)
+    if spec is None:
+        raise HTTPException(404, "Unknown model")
+    face_sensors = bool(await asyncio.to_thread(rt.face_sensor_ids))
+    reason = await asyncio.to_thread(_in_use, rt, kind, model_id, face_sensors)
+    if reason:
+        raise HTTPException(409, reason)
+    if not rt.downloaded(spec):
+        raise HTTPException(404, "This model is included in the app or not downloaded")
+    if kind == "face_models":
+        if rt.face_model is not None:
+            await rt.faces_settled()  # let the unused face model go before its file
+        await asyncio.to_thread(rt.delete_face_files, model_id)
+    else:
+        await asyncio.to_thread((rt.settings.models_dir / spec.filename).unlink, missing_ok=True)
+    return await asyncio.to_thread(get_settings, request)
+
+
 # --- storage ----------------------------------------------------------------------
 
 
@@ -313,6 +407,33 @@ async def put_teach_limits(body: TeachLimits, request: Request) -> dict:
     return _teach_view(rt)
 
 
+# --- face limits --------------------------------------------------------------
+
+
+def _faces_view(rt) -> dict:
+    """The global face limits: the face model's defaults, what the user set (None: the default)
+    and the result, which each object sensor inherits unless it has its own value."""
+    return {
+        "model": rt.face_choice,
+        "defaults": faces.tunable_defaults(rt.face_choice),
+        "values": {key: rt.global_faces.get(key) for key in FACE_TUNABLE},
+        "effective": faces.merge_limits(rt.face_choice, rt.global_faces, None),
+    }
+
+
+@router.get("/face-limits")
+def get_face_limits(request: Request) -> dict:
+    return _faces_view(runtime(request))
+
+
+@router.put("/face-limits")
+async def put_face_limits(body: FaceLimits, request: Request) -> dict:
+    rt = runtime(request)
+    check_faces(faces.merge_limits(rt.face_choice, body.stored(), None))
+    await asyncio.to_thread(rt.set_global_faces, body.stored())
+    return _faces_view(rt)
+
+
 # --- review reminder ----------------------------------------------------------
 
 _mlo = {k: v[0] for k, v in REMINDER_LIMITS.items()}
diff --git a/visionstate/backend/visionstate/api/teach.py b/visionstate/backend/visionstate/api/teach.py
index d9b5586..eed12f2 100644
--- a/visionstate/backend/visionstate/api/teach.py
+++ b/visionstate/backend/visionstate/api/teach.py
@@ -11,13 +11,14 @@
 from sqlalchemy.orm import Session
 
 from .. import detectors, imaging
-from ..db import Prediction, Sample
+from ..db import Person, Prediction, Sample
 from ..engine import Runtime
-from ..settings import KIND_OBJECTS, NONE_LABEL, TEACH, active_custom, merge_objects
+from ..settings import FACE_UNKNOWN_KEY, KIND_OBJECTS, NONE_LABEL, TEACH, active_custom, merge_objects
 from .common import API_PREFIX, get_sensor, iso, runtime, slugify
 from .sensors import prediction_view
 
 router = APIRouter(prefix=f"{API_PREFIX}/sensors", tags=["teach"])
+RESERVED_KEYS = {NONE_LABEL, FACE_UNKNOWN_KEY}  # never the key of an own label
 
 
 class TeachIn(BaseModel):
@@ -41,8 +42,9 @@ def _valid_box(cls, value: list[float]) -> list[float]:
         return [x1, y1, x2, y2]
 
 
-def _own_label(objects: dict, name: str, parent: str) -> dict:
-    """A new own label: its key never clashes with an object VisionState knows or another own label."""
+def own_label(objects: dict, name: str, parent: str, people: set[str]) -> dict:
+    """A new own label: its key never clashes with an object VisionState knows, another own label
+    or a person (``people``: their keys; they are entities of the sensor too)."""
     if parent not in objects["classes"]:
         raise HTTPException(400, "An own label belongs to one of the sensor's objects")
     if len(objects["custom"]) >= TEACH["max_labels"]:
@@ -50,7 +52,7 @@ def _own_label(objects: dict, name: str, parent: str) -> dict:
     name = name.strip()
     if any(label["name"].lower() == name.lower() for label in objects["custom"]):
         raise HTTPException(400, f"There is already a label called {name}")
-    taken = set(detectors.LABELS.by_key) | {label["key"] for label in objects["custom"]} | {NONE_LABEL}
+    taken = set(detectors.LABELS.by_key) | {label["key"] for label in objects["custom"]} | RESERVED_KEYS | people
     base = slugify(name, "label")
     key, n = base, 2
     while key in taken:
@@ -96,19 +98,21 @@ def taught(sensor_id: int, request: Request) -> dict:
         }
 
 
-async def _frame_image(rt: Runtime, sensor_id: int, body: TeachIn) -> tuple[Image.Image, str]:
-    """The frame the box was drawn on, and where it came from ("live" or "history")."""
-    if body.history_id is not None:
+async def frame_image(
+    rt: Runtime, sensor_id: int, frame_id: str | None, history_id: int | None
+) -> tuple[Image.Image, str]:
+    """The frame a box was drawn on, and where it came from ("live" or "history")."""
+    if history_id is not None:
         with rt.db.session() as s:
-            row = s.get(Prediction, body.history_id)
+            row = s.get(Prediction, history_id)
             if row is None or row.sensor_id != sensor_id or not row.frame:
                 raise HTTPException(404, "History frame not found")
             path = rt.storage.history_path(sensor_id, row.frame)
         if not path.exists():
             raise HTTPException(404, "History frame file missing")
         return await asyncio.to_thread(imaging.load, path), "history"
-    if body.frame_id:
-        data = rt.live_state(sensor_id).analysed_frame(body.frame_id)
+    if frame_id:
+        data = rt.live_state(sensor_id).analysed_frame(frame_id)
         if data is None:
             raise HTTPException(409, "That frame is no longer kept. Tap the box again on the newest frame.")
         return await asyncio.to_thread(imaging.decode, data), "live"
@@ -137,7 +141,9 @@ async def teach_box(sensor_id: int, body: TeachIn, request: Request) -> dict:
             raise HTTPException(400, f"Unknown object {body.detected!r}")
         new = None
         if body.new_label:
-            new = _own_label(objects, body.new_label, body.parent or body.detected or "")
+            new = own_label(
+                objects, body.new_label, body.parent or body.detected or "", set(s.scalars(select(Person.key)))
+            )
             label = new["key"]
         else:
             active = {c["key"] for c in active_custom(objects)}
@@ -150,7 +156,7 @@ async def teach_box(sensor_id: int, body: TeachIn, request: Request) -> dict:
     x1, _, x2, y2 = body.box
     if not imaging.in_region((x1 + x2) / 2, y2, roi):
         raise HTTPException(400, "That box is outside the sensor's region, so it never counts")
-    image, origin = await _frame_image(rt, sensor_id, body)
+    image, origin = await frame_image(rt, sensor_id, body.frame_id, body.history_id)
     if new is not None:
         with rt.db.session() as s:
             row = get_sensor(s, sensor_id)
@@ -172,7 +178,7 @@ async def teach_box(sensor_id: int, body: TeachIn, request: Request) -> dict:
     return {"id": example_id, "label": label, "seen": seen}
 
 
-def _delete_examples(rt: Runtime, s: Session, sensor_id: int, where: list[ColumnElement[bool]]) -> int:
+def delete_examples(rt: Runtime, s: Session, sensor_id: int, where: list[ColumnElement[bool]]) -> int:
     """Delete the taught boxes of a sensor that match ``where`` (all with an empty list); returns how many."""
     rows = s.scalars(
         select(Sample).where(Sample.sensor_id == sensor_id, Sample.object_label.is_not(None), *where)
@@ -188,13 +194,13 @@ def forget_box(sensor_id: int, example_id: int, request: Request) -> Response:
     rt = runtime(request)
     with rt.db.session() as s:
         get_sensor(s, sensor_id, KIND_OBJECTS)
-        if not _delete_examples(rt, s, sensor_id, [Sample.id == example_id]):
+        if not delete_examples(rt, s, sensor_id, [Sample.id == example_id]):
             raise HTTPException(404, "Taught box not found")
     rt.taught_changed(sensor_id)
     return Response(status_code=204)
 
 
-async def _remove_labels(rt: Runtime, sensor_id: int, keys: set[str] | None) -> None:
+async def remove_labels(rt: Runtime, sensor_id: int, keys: set[str] | None) -> None:
     """Remove own labels (``None``: all of them, and every taught box) with their entities."""
     with rt.db.session() as s:
         sensor = get_sensor(s, sensor_id, KIND_OBJECTS)
@@ -202,7 +208,7 @@ async def _remove_labels(rt: Runtime, sensor_id: int, keys: set[str] | None) ->
         gone = [c["key"] for c in objects["custom"] if keys is None or c["key"] in keys]
         if keys is not None and not gone:
             raise HTTPException(404, "Label not found")
-        _delete_examples(rt, s, sensor_id, [] if keys is None else [Sample.object_label.in_(gone)])
+        delete_examples(rt, s, sensor_id, [] if keys is None else [Sample.object_label.in_(gone)])
         sensor.objects = {**(sensor.objects or {}), "custom": [c for c in objects["custom"] if c["key"] not in gone]}
         slug = sensor.slug
     for key in gone:
@@ -214,31 +220,32 @@ async def _remove_labels(rt: Runtime, sensor_id: int, keys: set[str] | None) ->
 @router.delete("/{sensor_id}/taught", status_code=204)
 async def forget_all(sensor_id: int, request: Request) -> Response:
     """Forget everything an object sensor was taught: every box and every own label."""
-    await _remove_labels(runtime(request), sensor_id, None)
+    await remove_labels(runtime(request), sensor_id, None)
     return Response(status_code=204)
 
 
 @router.delete("/{sensor_id}/labels/{key}", status_code=204)
 async def remove_label(sensor_id: int, key: str, request: Request) -> Response:
     """Remove an own label, the boxes taught with it and its entities in Home Assistant."""
-    await _remove_labels(runtime(request), sensor_id, {key})
+    await remove_labels(runtime(request), sensor_id, {key})
     return Response(status_code=204)
 
 
 def import_custom(stored: object) -> list[dict]:
-    """Own labels from an exported sensor, checked like new ones."""
+    """Own labels from an exported sensor, checked like new ones (people come with their faces,
+    api/imports.py)."""
     labels: list[dict] = []
     for item in stored if isinstance(stored, list) else []:
         try:
             key, name, parent = str(item["key"]), str(item["name"]).strip(), str(item["parent"])
         except KeyError, TypeError:
             continue
-        taken = set(detectors.LABELS.by_key) | {label["key"] for label in labels} | {NONE_LABEL}
-        if slugify(key, "") != key or key in taken or not name or parent not in detectors.LABELS.by_key:
+        taken = set(detectors.LABELS.by_key) | {label["key"] for label in labels} | RESERVED_KEYS
+        if item.get("face") or slugify(key, "") != key or key in taken or not name:
+            continue
+        if parent not in detectors.LABELS.by_key or len(labels) >= TEACH["max_labels"]:
             continue
         labels.append({"key": key, "name": name[:64], "parent": parent})
-        if len(labels) >= TEACH["max_labels"]:
-            break
     return labels
 
 
diff --git a/visionstate/backend/visionstate/backbones.py b/visionstate/backend/visionstate/backbones.py
index f018bbe..23ce9a5 100644
--- a/visionstate/backend/visionstate/backbones.py
+++ b/visionstate/backend/visionstate/backbones.py
@@ -16,6 +16,7 @@
 import sys
 import threading
 import urllib.request
+from collections.abc import Callable
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Protocol
@@ -122,16 +123,22 @@ def sha256_file(path: Path) -> str:
     return digest.hexdigest()
 
 
-def download(spec: ModelFile, dest_dir: Path) -> Path:
+def download(spec: ModelFile, dest_dir: Path, progress: Callable[[int], None] | None = None) -> Path:
+    """Fetch a model into ``dest_dir`` (unless it is there already) and check its SHA-256;
+    ``progress`` is told the bytes fetched so far."""
     dest_dir.mkdir(parents=True, exist_ok=True)
     target = dest_dir / spec.filename
     if target.exists() and sha256_file(target) == spec.sha256:
         return target
     tmp = target.with_suffix(".part")
     log.info("Downloading model %s from %s", spec.id, spec.url)
+    done = 0
     with urllib.request.urlopen(spec.url, timeout=60) as resp, tmp.open("wb") as out:  # noqa: S310
         while chunk := resp.read(1 << 20):
             out.write(chunk)
+            done += len(chunk)
+            if progress is not None:
+                progress(done)
     actual = sha256_file(tmp)
     if actual != spec.sha256:
         tmp.unlink(missing_ok=True)
@@ -211,14 +218,15 @@ def _pool(self, hidden: np.ndarray) -> np.ndarray:
 
 
 if __name__ == "__main__":
-    from . import detectors, readers
+    from . import detectors, faces, readers
 
     logging.basicConfig(level=logging.INFO)
     args = [a for a in sys.argv[1:] if a != "--all"]
     destination = Path(args[0] if args else "models")
     # Every bundled model: backbones (states), detectors (objects), readers and wheel readers
-    # (readings); with --all also the ones downloaded on first use, for the tests of every model.
+    # (readings); with --all also the ones downloaded on first use (the face models are never
+    # bundled), for the tests of every model.
     specs = [*BACKBONES.values(), *detectors.DETECTORS.values(), *readers.READERS.values()]
-    for spec in [*specs, *readers.WHEEL_READERS.values()]:
+    for spec in [*specs, *readers.WHEEL_READERS.values(), faces.DETECTOR, *faces.RECOGNISERS.values()]:
         if spec.bundled or "--all" in sys.argv:
             print(download(spec, destination))
diff --git a/visionstate/backend/visionstate/bundle.py b/visionstate/backend/visionstate/bundle.py
index 031093d..bc12625 100644
--- a/visionstate/backend/visionstate/bundle.py
+++ b/visionstate/backend/visionstate/bundle.py
@@ -9,7 +9,7 @@
 
 from sqlalchemy import select
 
-from .db import Database, Prediction, Sample, Sensor
+from .db import Database, Face, Person, Prediction, Sample, Sensor
 from .redact import has_credentials, redact
 from .settings import EXPORT_STRIP_CREDENTIALS, UPLOAD_LIMITS, VERSION
 from .storage import Storage
@@ -18,8 +18,11 @@
 MANIFEST = "manifest.json"
 
 
-def export_sensor(db: Database, storage: Storage, sensor_id: int, target: Path) -> str:
-    """Writes a bundle for one sensor to ``target``; returns the suggested download filename."""
+def export_sensor(db: Database, storage: Storage, sensor_id: int, target: Path, include_faces: bool = False) -> str:
+    """Writes a bundle for one sensor to ``target``; returns the suggested download filename.
+
+    The faces taught on this sensor go along only with ``include_faces`` (the user ticks it), with
+    the names of their people; their embeddings never do (an import computes them again)."""
     with db.session() as s:
         sensor = s.get(Sensor, sensor_id)
         if sensor is None:
@@ -49,9 +52,28 @@ def export_sensor(db: Database, storage: Storage, sensor_id: int, target: Path)
                 "states": [{"key": st.key, "name": st.name, "color": st.color} for st in sensor.states],
             },
             "samples": [],
+            "faces": [],
         }
         samples = s.scalars(select(Sample).where(Sample.sensor_id == sensor_id)).all()
+        query = select(Face, Person).join(Person, Face.person_id == Person.id).where(Face.sensor_id == sensor_id)
+        taught_faces = [(face, person) for face, person in s.execute(query).all()] if include_faces else []
         with zipfile.ZipFile(target, "w", zipfile.ZIP_DEFLATED) as archive:
+            for face, person in taught_faces:
+                path = storage.face_path(face.filename)
+                if not path.exists():
+                    continue
+                archive.write(path, f"faces/{face.filename}")
+                manifest["faces"].append(
+                    {
+                        "file": face.filename,
+                        "person": person.key,
+                        "name": person.name,
+                        "origin": face.origin,
+                        "is_night": face.is_night,
+                        "face": face.face,
+                        "created_at": face.created_at.isoformat(),
+                    }
+                )
             for sample in samples:
                 path = storage.sample_path(sensor_id, sample.filename)
                 if not path.exists():
@@ -242,9 +264,9 @@ def read_manifest(path: Path) -> dict:
     return manifest
 
 
-def read_sample_bytes(path: Path, filename: str) -> bytes:
+def read_sample_bytes(path: Path, filename: str, folder: str = "samples") -> bytes:
     with zipfile.ZipFile(path) as archive:
-        info = archive.getinfo(f"samples/{filename}")
+        info = archive.getinfo(f"{folder}/{filename}")
         if info.file_size > UPLOAD_LIMITS["max_zip_member_mb"] * 1_000_000:
             raise ValueError(f"{filename} is too large")
         return archive.read(info)
diff --git a/visionstate/backend/visionstate/db.py b/visionstate/backend/visionstate/db.py
index 1ed2b94..71799d9 100644
--- a/visionstate/backend/visionstate/db.py
+++ b/visionstate/backend/visionstate/db.py
@@ -27,7 +27,7 @@
 from sqlalchemy.engine import Connection
 from sqlalchemy.orm import DeclarativeBase, Mapped, Session, mapped_column, relationship, sessionmaker
 
-SCHEMA_VERSION = 12
+SCHEMA_VERSION = 13
 
 # Schema upgrades for existing databases, keyed on the version they upgrade to.
 # Each step is a list of (table, column, SQL type) columns to add.
@@ -49,6 +49,7 @@
         ("sample", "score", "FLOAT"),
     ],
     11: [("prediction", "sample_id", "INTEGER")],
+    # 13: the people and their faces (new tables: person, face, face_embedding).
 }
 
 
@@ -243,6 +244,47 @@ class ModelInfo(Base):
     train_seconds: Mapped[float] = mapped_column(Float, default=0.0)
 
 
+class Person(Base):
+    """Someone taught by their face, known to every object sensor that recognises faces. ``key``
+    is the entity key on those sensors (``binary_sensor.<sensor>_<key>``) and of their own
+    sensor (``sensor.visionstate_<key>``): never changed once made."""
+
+    __tablename__ = "person"
+
+    id: Mapped[int] = mapped_column(primary_key=True)
+    key: Mapped[str] = mapped_column(String(64), unique=True)
+    name: Mapped[str] = mapped_column(String(128))
+    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow)
+
+
+class Face(Base):
+    """A taught face: the face with a margin, not aligned, in <data>/faces
+    (storage.Storage.face_path), the face in that crop's pixels {box, points, score} (see
+    faces.face_to_dict), and the camera it was taught on (null once that sensor is deleted: the
+    face stays with the person)."""
+
+    __tablename__ = "face"
+
+    id: Mapped[int] = mapped_column(primary_key=True)
+    person_id: Mapped[int] = mapped_column(ForeignKey("person.id", ondelete="CASCADE"), index=True)
+    sensor_id: Mapped[int | None] = mapped_column(ForeignKey("sensor.id", ondelete="SET NULL"), nullable=True)
+    filename: Mapped[str] = mapped_column(String(256))
+    origin: Mapped[str] = mapped_column(String(32))  # live | history | review | import
+    is_night: Mapped[bool] = mapped_column(Boolean, default=False)
+    face: Mapped[dict] = mapped_column(JSON)
+    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow)
+
+
+class FaceEmbedding(Base):
+    """A taught face's embedding by one face model (made again when the model changes)."""
+
+    __tablename__ = "face_embedding"
+
+    face_id: Mapped[int] = mapped_column(ForeignKey("face.id", ondelete="CASCADE"), primary_key=True)
+    model: Mapped[str] = mapped_column(String(64), primary_key=True)
+    vector: Mapped[bytes] = mapped_column(LargeBinary)
+
+
 class Setting(Base):
     __tablename__ = "setting"
 
diff --git a/visionstate/backend/visionstate/engine/__init__.py b/visionstate/backend/visionstate/engine/__init__.py
index c984708..024e46f 100644
--- a/visionstate/backend/visionstate/engine/__init__.py
+++ b/visionstate/backend/visionstate/engine/__init__.py
@@ -7,7 +7,7 @@
 
 from .logic import entity_triggers, filter_detections, next_check_at, review_reason, update_tracks
 from .runtime import Runtime
-from .state import Debouncer, LiveState, ObjectTrack, SensorConfig
+from .state import Debouncer, LiveState, ObjectTrack, SensorConfig, with_people
 
 __all__ = [
     "Debouncer",
@@ -15,6 +15,7 @@
     "ObjectTrack",
     "Runtime",
     "SensorConfig",
+    "with_people",
     "entity_triggers",
     "filter_detections",
     "next_check_at",
diff --git a/visionstate/backend/visionstate/engine/base.py b/visionstate/backend/visionstate/engine/base.py
index 23f6ae0..60fc325 100644
--- a/visionstate/backend/visionstate/engine/base.py
+++ b/visionstate/backend/visionstate/engine/base.py
@@ -14,7 +14,7 @@
 
 from sqlalchemy import select
 
-from .. import backbones, classifier, detectors, readers, teach
+from .. import backbones, classifier, detectors, faces, readers, teach
 from ..db import Database, Sensor
 from ..ha_events import HaEventListener
 from ..lights import Lights
@@ -45,6 +45,7 @@ def __init__(self, settings: Settings, db: Database):
         self._registry_wanted = asyncio.Event()  # discovery was published: read the registry again
         self.global_review: dict = {}  # global review rules (DB setting "review")
         self.global_teach: dict = {}  # the user's teaching limits for all sensors (DB setting "teach")
+        self.global_faces: dict = {}  # the user's face limits for all sensors (DB setting "faces")
         self.storage_rules: dict = {}  # history limits (DB setting "storage"); see storage_limits()
         self.history_trimmed = False  # frames were removed to stay under the size limit (until limits change)
         self.reminder_rules: dict = {}  # the review reminder (DB setting "reminder"); see reminder_settings()
@@ -62,6 +63,20 @@ def __init__(self, settings: Settings, db: Database):
         self.wheel_reader: readers.WheelReader | None = None  # loaded on first use by a counter
         self.wheel_reader_error = ""
         self._wheel_reader_lock = asyncio.Lock()
+        # Faces (object sensors): the face model, loaded in the background when a sensor turns face
+        # recognition on (engine/faces.py); the chosen recogniser (DB setting "face_model").
+        self.face_model: faces.FaceModel | None = None
+        self.face_choice = faces.DEFAULT_RECOGNISER
+        self.face_error = ""
+        self.face_progress: dict | None = None  # {"done", "total"} bytes while it downloads
+        self._face_lock = asyncio.Lock()
+        self._face_task: asyncio.Task | None = None
+        self.face_index: faces.FaceIndex | None = None  # the people's taught faces; built on use
+        self._face_generation = 0
+        # Where each person is seen (their own entity, sensor.visionstate_<key>): what was last
+        # published, and when and on which sensor they were last seen.
+        self._person_where: dict[str, str] = {}
+        self._person_last: dict[str, tuple[float, int]] = {}
         self.heads: dict[int, classifier.Head] = {}
         self.taught: dict[int, teach.TaughtIndex] = {}  # object sensors; built on use, dropped on change
         self._taught_generation: dict[int, int] = {}  # counts changes to each sensor's taught boxes
@@ -134,3 +149,8 @@ def backbone_id(self) -> str:
     def teach_limits(self, cfg: SensorConfig) -> dict:
         """An object sensor's teaching limits: the backbone's defaults < global < the sensor's own."""
         return merge_teach(self.backbone_id, self.global_teach, cfg.objects.get("teach"))
+
+    def face_limits(self, cfg: SensorConfig | None) -> dict[str, float]:
+        """An object sensor's face limits: the face model's defaults < global < the sensor's own
+        (``None``: the global ones)."""
+        return faces.merge_limits(self.face_choice, self.global_faces, cfg.objects.get("faces") if cfg else None)
diff --git a/visionstate/backend/visionstate/engine/faces.py b/visionstate/backend/visionstate/engine/faces.py
new file mode 100644
index 0000000..75948cc
--- /dev/null
+++ b/visionstate/backend/visionstate/engine/faces.py
@@ -0,0 +1,468 @@
+"""Recognising people by their face on object sensors (see faces.py and docs/FACE_RECOGNITION_PLAN.md).
+
+The face model is downloaded and loaded in the background when a sensor needs it — a sensor keeps
+working as an object sensor without faces meanwhile — and let go when no sensor uses it.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from collections.abc import Sequence
+from datetime import UTC, datetime
+
+import numpy as np
+from PIL import Image
+from sqlalchemy import select
+
+from .. import backbones, faces, imaging
+from ..db import Face, FaceEmbedding, Person, Sensor
+from ..mqtt import person_topics
+from ..redact import redact
+from ..settings import FACE_UNKNOWN_KEY, FACES, KIND_OBJECTS, PERSON_NOT_SEEN, TEACH, faces_on, merge_objects
+from .base import RuntimeBase
+from .models import ModelsMixin
+from .publishing import PublishingMixin
+from .state import SensorConfig
+
+log = logging.getLogger(__name__)
+
+
+def _iou(a: Sequence[float], b: Sequence[float]) -> float:
+    x1, y1 = max(a[0], b[0]), max(a[1], b[1])
+    x2, y2 = min(a[2], b[2]), min(a[3], b[3])
+    inter = max(0.0, x2 - x1) * max(0.0, y2 - y1)
+    union = (a[2] - a[0]) * (a[3] - a[1]) + (b[2] - b[0]) * (b[3] - b[1]) - inter
+    return inter / union if union > 0 else 0.0
+
+
+def same_box(previous: list[dict], box: Sequence[float]) -> dict | None:
+    """The last check's person box that is this one (it overlaps by TEACH["keep_iou"])."""
+    best = max(
+        (d for d in previous if d.get("key") == "person" or d.get("was") == "person"),
+        key=lambda d: _iou(d["box"], box),
+        default=None,
+    )
+    return best if best is not None and _iou(best["box"], box) >= TEACH["keep_iou"] else None
+
+
+class FacesMixin(ModelsMixin, PublishingMixin, RuntimeBase):
+    # --- The model ------------------------------------------------------------------------------
+
+    def face_status(self) -> dict:
+        """The face model for the UI: chosen, loaded, downloading (bytes), or why it failed."""
+        spec = faces.recogniser_spec(self.face_choice)
+        return {
+            "model": spec.id,
+            "loaded": self.face_model is not None and self.face_model.id == spec.id,
+            "loading": self._face_task is not None and not self._face_task.done(),
+            "progress": self.face_progress,
+            "error": self.face_error,
+            "downloaded": self.face_files_present(spec.id),
+        }
+
+    def face_files_present(self, model_id: str) -> bool:
+        spec = faces.recogniser_spec(model_id)
+        return self.model_path(faces.DETECTOR) is not None and self.model_path(spec) is not None
+
+    def wanted_faces(self) -> faces.FaceModel | None:
+        """The face model when it is loaded; otherwise its loading is started in the background
+        (unless it failed: then only ``retry_faces`` tries again) and None is returned."""
+        model = self.face_model
+        if model is not None and model.id == self.face_choice:
+            return model
+        if self._on_loop(self.wanted_faces):
+            return None  # called from a worker thread: started on the event loop
+        if not self.face_error and (self._face_task is None or self._face_task.done()):
+            self._face_task = self._spawn(self._load_faces())
+        return None
+
+    def retry_faces(self) -> None:
+        self.face_error = ""
+        self.wanted_faces()
+
+    def _set_progress(self, done: int, total: int) -> None:
+        self.face_progress = {"done": done, "total": total}
+
+    async def _load_faces(self) -> None:
+        async with self._face_lock:
+            detector_spec, spec = faces.DETECTOR, faces.recogniser_spec(self.face_choice)
+            try:
+                missing = [s for s in (detector_spec, spec) if self.model_path(s) is None]
+                total, before = sum(s.size for s in missing), 0
+                paths = []
+                for model_spec in (detector_spec, spec):
+                    path = self.model_path(model_spec)
+                    if path is None:
+                        start = before
+                        self._set_progress(start, total)
+                        try:
+                            path = await asyncio.to_thread(
+                                backbones.download,
+                                model_spec,
+                                self.settings.models_dir,
+                                lambda n, start=start: self._set_progress(start + n, total),
+                            )
+                        except Exception as err:
+                            raise RuntimeError(
+                                f"Could not download {model_spec.filename} from {model_spec.url} ({err}). "
+                                f"Without internet, put that file in {self.settings.models_dir} by hand."
+                            ) from err
+                        before += model_spec.size
+                    paths.append(path)
+                model = await asyncio.to_thread(
+                    lambda: faces.FaceModel(
+                        faces.FaceDetector(detector_spec, paths[0]), faces.Recogniser(spec, paths[1])
+                    )
+                )
+            except Exception as err:  # noqa: BLE001 - shown in the UI; the sensors go on without faces
+                self.face_error = redact(str(err)) or type(err).__name__
+                log.exception("Could not load the face model %s", spec.id)
+                return
+            finally:
+                self.face_progress = None
+            replaced, self.face_model, self.face_error = self.face_model, model, ""
+            log.info("Face model %s loaded", spec.id)
+        if replaced is not None:
+            await asyncio.to_thread(backbones.release_memory)
+        for sensor_id in await asyncio.to_thread(self.face_sensor_ids):
+            self.wake(sensor_id, force=True)
+
+    def face_sensor_ids(self) -> list[int]:
+        with self.db.session() as s:
+            rows = s.scalars(select(Sensor).where(Sensor.kind == KIND_OBJECTS)).all()
+            return [row.id for row in rows if faces_on(merge_objects(row.objects))]
+
+    async def faces_settled(self) -> None:
+        """A sensor turned faces on or off: load the model when one uses it, let it go when none does."""
+        if await asyncio.to_thread(self.face_sensor_ids):
+            self.wanted_faces()
+            return
+        if self.face_model is not None:
+            self.face_model = None
+            self.face_index = None
+            await asyncio.to_thread(backbones.release_memory)
+            log.info("Face model let go: no sensor recognises faces")
+
+    async def set_face_model(self, model_id: str) -> None:
+        """Choose the recogniser. Taught faces are embedded again with it (their crops are kept)."""
+        await asyncio.to_thread(self.db.set_setting, "face_model", model_id)
+        self.face_choice = model_id
+        self.face_error = ""
+        self.face_index = None
+        if self.face_model is not None or await asyncio.to_thread(self.face_sensor_ids):
+            self.wanted_faces()
+
+    def delete_face_files(self, model_id: str) -> None:
+        """Delete a downloaded face model (only while no sensor uses it); the face detector goes
+        with the last one."""
+        models = self.settings.models_dir
+        (models / faces.RECOGNISERS[model_id].filename).unlink(missing_ok=True)
+        if not any((models / spec.filename).exists() for spec in faces.RECOGNISERS.values()):
+            (models / faces.DETECTOR.filename).unlink(missing_ok=True)
+
+    # --- What was taught (the people, shared by every sensor) -------------------------------------
+
+    async def face_index_for(self, model: faces.FaceModel) -> faces.FaceIndex:
+        index = self.face_index
+        if index is None or index.model != model.id or index.generation != self._face_generation:
+            index = await asyncio.to_thread(self._build_face_index, model, self._face_generation)
+            self.face_index = index
+        return index
+
+    def _build_face_index(self, model: faces.FaceModel, generation: int) -> faces.FaceIndex:
+        with self.db.session() as s:
+            examples = [
+                (face_id, key, filename, dict(face))
+                for face_id, key, filename, face in s.execute(
+                    select(Face.id, Person.key, Face.filename, Face.face)
+                    .join(Person, Face.person_id == Person.id)
+                    .order_by(Face.id)
+                ).all()
+            ]
+            cached = {
+                r.face_id: np.frombuffer(r.vector, dtype=np.float32)
+                for r in s.scalars(
+                    select(FaceEmbedding).where(
+                        FaceEmbedding.face_id.in_([e[0] for e in examples]), FaceEmbedding.model == model.id
+                    )
+                )
+            }
+        ids, labels, vectors = [], [], []
+        for face_id, key, filename, face in examples:
+            vector = cached.get(face_id)
+            if vector is None:
+                path = self.storage.face_path(filename)
+                if not path.exists():
+                    continue
+                crop = imaging.load(path)
+                aligned = faces.align(crop, faces.face_from_dict(face), model.recogniser.spec.input_size)
+                vector = model.recogniser.embed([aligned])[0]
+                with self.db.session() as s:
+                    s.merge(FaceEmbedding(face_id=face_id, model=model.id, vector=vector.tobytes()))
+            ids.append(face_id)
+            labels.append(key)
+            vectors.append(vector)
+        stacked = np.stack(vectors) if vectors else np.zeros((0, 0), dtype=np.float32)
+        return faces.FaceIndex(model.id, ids, labels, stacked, generation)
+
+    def faces_changed(self) -> None:
+        """Taught faces were added, moved or removed: every sensor that recognises faces compares
+        with the new set from its next check on."""
+        self._face_generation += 1
+        self.face_index = None
+        for sensor_id in self.face_sensor_ids():
+            self.wake(sensor_id, force=True)
+
+    async def people_changed(self) -> None:
+        """A person was added, renamed or forgotten: the entities of every sensor that recognises
+        faces, and each person's own entity, follow."""
+        await asyncio.to_thread(self.faces_changed)
+        for sensor_id in await asyncio.to_thread(self.face_sensor_ids):
+            cfg = await asyncio.to_thread(self.load_sensor, sensor_id)
+            if cfg is not None:
+                await self.publish_discovery(cfg)
+        await self.publish_people()
+
+    # --- Where each person is (sensor.visionstate_<key>) ----------------------------------------
+
+    def _people(self) -> list[tuple[str, str]]:
+        with self.db.session() as s:
+            return [(p.key, p.name) for p in s.scalars(select(Person).order_by(Person.created_at))]
+
+    def person_seen(self, key: str) -> dict:
+        """Where a person is seen now (a sensor's slug, or PERSON_NOT_SEEN), and when and on which
+        sensor they were last seen."""
+        at, sensor_id = self._person_last.get(key, (0.0, 0))
+        return {"now": self._person_where.get(key, PERSON_NOT_SEEN), "at": at or None, "sensor_id": sensor_id or None}
+
+    async def publish_people(self) -> None:
+        """Each person's own entity (again): discovery for the people there are, removed for the
+        ones forgotten, and where each is."""
+        people = await asyncio.to_thread(self._people)
+        keys = {key for key, _ in people}
+        for key in set(self._person_where) - keys:
+            await self.mqtt.remove_person(key)
+            self._person_where.pop(key, None)
+            self._person_last.pop(key, None)
+        await self.mqtt.publish_people_discovery(people)
+        for key, _ in people:
+            await self._publish_person(key, self._person_where.get(key, PERSON_NOT_SEEN), force=True)
+
+    async def update_people(self, cfg: SensorConfig) -> None:
+        """After a check of a sensor that recognises faces: where each person is now. A person
+        seen on several sensors is where they were seen last."""
+        live = self.live_state(cfg.id)
+        keys = [c["key"] for c in cfg.custom_labels if c.get("face")]
+        for key in keys:
+            track = live.tracks.get(key)
+            if track is not None and track.on and track.last_seen:
+                last = self._person_last.get(key)
+                if last is None or track.last_seen >= last[0]:
+                    self._person_last[key] = (track.last_seen, cfg.id)
+        for key in keys:
+            where = PERSON_NOT_SEEN
+            _at, sensor_id = self._person_last.get(key, (0.0, 0))
+            track = self.live_state(sensor_id).tracks.get(key) if sensor_id else None
+            if track is not None and track.on:
+                where = cfg.slug if sensor_id == cfg.id else await self._sensor_slug(sensor_id) or PERSON_NOT_SEEN
+            await self._publish_person(key, where)
+
+    async def _sensor_slug(self, sensor_id: int) -> str:
+        cfg = await asyncio.to_thread(self.load_sensor, sensor_id)
+        return cfg.slug if cfg else ""
+
+    async def _publish_person(self, key: str, where: str, force: bool = False) -> None:
+        if not force and self._person_where.get(key) == where:
+            return
+        self._person_where[key] = where
+        t = person_topics(key)
+        at, sensor_id = self._person_last.get(key, (0.0, 0))
+        last = await asyncio.to_thread(self.load_sensor, sensor_id) if sensor_id else None
+        attributes = {
+            "last_seen": datetime.fromtimestamp(at, UTC).isoformat() if at else None,
+            "last_sensor": last.slug if last else None,
+            "last_sensor_name": last.name if last else None,
+        }
+        await self.mqtt.publish(t["state"], where, retain=True)
+        await self.mqtt.publish(t["attributes"], attributes, retain=True)
+
+    # --- One check ------------------------------------------------------------------------------
+
+    async def find_faces(
+        self, model: faces.FaceModel, image: Image.Image, boxes: list[Sequence[float]]
+    ) -> list[tuple[faces.Face | None, Image.Image | None]]:
+        """The best face in each person box (normalised), in frame pixels, with it aligned."""
+        regions, offsets = [], []
+        for box in boxes:
+            x1, y1, x2, y2 = faces.head_region(box, image.width, image.height)
+            regions.append(image.crop((x1, y1, max(x2, x1 + 1), max(y2, y1 + 1))))
+            offsets.append((x1, y1))
+        async with self._sem:
+            found = await asyncio.to_thread(model.detector.detect, regions)
+        result: list[tuple[faces.Face | None, Image.Image | None]] = []
+        for face, (dx, dy) in zip(found, offsets, strict=True):
+            if face is None:
+                result.append((None, None))
+                continue
+            moved = face.moved(dx, dy, 1.0)
+            aligned = await asyncio.to_thread(faces.align, image, moved, model.recogniser.spec.input_size)
+            result.append((moved, aligned))
+        return result
+
+    async def apply_faces(
+        self, cfg: SensorConfig, image: Image.Image, found: list[dict], previous: list[dict]
+    ) -> list[dict]:
+        """Look for faces in the person boxes of a check and tell who they are.
+
+        Each searched person box gets ``face`` ({box (normalised), problem, label, similarity,
+        state, sure, unknown, age}); a recognised person becomes the box's own ``label`` (it
+        counts for that person), a maybe ``ask``, and a clearly seen face that matched nobody for
+        FACES["unknown_after"] checks ``label`` = FACE_UNKNOWN_KEY. A box that stays where it was
+        keeps its name while no usable face is seen (and while the face stays close enough).
+        """
+        model = self.wanted_faces()
+        if model is None:
+            return found
+        live = self.live_state(cfg.id)
+        index = await self.face_index_for(model)
+        limits = self.face_limits(cfg)
+        settings = cfg.objects["faces"]
+        result = [dict(d) for d in found]
+        searched: list[int] = []
+        waiting: list[tuple[int, dict]] = []  # person boxes to search, with what they were last check
+        for i, d in enumerate(result):
+            if d["key"] != "person" or d.get("filtered"):
+                continue
+            face_before = (same_box(previous, d["box"]) or {}).get("face") or {}
+            if face_before.get("sure") and face_before.get("age", 0) + 1 < FACES["recheck_every"]:
+                # A sure name: carried over for a few checks without looking again (saves CPU).
+                result[i]["face"] = {**face_before, "age": face_before.get("age", 0) + 1}
+                result[i]["label"] = face_before["label"]
+                continue
+            waiting.append((i, face_before))
+        # At most FACES["max_faces"] per check: new boxes first, then the ones searched longest ago,
+        # so in a crowd every person is looked at in turn.
+        waiting.sort(key=lambda w: (bool(w[1]), -w[1].get("age", 0), -result[w[0]]["score"]))
+        for i, face_before in waiting:
+            if len(searched) < FACES["max_faces"]:
+                searched.append(i)
+            elif face_before:  # not looked at this check: it stays what it was
+                result[i]["face"] = {**face_before, "age": face_before.get("age", 0) + 1}
+                if face_before.get("label") and face_before.get("state") == "match":
+                    result[i]["label"] = face_before["label"]
+                elif face_before.get("unknown", 0) >= FACES["unknown_after"] and not face_before.get("named"):
+                    result[i]["label"] = FACE_UNKNOWN_KEY
+        if not searched:
+            return result
+        seen = await self.find_faces(model, image, [result[i]["box"] for i in searched])
+        live.face_frame = (image.width, image.height)
+        live.face_min_px = limits["min_face_px"]
+        taken: list[tuple[float, float, float, float]] = []  # one face counts for one person box
+        usable: list[tuple[int, faces.Face, Image.Image]] = []
+        for i, (face, aligned) in zip(searched, seen, strict=True):
+            problem = "none"
+            if face is not None and aligned is not None:
+                if any(_iou(face.box, other) > 0.5 for other in taken):
+                    face, problem = None, "none"
+                else:
+                    taken.append(face.box)
+                    min_px = limits["min_face_px"]
+                    problem = faces.problem(face, aligned, min_px, (image.width, image.height)) or ""
+            entry: dict = {"problem": problem or None}
+            if face is not None:
+                w, h = image.width, image.height
+                x1, y1, x2, y2 = face.box
+                entry["box"] = [round(x1 / w, 4), round(y1 / h, 4), round(x2 / w, 4), round(y2 / h, 4)]
+                entry["face_px"] = round(face.width)
+            result[i]["face"] = entry
+            live.face_stats.append(problem or "ok")
+            if not problem and face is not None and aligned is not None:
+                usable.append((i, face, aligned))
+        vectors = np.zeros((0, 0), dtype=np.float32)
+        if usable and index.ids:
+            async with self._sem:
+                vectors = await asyncio.to_thread(model.recogniser.embed, [a for _, _, a in usable])
+        for n, (i, face, _aligned) in enumerate(usable):
+            clear = faces.clearly_seen(face, limits["min_face_px"])
+            self._name_face(result[i], previous, index, vectors[n] if index.ids else None, limits, settings, clear)
+        for i in searched:
+            if result[i]["face"].get("problem"):
+                self._keep_name(result[i], previous)  # no usable face now: keep what the box was
+        return result
+
+    def _name_face(
+        self,
+        det: dict,
+        previous: list[dict],
+        index: faces.FaceIndex,
+        vector: np.ndarray | None,
+        limits: dict[str, float],
+        settings: dict,
+        clear: bool = True,
+    ) -> None:
+        """Who the usable face of a person box is (see apply_faces). ``clear``: seen well enough
+        (faces.clearly_seen) that matching nobody counts towards Unknown person."""
+        face = det["face"]
+        before = same_box(previous, det["box"])
+        face_before = (before or {}).get("face") or {}
+        named_before = face_before.get("label") if face_before.get("state") == "match" else None
+        if vector is None:  # nobody taught yet: a usable face, nothing to compare with
+            identity = faces.Identity(None, 0.0, 0.0, None)
+        else:
+            identity = faces.identify(index, vector)
+        decision = faces.decide(identity, limits, named_before)
+        named = bool(face_before.get("named")) or named_before is not None
+        face.update({"similarity": identity.similarity, "candidate": identity.label, "age": 0, "named": named})
+        if decision == "match" and identity.label is not None:
+            agreed = named_before == identity.label or face_before.get("candidate") == identity.label
+            if identity.similarity >= limits["sure_similarity"] or agreed:
+                sure = identity.similarity >= limits["sure_similarity"]
+                face.update({"label": identity.label, "state": "match", "sure": sure, "named": True})
+                det["label"] = identity.label
+                det["match"] = {"id": identity.example_id, "label": identity.label, "similarity": identity.similarity}
+            else:
+                face["state"] = "pending"  # a second check must agree first
+            return
+        if decision == "ask" and identity.label is not None:
+            face["state"] = "ask"
+            det["ask"] = {
+                "id": identity.example_id,
+                "label": identity.label,
+                "similarity": identity.similarity,
+                "face": True,
+            }
+            return
+        if not clear:  # small or turned: not looking like anyone may only be the angle
+            face.update({"state": "unclear", "unknown": face_before.get("unknown", 0)})
+            if face["unknown"] >= FACES["unknown_after"] and not named and not det.get("label"):
+                det["label"] = FACE_UNKNOWN_KEY  # a stranger seen clearly before stays one
+            return
+        face["state"] = "unknown"
+        if not index.ids:
+            return  # nobody was taught: there is no "unknown" yet
+        face["unknown"] = face_before.get("unknown", 0) + 1
+        if not named and face["unknown"] >= FACES["unknown_after"] and not det.get("label"):
+            det["label"] = FACE_UNKNOWN_KEY
+        if settings["ask_unknown"]:
+            det["ask"] = {"id": None, "label": FACE_UNKNOWN_KEY, "similarity": identity.similarity, "face": True}
+
+    @staticmethod
+    def _keep_name(det: dict, previous: list[dict]) -> None:
+        """A person box without a usable face this check keeps the name it had in the last one."""
+        face_before = (same_box(previous, det["box"]) or {}).get("face") or {}
+        if face_before.get("state") == "match" and face_before.get("label"):
+            det["face"].update(
+                {
+                    "label": face_before["label"],
+                    "state": "match",
+                    "kept": True,
+                    "named": True,
+                    "similarity": face_before.get("similarity"),
+                }
+            )
+            det["label"] = face_before["label"]
+        elif face_before.get("unknown"):
+            det["face"].update({"unknown": face_before["unknown"], "named": bool(face_before.get("named"))})
+            if face_before["unknown"] >= FACES["unknown_after"] and not face_before.get("named"):
+                det["label"] = det.get("label") or FACE_UNKNOWN_KEY
diff --git a/visionstate/backend/visionstate/engine/models.py b/visionstate/backend/visionstate/engine/models.py
index 5f34b11..31e27b6 100644
--- a/visionstate/backend/visionstate/engine/models.py
+++ b/visionstate/backend/visionstate/engine/models.py
@@ -23,6 +23,10 @@ class ModelsMixin(TrainingMixin, RuntimeBase):
     def model_path(self, spec: backbones.ModelFile) -> Path | None:
         return backbones.locate(spec, [self.settings.bundled_models_dir, self.settings.models_dir])
 
+    def downloaded(self, spec: backbones.ModelFile) -> bool:
+        """Whether a model was downloaded (and can be deleted again), not included in the app."""
+        return (self.settings.models_dir / spec.filename).exists()
+
     async def load_embedder(self, backbone_id: str) -> None:
         spec = backbones.BACKBONES.get(backbone_id) or backbones.BACKBONES[backbones.DEFAULT_BACKBONE]
         path = self.model_path(spec)
diff --git a/visionstate/backend/visionstate/engine/objects.py b/visionstate/backend/visionstate/engine/objects.py
index 8c55640..b2ee2b4 100644
--- a/visionstate/backend/visionstate/engine/objects.py
+++ b/visionstate/backend/visionstate/engine/objects.py
@@ -14,8 +14,9 @@
 from ..db import Prediction
 from ..mqtt import object_topics, topics
 from ..redact import redact
-from ..settings import DETECTION, TEACH
+from ..settings import DETECTION, FACE_UNKNOWN_KEY, FACES, TEACH
 from .base import RuntimeBase
+from .faces import FacesMixin
 from .logic import update_tracks
 from .publishing import PublishingMixin
 from .state import SensorConfig
@@ -25,7 +26,7 @@
 OBJECT_BOX_COLOR = "#7ee2b8"  # boxes drawn on the Home Assistant image of an object sensor
 
 
-class ObjectChecksMixin(TeachingMixin, PublishingMixin, RuntimeBase):
+class ObjectChecksMixin(FacesMixin, TeachingMixin, PublishingMixin, RuntimeBase):
     async def _run_objects(self, cfg: SensorConfig, image: Image.Image) -> None:
         """One check of an object sensor: detect, compare with what was taught, update and publish."""
         live = self.live_state(cfg.id)
@@ -45,6 +46,8 @@ async def _run_objects(self, cfg: SensorConfig, image: Image.Image) -> None:
             found = [d for d in found if d["score"] >= cfg.threshold and d["key"] in classes]
         if index is not None:
             found = await self.apply_taught(cfg, image, index, found, candidates, live.detections)
+        if cfg.faces_on:
+            found = await self.apply_faces(cfg, image, found, live.detections)
         counted = [d for d in found if not d.get("filtered")]
         now = time.time()
         live.detections = found
@@ -56,6 +59,8 @@ async def _run_objects(self, cfg: SensorConfig, image: Image.Image) -> None:
         # Something that was there is missing: look again soon instead of at the next interval.
         missing = any(t.on and 0 < t.misses < DETECTION["clear_misses"] for t in live.tracks.values())
         live.recheck_at = now + DETECTION["recheck_s"] if missing else 0.0
+        if cfg.faces_on:
+            await self.update_people(cfg)  # each person's own entity: where they are seen
 
         for key in keys:
             track = live.tracks[key]
@@ -73,6 +78,7 @@ async def _run_objects(self, cfg: SensorConfig, image: Image.Image) -> None:
             await self._send(cfg, ot["attributes"], attributes, retain=True)
         names = {key: label.name for key, label in detectors.LABELS.by_key.items()}
         names.update({c["key"]: c["name"] for c in cfg.custom_labels})
+        names[FACE_UNKNOWN_KEY] = "Unknown person"
         drawn = [{**d, "key": d.get("label") or d["key"]} for d in counted]
         annotated = await asyncio.to_thread(
             lambda: imaging.crop_box(
@@ -86,8 +92,34 @@ async def _run_objects(self, cfg: SensorConfig, image: Image.Image) -> None:
             published = "on" if track.on else "off"
             await asyncio.to_thread(self._record_detection, cfg.id, image, key, published, track.score, found)
         await self._record_filtered(cfg, image, found, now)
+        if cfg.faces_on:
+            await self._record_face(cfg, image, found, now)
         await self._ask_unsure(cfg, image, found, now)
 
+    async def _record_face(self, cfg: SensorConfig, image: Image.Image, found: list[dict], now: float) -> None:
+        """Keep a frame in the history when a usable face nobody is named for is seen, so the user
+        can name them there later: at most one per FACES["record_cooldown_s"], unless the face came
+        FACES["record_closer"] times closer since (the best look at someone at a doorbell)."""
+        live = self.live_state(cfg.id)
+        unnamed = [
+            d
+            for d in found
+            if (face := d.get("face"))
+            and face.get("problem") is None
+            and face.get("box")
+            and face.get("state") != "match"
+        ]
+        if not unnamed:
+            return
+        best = max(unnamed, key=lambda d: d["face"].get("face_px", 0))
+        width = float(best["face"].get("face_px", 0))
+        at, logged_width = live.face_logged
+        if now - at < FACES["record_cooldown_s"] and width < logged_width * FACES["record_closer"]:
+            return
+        live.face_logged = (now, width)
+        key = best.get("label") or best["key"]
+        await asyncio.to_thread(self._record_detection, cfg.id, image, key, "face", best["score"], found)
+
     async def _ask_unsure(self, cfg: SensorConfig, image: Image.Image, found: list[dict], now: float) -> None:
         """Put a box that may be an own label, but not clearly, in the review queue ("Is this Our
         car?"); the answer is taught. One question per label at a time, at most one per review
@@ -123,6 +155,9 @@ def _record_question(self, sensor_id: int, image: Image.Image, box: dict, found:
             return False
         frame = self.storage.save_history(sensor_id, image)
         question = {"label": label, "similarity": box["ask"]["similarity"], "box": box["box"], "detected": box["key"]}
+        if box["ask"].get("face") and (box.get("face") or {}).get("box"):
+            question["face"] = box["face"]["box"]  # "Is this Kari?" / "Who is this?" about a face
+            question["example"] = box["ask"].get("id")  # the closest taught face, to compare with
         with self.db.session() as s:
             s.add(
                 Prediction(
@@ -155,7 +190,8 @@ async def _record_filtered(self, cfg: SensorConfig, image: Image.Image, found: l
     def _record_detection(
         self, sensor_id: int, image: Image.Image, key: str, published: str, score: float, found: list[dict]
     ) -> None:
-        """A history row of an object sensor: a class or own label went "on" or "off", or was "filtered"."""
+        """A history row of an object sensor: a class or own label went "on" or "off", was
+        "filtered", or a face nobody is named for was seen ("face")."""
         frame = self.storage.save_history(sensor_id, image)
         with self.db.session() as s:
             s.add(
@@ -166,7 +202,7 @@ def _record_detection(
                     confidence=score,
                     probs={},
                     frame=frame,
-                    is_change=True,
+                    is_change=published != "face",  # a face seen is not a change of what is there
                     reviewed=True,  # nothing to review: the detector is not trained here
                     detections=found,
                 )
diff --git a/visionstate/backend/visionstate/engine/runtime.py b/visionstate/backend/visionstate/engine/runtime.py
index 7b4166a..6c2ad40 100644
--- a/visionstate/backend/visionstate/engine/runtime.py
+++ b/visionstate/backend/visionstate/engine/runtime.py
@@ -8,7 +8,7 @@
 
 from sqlalchemy import select
 
-from .. import backbones, detectors, readers
+from .. import backbones, detectors, faces, readers
 from ..db import Database, Sensor
 from ..ha_events import HaEventListener, fetch_entity_ids
 from ..mqtt import MqttBridge, SensorDescriptor
@@ -31,7 +31,7 @@ class Runtime(ChecksMixin, HistoryMixin, RemindersMixin):
 
     def __init__(self, settings: Settings, db: Database):
         super().__init__(settings, db)
-        self.mqtt = MqttBridge(settings, self._on_command, self._on_mqtt_connect)
+        self.mqtt = MqttBridge(settings, self._on_command, self._on_connect)
         self.ha_events = HaEventListener(settings, self._on_ha_state)
 
     async def start(self) -> None:
@@ -40,6 +40,7 @@ async def start(self) -> None:
         self._loop = asyncio.get_running_loop()
         self.global_review = await asyncio.to_thread(self.db.get_dict, "review")
         self.global_teach = await asyncio.to_thread(self.db.get_dict, "teach")
+        self.global_faces = await asyncio.to_thread(self.db.get_dict, "faces")
         self.storage_rules = await asyncio.to_thread(self.db.get_dict, "storage")
         self.reminder_rules = await asyncio.to_thread(self.db.get_dict, "reminder")
         self.reminder_sent_at = await asyncio.to_thread(self.db.get_setting, "reminder_sent_at")
@@ -47,6 +48,9 @@ async def start(self) -> None:
         await asyncio.to_thread(self._pin_setting, "backbone", backbones.DEFAULT_BACKBONE)
         await asyncio.to_thread(self._pin_setting, "detector", detectors.DEFAULT_DETECTOR)
         await asyncio.to_thread(self._pin_setting, "reader", readers.DEFAULT_READER)
+        self.face_choice = await asyncio.to_thread(self.db.get_text, "face_model", faces.DEFAULT_RECOGNISER)
+        if self.face_choice not in faces.RECOGNISERS:
+            self.face_choice = faces.DEFAULT_RECOGNISER
         backbone_id = self.db.get_text("backbone", backbones.DEFAULT_BACKBONE)
         try:
             await self.load_embedder(backbone_id)
@@ -59,6 +63,7 @@ async def start(self) -> None:
                 self.schedule_retrain(sensor_id, delay=0)
         for sensor_id in await asyncio.to_thread(self._sensor_ids):
             self._start_loop(sensor_id)
+        await self.faces_settled()  # sensors that recognise faces: load the face model in the background
         await self.refresh_trigger_entities()
         self.ha_events.start()
         self.mqtt.start()
@@ -68,6 +73,11 @@ async def start(self) -> None:
         if self.ha_events.enabled:
             self._spawn(self._entity_registry_loop())
 
+    async def _on_connect(self) -> None:
+        """MQTT (re)connected: every sensor's entities, then each person's own entity."""
+        await self._on_mqtt_connect()
+        await self.publish_people()
+
     async def stop(self) -> None:
         for handle in self._retrain_handles.values():
             handle.cancel()
@@ -110,6 +120,8 @@ async def sensor_created(self, sensor_id: int) -> None:
             await self.publish_discovery(cfg)
         self._start_loop(sensor_id)
         await self.refresh_trigger_entities()
+        if cfg and cfg.is_objects:
+            await self.faces_settled()  # made with face recognition on (the wizard)
 
     async def sensor_updated(self, sensor_id: int, retrain: bool) -> None:
         cfg = await asyncio.to_thread(self.load_sensor, sensor_id)
@@ -121,6 +133,8 @@ async def sensor_updated(self, sensor_id: int, retrain: bool) -> None:
         self.taught.pop(sensor_id, None)  # own labels or classes may have changed
         self.live_state(sensor_id).signature = None  # the region may have moved
         await self.refresh_trigger_entities()
+        if cfg.is_objects:
+            await self.faces_settled()
         self.wake(sensor_id)
 
     async def sensor_deleted(self, sensor_id: int, descriptor: SensorDescriptor) -> None:
@@ -132,6 +146,7 @@ async def sensor_deleted(self, sensor_id: int, descriptor: SensorDescriptor) ->
         self.live.pop(sensor_id, None)
         self.heads.pop(sensor_id, None)
         self.taught.pop(sensor_id, None)
+        self._person_last = {k: v for k, v in self._person_last.items() if v[1] != sensor_id}
         self._published_classes.pop(sensor_id, None)
         (self.settings.heads_dir / f"{sensor_id}.joblib").unlink(missing_ok=True)
         await self.mqtt.remove_discovery(descriptor)
@@ -140,6 +155,8 @@ async def sensor_deleted(self, sensor_id: int, descriptor: SensorDescriptor) ->
         await self.refresh_trigger_entities()
         if descriptor.kind == KIND_OBJECTS and not await asyncio.to_thread(self._sensor_ids, KIND_OBJECTS):
             self.detector = None  # the last object sensor is gone: free the memory
+        if descriptor.kind == KIND_OBJECTS:
+            await self.faces_settled()
         if descriptor.kind == KIND_READING and not await asyncio.to_thread(self._sensor_ids, KIND_READING):
             self.reader = None
         await asyncio.to_thread(backbones.release_memory)
@@ -153,6 +170,11 @@ def set_global_teach(self, limits: dict) -> None:
         self.global_teach = {k: v for k, v in limits.items() if v is not None}
         self.db.set_setting("teach", self.global_teach)
 
+    def set_global_faces(self, limits: dict) -> None:
+        """The user's face limits for all sensors; only the ones set (not None) are kept."""
+        self.global_faces = {k: v for k, v in limits.items() if v is not None}
+        self.db.set_setting("faces", self.global_faces)
+
     def _trigger_index(self) -> dict[str, set[int]]:
         index: dict[str, set[int]] = {}
         with self.db.session() as s:
diff --git a/visionstate/backend/visionstate/engine/state.py b/visionstate/backend/visionstate/engine/state.py
index c030ad3..8b0e256 100644
--- a/visionstate/backend/visionstate/engine/state.py
+++ b/visionstate/backend/visionstate/engine/state.py
@@ -8,17 +8,22 @@
 from dataclasses import dataclass, field
 
 import numpy as np
+from sqlalchemy import select
+from sqlalchemy.orm import Session, object_session
 
 from .. import detectors
-from ..db import Sensor
+from ..db import Person, Sensor
 from ..mqtt import SensorDescriptor
 from ..settings import (
+    FACE_UNKNOWN_KEY,
+    FACES,
     KIND_OBJECTS,
     KIND_READING,
     KIND_STATES,
     RUNTIME,
     TEACH,
     active_custom,
+    faces_on,
     merge_objects,
     merge_reading,
     merge_triggers,
@@ -70,10 +75,17 @@ def parents(self) -> dict[str, str]:
         """Own label -> the class it is a kind of."""
         return {label["key"]: label["parent"] for label in self.custom_labels}
 
+    @property
+    def faces_on(self) -> bool:
+        """An object sensor that recognises people by their face."""
+        return self.is_objects and faces_on(self.objects)
+
     @property
     def object_keys(self) -> list[str]:
-        """What an object sensor reports: its classes, then its own labels."""
-        return [*self.objects["classes"], *(label["key"] for label in self.custom_labels)]
+        """What an object sensor reports: its classes, then its own labels (and, recognising
+        faces, the unknown person)."""
+        unknown = [FACE_UNKNOWN_KEY] if self.faces_on else []
+        return [*self.objects["classes"], *(label["key"] for label in self.custom_labels), *unknown]
 
     @property
     def descriptor(self) -> SensorDescriptor:
@@ -81,6 +93,7 @@ def descriptor(self) -> SensorDescriptor:
         objects = (
             [(k, names[k].name, names[k].icon) for k in self.objects["classes"] if k in names]
             + [(c["key"], c["name"], names[c["parent"]].icon) for c in self.custom_labels if c["parent"] in names]
+            + ([(FACE_UNKNOWN_KEY, "Unknown person", "mdi:account-question")] if self.faces_on else [])
             if self.is_objects
             else []
         )
@@ -105,13 +118,26 @@ def from_row(cls, row: Sensor) -> SensorConfig:
             triggers=merge_triggers(row.triggers),
             review=dict(row.review or {}),
             kind=row.kind or KIND_STATES,
-            objects=merge_objects(row.objects),
+            objects=with_people(merge_objects(row.objects), object_session(row)),
             reading=merge_reading(row.reading),
             entity_prefix=bool(row.entity_prefix),
             publish=bool(row.publish),
         )
 
 
+def with_people(objects: dict, session: Session | None) -> dict:
+    """An object sensor's settings with the people (shared by every sensor, db.Person) among its
+    own labels while it recognises faces: {key, name, parent "person", face True}. Each one is an
+    entity of the sensor and a name its faces can get."""
+    if session is None or not faces_on(objects):
+        return objects
+    people = [
+        {"key": p.key, "name": p.name, "parent": "person", "face": True}
+        for p in session.scalars(select(Person).order_by(Person.created_at))
+    ]
+    return {**objects, "custom": [*(c for c in objects["custom"] if not c.get("face")), *people]}
+
+
 class Debouncer:
     """Only changes the published value after ``required`` consecutive equal results."""
 
@@ -177,6 +203,12 @@ class LiveState:
     filtered_logged: dict[str, float] = field(default_factory=dict)
     recheck_at: float = 0.0  # an object that was there went missing: check again then (0: no)
     asked: dict[str, float] = field(default_factory=dict)  # own label -> when the review last asked
+    # Faces: what the face search gave for the last person boxes it looked at ("ok" or the
+    # problem: "none", "cut", "small", "turned", "blurry", "unsure"), for "a usable face in 9 of 40".
+    face_stats: deque = field(default_factory=lambda: deque(maxlen=FACES["stats_window"]))
+    face_frame: tuple[int, int] | None = None  # the size of the last frame looked at for faces
+    face_min_px: float | None = None  # the smallest face (its width) it was looked at with
+    face_logged: tuple[float, float] = (0.0, 0.0)  # when a face nobody is named for was last kept, and its width
     # Reading sensors: the last read {"text", "score", "value", "reason", "at"} and the image
     # the reader saw (JPEG), shown in the UI so a bad region or display setting is easy to spot.
     reading: dict | None = None
diff --git a/visionstate/backend/visionstate/faces.json b/visionstate/backend/visionstate/faces.json
new file mode 100644
index 0000000..a398793
--- /dev/null
+++ b/visionstate/backend/visionstate/faces.json
@@ -0,0 +1,62 @@
+{
+  "detector": "yunet-2023mar",
+  "detectors": {
+    "yunet-2023mar": {
+      "name": "YuNet (OpenCV Zoo)",
+      "description": "Finds faces and five points on them (eyes, nose, mouth corners) in the upper part of a person box.",
+      "url": "https://huggingface.co/opencv/face_detection_yunet/resolve/3cc26e7f1014a5ee5d74a42acee58bafc9d0a310/face_detection_yunet_2023mar.onnx",
+      "sha256": "8f2383e4dd3cfbb4553ea8718107fc0423210dc964f9f4280604804ed2552fa4",
+      "size": 232589,
+      "input_size": 640,
+      "license": "MIT",
+      "data": "Trained on WIDER FACE, a research data set (non-commercial terms).",
+      "source": "https://huggingface.co/opencv/face_detection_yunet",
+      "bundled": false
+    }
+  },
+  "default": "sface-2021dec",
+  "recognisers": {
+    "sface-2021dec": {
+      "name": "SFace (fast)",
+      "description": "Small and fast; made to work with YuNet. Recommended, also on a Raspberry Pi.",
+      "url": "https://huggingface.co/opencv/face_recognition_sface/resolve/3d7082438a6e4551e840c9b2bb60b71e8da4b524/face_recognition_sface_2021dec.onnx",
+      "sha256": "0ba9fbfa01b5270c96627c4ef784da859931e02f04419c829e83484087c34e79",
+      "size": 38696353,
+      "input_size": 112,
+      "mean": 0.0,
+      "std": 1.0,
+      "match_similarity": 0.5,
+      "sure_similarity": 0.6,
+      "ask_similarity": 0.38,
+      "keep_similarity": 0.4,
+      "license": "Apache-2.0",
+      "data": "The model's paper trained it on CASIA-WebFace, VGGFace2 and MS-Celeb-1M, research data sets (non-commercial terms).",
+      "source": "https://huggingface.co/opencv/face_recognition_sface",
+      "bundled": false,
+      "ram_mb": 60,
+      "time_ms": 20,
+      "accuracy": 3
+    },
+    "auraface-v1": {
+      "name": "AuraFace v1 (accurate)",
+      "description": "A large model: tells faces apart better in poor light and at an angle, but needs about 600 MB of memory and is slow on small CPUs. For a fast PC.",
+      "url": "https://huggingface.co/fal/AuraFace-v1/resolve/af6d057c9b0ec4071d4c49c80e3539258798b609/glintr100.onnx",
+      "sha256": "a7933ea5330113b01c9b60351d8f4c33003f145d8470ac5f0e52ee2effe25c60",
+      "size": 260694151,
+      "input_size": 112,
+      "mean": 127.5,
+      "std": 127.5,
+      "match_similarity": 0.4,
+      "sure_similarity": 0.5,
+      "ask_similarity": 0.28,
+      "keep_similarity": 0.3,
+      "license": "Apache-2.0",
+      "data": "Trained on a commercial data set that its makers say allows commercial use.",
+      "source": "https://huggingface.co/fal/AuraFace-v1",
+      "bundled": false,
+      "ram_mb": 600,
+      "time_ms": 60,
+      "accuracy": 5
+    }
+  }
+}
diff --git a/visionstate/backend/visionstate/faces.py b/visionstate/backend/visionstate/faces.py
new file mode 100644
index 0000000..d3d5991
--- /dev/null
+++ b/visionstate/backend/visionstate/faces.py
@@ -0,0 +1,527 @@
+"""Faces in person boxes: find them, check that they are usable, align and embed them, and tell
+who they are (object sensors with face recognition on; docs/FACE_RECOGNITION_PLAN.md).
+
+The face models are declared in ``faces.json``: one face detector (YuNet: a face box and five
+points — the eyes, the nose tip and the mouth corners) and the recognisers the user can choose
+from (a face embedding from a face aligned on those five points). None of them is bundled: they
+are downloaded when a sensor first turns face recognition on. Entries are never changed or
+removed once released (their texts and footprint may be).
+
+A face is only looked for in the upper part of a person box the object detector found, cut from
+the full frame. A face that fails the quality gate (too small, blurry, turned away, unsure) is
+*no face*, never someone unknown. A usable face is compared with the faces the user taught for
+each person; see ``identify``.
+"""
+
+from __future__ import annotations
+
+import json
+import math
+import threading
+from collections.abc import Sequence
+from dataclasses import dataclass, field
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+from .backbones import Footprint, cpu_session
+from .settings import FACE_DEFAULTS, FACE_TUNABLE, FACES
+
+REGISTRY_FILE = Path(__file__).with_name("faces.json")
+
+# Where the five points of a face go in a 112 × 112 aligned face (the ArcFace template, which
+# SFace uses too): right eye, left eye (as seen in the picture: left, right), nose tip, mouth corners.
+TEMPLATE_112 = np.array(
+    [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], [41.5493, 92.3655], [70.7299, 92.2041]],
+    dtype=np.float64,
+)
+
+
+@dataclass(frozen=True)
+class FaceDetectorSpec:
+    id: str
+    name: str
+    description: str
+    url: str
+    sha256: str
+    size: int
+    input_size: int
+    license: str
+    data: str
+    source: str
+    bundled: bool = False
+
+    @property
+    def filename(self) -> str:
+        return f"{self.id}.onnx"
+
+
+@dataclass(frozen=True)
+class RecogniserSpec:
+    id: str
+    name: str
+    description: str
+    url: str
+    sha256: str
+    size: int
+    input_size: int
+    mean: float  # the aligned face (RGB, 0-255) is fed as (pixel - mean) / std
+    std: float
+    # Cosine similarities of this model (they mean nothing for another one): a match needs
+    # ``match_similarity`` (and a margin over the next person), a name is published at once from
+    # ``sure_similarity`` (else after a second check agrees), between ``ask_similarity`` and a
+    # match the sensor is unsure and may ask, and a person who stays keeps their name down to
+    # ``keep_similarity``.
+    match_similarity: float
+    sure_similarity: float
+    ask_similarity: float
+    keep_similarity: float
+    license: str
+    data: str  # what it was trained on, as its makers say
+    source: str
+    bundled: bool = False
+    footprint: Footprint = field(default_factory=lambda: Footprint())
+
+    @property
+    def filename(self) -> str:
+        return f"{self.id}.onnx"
+
+    @property
+    def thresholds(self) -> dict[str, float]:
+        return {
+            "match_similarity": self.match_similarity,
+            "sure_similarity": self.sure_similarity,
+            "ask_similarity": self.ask_similarity,
+            "keep_similarity": self.keep_similarity,
+        }
+
+
+def load_registry() -> tuple[FaceDetectorSpec, str, dict[str, RecogniserSpec]]:
+    """The face detector, the default recogniser's id and every recogniser in ``faces.json``."""
+    raw = json.loads(REGISTRY_FILE.read_text(encoding="utf-8"))
+    d = raw["detectors"][raw["detector"]]
+    detector = FaceDetectorSpec(
+        id=raw["detector"],
+        name=d["name"],
+        description=d.get("description", ""),
+        url=d["url"],
+        sha256=d["sha256"],
+        size=int(d.get("size", 0)),
+        input_size=int(d["input_size"]),
+        license=d.get("license", ""),
+        data=d.get("data", ""),
+        source=d.get("source", ""),
+        bundled=bool(d.get("bundled", False)),
+    )
+    recognisers = {
+        key: RecogniserSpec(
+            id=key,
+            name=v["name"],
+            description=v.get("description", ""),
+            url=v["url"],
+            sha256=v["sha256"],
+            size=int(v.get("size", 0)),
+            input_size=int(v["input_size"]),
+            mean=float(v["mean"]),
+            std=float(v["std"]),
+            match_similarity=float(v["match_similarity"]),
+            sure_similarity=float(v["sure_similarity"]),
+            ask_similarity=float(v["ask_similarity"]),
+            keep_similarity=float(v["keep_similarity"]),
+            license=v.get("license", ""),
+            data=v.get("data", ""),
+            source=v.get("source", ""),
+            bundled=bool(v.get("bundled", False)),
+            footprint=Footprint.of(v),
+        )
+        for key, v in raw["recognisers"].items()
+    }
+    return detector, raw["default"], recognisers
+
+
+DETECTOR, DEFAULT_RECOGNISER, RECOGNISERS = load_registry()
+
+
+def tunable_defaults(model_id: str) -> dict[str, float]:
+    """The tunable face limits (settings.FACE_TUNABLE) as they are by default with a face model."""
+    return {"min_face_px": FACE_DEFAULTS["min_face_px"], **recogniser_spec(model_id).thresholds}
+
+
+def merge_limits(model_id: str, global_limits: dict | None, sensor: dict | None) -> dict[str, float]:
+    """Effective face limits: the model's defaults < the global values < the sensor's own."""
+    merged = tunable_defaults(model_id)
+    for overrides in (global_limits, sensor):
+        merged.update({k: float(v) for k, v in (overrides or {}).items() if k in FACE_TUNABLE and v is not None})
+    return merged
+
+
+def recogniser_spec(model_id: str) -> RecogniserSpec:
+    return RECOGNISERS.get(model_id) or RECOGNISERS[DEFAULT_RECOGNISER]
+
+
+# --- Finding faces -----------------------------------------------------------------------------
+
+
+@dataclass(frozen=True)
+class Face:
+    """A face found in a picture, in that picture's pixels."""
+
+    box: tuple[float, float, float, float]  # x1, y1, x2, y2
+    points: tuple[tuple[float, float], ...]  # the five points (see TEMPLATE_112)
+    score: float
+
+    @property
+    def width(self) -> float:
+        return self.box[2] - self.box[0]
+
+    @property
+    def eye_distance(self) -> float:
+        (ax, ay), (bx, by) = self.points[0], self.points[1]
+        return math.hypot(bx - ax, by - ay)
+
+    def moved(self, dx: float, dy: float, scale: float) -> Face:
+        """The same face in another picture, whose pixels are ``x * scale + dx`` (and y alike)."""
+        x1, y1, x2, y2 = self.box
+        return Face(
+            (x1 * scale + dx, y1 * scale + dy, x2 * scale + dx, y2 * scale + dy),
+            tuple((x * scale + dx, y * scale + dy) for x, y in self.points),
+            self.score,
+        )
+
+
+def head_region(box: Sequence[float], width: int, height: int) -> tuple[int, int, int, int]:
+    """Where a face is looked for in a person box (normalised): its upper part, in frame pixels.
+
+    A standing person's box is about three times as tall as wide; the head is in the top of it.
+    A box of only head and shoulders (a doorbell) is about as tall as wide and is taken whole.
+    """
+    x1, y1, x2, y2 = (box[0] * width, box[1] * height, box[2] * width, box[3] * height)
+    bw, bh = x2 - x1, y2 - y1
+    margin = bw * FACES["region_margin"]
+    top = y1 - margin
+    bottom = y1 + min(bh, bw * FACES["region_height"]) + margin
+    return (
+        max(0, int(x1 - margin)),
+        max(0, int(top)),
+        min(width, int(math.ceil(x2 + margin))),
+        min(height, int(math.ceil(bottom))),
+    )
+
+
+def _nms(boxes: np.ndarray, scores: np.ndarray, iou: float) -> list[int]:
+    order = list(np.argsort(-scores))
+    keep: list[int] = []
+    while order:
+        best, order = order[0], order[1:]
+        keep.append(int(best))
+        if not order:
+            break
+        rest = boxes[order]
+        x1 = np.maximum(boxes[best, 0], rest[:, 0])
+        y1 = np.maximum(boxes[best, 1], rest[:, 1])
+        x2 = np.minimum(boxes[best, 2], rest[:, 2])
+        y2 = np.minimum(boxes[best, 3], rest[:, 3])
+        inter = np.clip(x2 - x1, 0, None) * np.clip(y2 - y1, 0, None)
+        area = lambda b: (b[..., 2] - b[..., 0]) * (b[..., 3] - b[..., 1])  # noqa: E731
+        overlap = inter / np.maximum(area(boxes[best]) + area(rest) - inter, 1e-9)
+        order = [o for o, v in zip(order, overlap, strict=True) if v <= iou]
+    return keep
+
+
+def decode_yunet(outputs: Sequence[np.ndarray], size: int, min_score: float) -> list[Face]:
+    """Faces from YuNet's outputs (cls, obj, bbox, kps for strides 8, 16, 32), in input pixels.
+
+    The same decoding as OpenCV's ``FaceDetectorYN``: the score is sqrt(cls × obj), boxes and
+    points are offsets from each cell of the stride's grid.
+    """
+    strides = (8, 16, 32)
+    boxes, scores, points = [], [], []
+    for i, stride in enumerate(strides):
+        cls, obj = outputs[i][0, :, 0], outputs[i + 3][0, :, 0]
+        bbox, kps = outputs[i + 6][0], outputs[i + 9][0]
+        score = np.sqrt(np.clip(cls, 0, 1) * np.clip(obj, 0, 1))
+        idx = np.nonzero(score >= min_score)[0]
+        if not len(idx):
+            continue
+        cols = size // stride
+        r, c = (idx // cols).astype(np.float32), (idx % cols).astype(np.float32)
+        cx, cy = (c + bbox[idx, 0]) * stride, (r + bbox[idx, 1]) * stride
+        w, h = np.exp(bbox[idx, 2]) * stride, np.exp(bbox[idx, 3]) * stride
+        boxes.append(np.stack([cx - w / 2, cy - h / 2, cx + w / 2, cy + h / 2], axis=1))
+        scores.append(score[idx])
+        pts = kps[idx].reshape(-1, 5, 2)
+        points.append(np.stack([(pts[:, :, 0] + c[:, None]) * stride, (pts[:, :, 1] + r[:, None]) * stride], axis=2))
+    if not boxes:
+        return []
+    all_boxes, all_scores, all_points = np.concatenate(boxes), np.concatenate(scores), np.concatenate(points)
+    keep = _nms(all_boxes, all_scores, FACES["nms_iou"])
+    return [
+        Face(
+            tuple(float(v) for v in all_boxes[k]),  # type: ignore[arg-type]
+            tuple((float(x), float(y)) for x, y in all_points[k]),
+            float(all_scores[k]),
+        )
+        for k in keep
+    ]
+
+
+class FaceDetector:
+    """YuNet with a fixed square input: up to four head regions are looked at in one run, each
+    scaled into a quarter of the input (YuNet finds faces of about 10 to 300 pixels)."""
+
+    def __init__(self, spec: FaceDetectorSpec, model_path: Path):
+        self.spec = spec
+        self.session = cpu_session(model_path)
+        self.input_name = self.session.get_inputs()[0].name
+        self._lock = threading.Lock()
+
+    @property
+    def cell(self) -> int:
+        return self.spec.input_size // 2
+
+    def detect(self, regions: list[Image.Image]) -> list[Face | None]:
+        """The best face in each region (None: none found), in that region's pixels."""
+        result: list[Face | None] = []
+        for start in range(0, len(regions), 4):
+            result.extend(self._detect_four(regions[start : start + 4]))
+        return result
+
+    def _detect_four(self, regions: list[Image.Image]) -> list[Face | None]:
+        size, cell = self.spec.input_size, self.cell
+        canvas = np.zeros((size, size, 3), dtype=np.float32)
+        placed = []
+        for i, region in enumerate(regions):
+            scale = cell / max(region.width, region.height)
+            w, h = max(1, round(region.width * scale)), max(1, round(region.height * scale))
+            ox, oy = (i % 2) * cell, (i // 2) * cell
+            canvas[oy : oy + h, ox : ox + w] = np.asarray(
+                region.convert("RGB").resize((w, h), Image.Resampling.BILINEAR), dtype=np.float32
+            )
+            placed.append((ox, oy, w, h, scale))
+        batch = canvas[:, :, ::-1].transpose(2, 0, 1)[None].copy()  # YuNet takes BGR, 0-255
+        with self._lock:
+            outputs = [np.asarray(o) for o in self.session.run(None, {self.input_name: batch})]
+        found = decode_yunet(outputs, size, FACES["detect_score"])
+        best: list[Face | None] = []
+        for ox, oy, w, h, scale in placed:
+            inside = [
+                f
+                for f in found
+                if ox <= (f.box[0] + f.box[2]) / 2 < ox + w and oy <= (f.box[1] + f.box[3]) / 2 < oy + h
+            ]
+            face = max(inside, key=lambda f: f.score, default=None)
+            best.append(face.moved(-ox / scale, -oy / scale, 1 / scale) if face else None)
+        return best
+
+
+# --- Quality, alignment ------------------------------------------------------------------------
+
+
+def similarity_transform(src: np.ndarray, dst: np.ndarray) -> np.ndarray:
+    """The 2 × 3 rotation, scale and shift that best maps points ``src`` onto ``dst`` (Umeyama)."""
+    n = len(src)
+    mu_s, mu_d = src.mean(axis=0), dst.mean(axis=0)
+    s_c, d_c = src - mu_s, dst - mu_d
+    cov = d_c.T @ s_c / n
+    u, sv, vt = np.linalg.svd(cov)
+    d = np.eye(2)
+    if np.linalg.det(u) * np.linalg.det(vt) < 0:
+        d[1, 1] = -1
+    rotation = u @ d @ vt
+    scale = float(np.trace(np.diag(sv) @ d) / ((s_c**2).sum() / n))
+    shift = mu_d - scale * rotation @ mu_s
+    return np.hstack([scale * rotation, shift[:, None]])
+
+
+def align(image: Image.Image, face: Face, size: int = 112) -> Image.Image:
+    """The face turned and scaled so that its five points sit on the template (``size`` square)."""
+    src = np.array(face.points, dtype=np.float64)
+    m = similarity_transform(src, TEMPLATE_112 * (size / 112))
+    a, t = m[:, :2], m[:, 2]
+    inv = np.linalg.inv(a)
+    back = -inv @ t
+    data = (inv[0, 0], inv[0, 1], back[0], inv[1, 0], inv[1, 1], back[1])
+    return image.convert("RGB").transform((size, size), Image.Transform.AFFINE, data, Image.Resampling.BILINEAR)
+
+
+def sharpness(aligned: Image.Image) -> float:
+    """Variance of the Laplacian of an aligned face (grey): low = blurry."""
+    g = np.asarray(aligned.convert("L"), dtype=np.float32)
+    lap = 4 * g[1:-1, 1:-1] - g[:-2, 1:-1] - g[2:, 1:-1] - g[1:-1, :-2] - g[1:-1, 2:]
+    return float(lap.var())
+
+
+def turn(face: Face) -> float:
+    """How far the face is turned sideways: the nose's offset from between the eyes, along the
+    line of the eyes, in eye distances (0 = straight at the camera, about 0.35 = 30 degrees, 0.6 = 45)."""
+    (ax, ay), (bx, by), (nx, ny) = face.points[0], face.points[1], face.points[2]
+    dist = math.hypot(bx - ax, by - ay)
+    if dist <= 0:
+        return 1.0
+    ux, uy = (bx - ax) / dist, (by - ay) / dist
+    mx, my = (ax + bx) / 2, (ay + by) / 2
+    return abs((nx - mx) * ux + (ny - my) * uy) / dist
+
+
+def cut_off(face: Face, frame: tuple[int, int]) -> bool:
+    """Whether the frame's edge cuts the face: its box reaches the edge or a point is outside.
+    The face detector then guesses where the eyes are, and the face looks like nobody."""
+    w, h = frame
+    x1, y1, x2, y2 = face.box
+    inside = all(0 <= x < w and 0 <= y < h for x, y in face.points)
+    return not inside or x1 <= 0 or y1 <= 0 or x2 >= w - 1 or y2 >= h - 1
+
+
+def problem(face: Face, aligned: Image.Image, min_face_px: float, frame: tuple[int, int]) -> str | None:
+    """Why a face cannot be used ("cut", "small", "turned", "blurry", "unsure"), or None.
+
+    ``face`` in the pixels of the frame of size ``frame``; ``min_face_px`` is the sensor's
+    smallest face (its width)."""
+    if cut_off(face, frame):
+        return "cut"
+    if face.width < min_face_px:
+        return "small"
+    if face.score < FACES["min_score"]:
+        return "unsure"
+    if turn(face) > FACES["max_turn"] or face.eye_distance < face.width * FACES["turned_eye_share"]:
+        return "turned"
+    if sharpness(aligned) < FACES["min_sharpness"]:
+        return "blurry"
+    return None
+
+
+def clearly_seen(face: Face, min_face_px: float) -> bool:
+    """Whether a usable face is seen well enough that matching nobody makes it a stranger: big
+    enough, nearly straight at the camera (a turned face may just not look like its photos) and
+    surely a face."""
+    return (
+        face.width >= min_face_px * FACES["unknown_size"]
+        and turn(face) <= FACES["unknown_max_turn"]
+        and face.score >= FACES["unknown_min_score"]
+    )
+
+
+# --- Telling who it is ------------------------------------------------------------------------
+
+
+class Recogniser:
+    """Turns aligned faces into L2-normalised embeddings with one ONNX recogniser."""
+
+    def __init__(self, spec: RecogniserSpec, model_path: Path):
+        self.spec = spec
+        self.session = cpu_session(model_path)
+        self.input_name = self.session.get_inputs()[0].name
+        self._lock = threading.Lock()
+
+    def embed(self, faces: list[Image.Image]) -> np.ndarray:
+        """One embedding per aligned face; one at a time (some models take a batch of one only)."""
+        vectors = []
+        for face in faces:
+            arr = np.asarray(face.convert("RGB"), dtype=np.float32)
+            arr = ((arr - self.spec.mean) / self.spec.std).transpose(2, 0, 1)[None]
+            with self._lock:
+                out = np.asarray(self.session.run(None, {self.input_name: arr})[0])[0]
+            vectors.append(out / max(float(np.linalg.norm(out)), 1e-8))
+        return np.stack(vectors).astype(np.float32) if vectors else np.zeros((0, 0), dtype=np.float32)
+
+
+@dataclass
+class FaceModel:
+    """The face detector and the chosen recogniser, loaded together."""
+
+    detector: FaceDetector
+    recogniser: Recogniser
+
+    @property
+    def id(self) -> str:
+        return self.recogniser.spec.id
+
+
+@dataclass
+class FaceIndex:
+    """The people's taught faces (shared by every sensor), ready to compare with."""
+
+    model: str
+    ids: list[int]
+    labels: list[str]  # the person (their key) of each face
+    vectors: np.ndarray
+    generation: int = 0
+
+
+@dataclass(frozen=True)
+class Identity:
+    """Who a face is: ``label`` is the closest person (None: nobody is taught), ``similarity`` its
+    score, ``margin`` over the next person (1 when there is only one)."""
+
+    label: str | None
+    similarity: float
+    margin: float
+    example_id: int | None
+
+
+def scores(index: FaceIndex, vector: np.ndarray) -> list[tuple[str, float, int]]:
+    """(person, score, closest face's id) for each taught person, best first.
+
+    A person's score is the mean similarity to their ``FACES["top_k"]`` closest faces, not the
+    single closest: one face taught under the wrong name does not then match everything like it.
+    """
+    if not index.ids:
+        return []
+    sims = index.vectors @ vector
+    per: dict[str, list[tuple[float, int]]] = {}
+    for sim, label, example in zip(sims, index.labels, index.ids, strict=True):
+        per.setdefault(label, []).append((float(sim), example))
+    ranked = []
+    for label, values in per.items():
+        values.sort(key=lambda v: -v[0])
+        top = values[: FACES["top_k"]]
+        ranked.append((label, sum(v for v, _ in top) / len(top), top[0][1]))
+    ranked.sort(key=lambda r: -r[1])
+    return ranked
+
+
+def identify(index: FaceIndex, vector: np.ndarray) -> Identity:
+    ranked = scores(index, vector)
+    if not ranked:
+        return Identity(None, 0.0, 0.0, None)
+    label, score, example = ranked[0]
+    margin = score - ranked[1][1] if len(ranked) > 1 else 1.0
+    return Identity(label, round(score, 3), round(margin, 3), example)
+
+
+def decide(identity: Identity, limits: dict[str, float], kept: str | None = None) -> str:
+    """What a usable face is: "match" (it is ``identity.label``), "ask" (maybe, not sure) or
+    "unknown" (nobody taught). ``kept``: the name this person box had in the last check; it keeps
+    it while the face stays close enough (``keep_similarity``)."""
+    if identity.label is None:
+        return "unknown"
+    if kept is not None and identity.label == kept and identity.similarity >= limits["keep_similarity"]:
+        return "match"
+    if identity.similarity >= limits["match_similarity"] and identity.margin >= FACES["margin"]:
+        return "match"
+    if identity.similarity >= limits["ask_similarity"]:
+        return "ask"
+    return "unknown"
+
+
+def face_crop(image: Image.Image, face: Face) -> tuple[Image.Image, Face]:
+    """What is stored of a taught face: the face with a margin (not aligned, so another model can
+    align it its own way later), and the face in that crop's pixels."""
+    x1, y1, x2, y2 = face.box
+    m = max(x2 - x1, y2 - y1) * FACES["crop_margin"]
+    left, top = max(0, int(x1 - m)), max(0, int(y1 - m))
+    right, bottom = min(image.width, int(math.ceil(x2 + m))), min(image.height, int(math.ceil(y2 + m)))
+    return image.crop((left, top, right, bottom)).convert("RGB"), face.moved(-left, -top, 1.0)
+
+
+def face_from_dict(data: dict) -> Face:
+    return Face(tuple(data["box"]), tuple(tuple(p) for p in data["points"]), float(data.get("score", 1.0)))  # type: ignore[arg-type]
+
+
+def face_to_dict(face: Face) -> dict:
+    return {
+        "box": [round(v, 1) for v in face.box],
+        "points": [[round(x, 1), round(y, 1)] for x, y in face.points],
+        "score": round(face.score, 3),
+    }
diff --git a/visionstate/backend/visionstate/main.py b/visionstate/backend/visionstate/main.py
index e14fefd..ac53aa3 100644
--- a/visionstate/backend/visionstate/main.py
+++ b/visionstate/backend/visionstate/main.py
@@ -9,7 +9,7 @@
 from fastapi.responses import FileResponse, JSONResponse
 from fastapi.staticfiles import StaticFiles
 
-from .api import cameras, history, imports, review, samples, sensors, system, teach
+from .api import cameras, faces, history, imports, review, samples, sensors, system, teach
 from .db import Database
 from .engine import Runtime
 from .settings import INGRESS_PROXY_IP, VERSION, Settings, load_settings
@@ -52,6 +52,8 @@ async def ingress_only(request: Request, call_next):
     app.include_router(sensors.router)
     app.include_router(samples.router)
     app.include_router(teach.router)
+    app.include_router(faces.router)
+    app.include_router(faces.people_router)
 
     frontend = settings.frontend_dir.resolve()
     index = frontend / "index.html"
diff --git a/visionstate/backend/visionstate/mqtt.py b/visionstate/backend/visionstate/mqtt.py
index a7f1c3e..e4caada 100644
--- a/visionstate/backend/visionstate/mqtt.py
+++ b/visionstate/backend/visionstate/mqtt.py
@@ -365,15 +365,49 @@ def main_entities(sensor: SensorDescriptor) -> list[tuple[str, str]]:
 }
 
 
-def hub_discovery_messages(prefix: str) -> list[tuple[str, dict]]:
-    """App-wide entities (not tied to one sensor), grouped under a "VisionState" device."""
-    device = {
+def _hub_device() -> dict:
+    return {
         "identifiers": [APP_SLUG],
         "name": "VisionState",
         "manufacturer": "VisionState",
         "model": "VisionState app",
         "sw_version": VERSION,
     }
+
+
+def person_topics(key: str) -> dict[str, str]:
+    """Topics of a person's own entity: on which sensor they are seen (never renamed once released)."""
+    base = f"{APP_SLUG}/people/{key}"
+    return {"state": f"{base}/state", "attributes": f"{base}/attributes"}
+
+
+def person_discovery_messages(prefix: str, people: list[tuple[str, str]]) -> list[tuple[str, dict]]:
+    """Each person's own entity on the "VisionState" device: Home Assistant names it
+    sensor.visionstate_<key>; its value is the slug of the sensor that sees them, or not_seen."""
+    messages = []
+    for key, name in people:
+        t = person_topics(key)
+        messages.append(
+            (
+                f"{prefix}/sensor/{APP_SLUG}/person_{key}/config",
+                {
+                    "unique_id": f"{APP_SLUG}_person_{key}",
+                    "default_entity_id": f"sensor.{APP_SLUG}_{key}",
+                    "name": name,
+                    "state_topic": t["state"],
+                    "json_attributes_topic": t["attributes"],
+                    "icon": "mdi:account-eye",
+                    "availability": [{"topic": BRIDGE_AVAILABILITY}],
+                    "device": _hub_device(),
+                },
+            )
+        )
+    return messages
+
+
+def hub_discovery_messages(prefix: str) -> list[tuple[str, dict]]:
+    """App-wide entities (not tied to one sensor), grouped under a "VisionState" device."""
+    device = _hub_device()
     return [
         (
             f"{prefix}/sensor/{APP_SLUG}/review_queue/config",
@@ -531,6 +565,16 @@ async def publish_hub_discovery(self) -> None:
         for topic, payload in hub_discovery_messages(self.settings.discovery_prefix):
             await self.publish(topic, payload, retain=True)
 
+    async def publish_people_discovery(self, people: list[tuple[str, str]]) -> None:
+        for topic, payload in person_discovery_messages(self.settings.discovery_prefix, people):
+            await self.publish(topic, payload, retain=True)
+
+    async def remove_person(self, key: str) -> None:
+        """Forget a person's own entity and its retained values."""
+        await self.publish(f"{self.settings.discovery_prefix}/sensor/{APP_SLUG}/person_{key}/config", "", retain=True)
+        for topic in person_topics(key).values():
+            await self.publish(topic, "", retain=True)
+
     async def remove_discovery(self, sensor: SensorDescriptor) -> None:
         for topic, _ in discovery_messages(self.settings.discovery_prefix, sensor):
             await self.publish(topic, "", retain=True)
diff --git a/visionstate/backend/visionstate/settings.py b/visionstate/backend/visionstate/settings.py
index 96c21ec..e4f36b7 100644
--- a/visionstate/backend/visionstate/settings.py
+++ b/visionstate/backend/visionstate/settings.py
@@ -55,6 +55,7 @@
     # "Our car" under "car". Each gets the same two entities as a class. Made by teaching a box.
     "custom": [],
     "teach": {},  # this sensor's own values of the tunable teaching limits (TEACH_TUNABLE); missing = global
+    "faces": {},  # recognising people by their face: see FACE_DEFAULTS (missing = default)
 }
 OBJECT_LIMITS = {
     "min_size": (0.0, 0.5),
@@ -218,6 +219,80 @@
     "dinov2-base": {"match_similarity": 0.9, "rescue_similarity": 0.92},
 }
 
+# Recognising people by their face (object sensors that look for "person"; off by default, see
+# docs/FACE_RECOGNITION_PLAN.md). The faces of each person box are looked for in its upper part,
+# checked (a face that fails is "no face", never someone unknown), aligned and compared with the
+# faces the user taught. Similarity thresholds belong to the face model (faces.json).
+FACE_DEFAULTS = {
+    "enabled": False,
+    # Smallest usable face: the width of the face in the camera's frame, in pixels (not the
+    # distance between the eyes, which shrinks as a head turns). On a 480x640 living room camera
+    # and the test photos scaled down, SFace still told the person (0.53-0.60) from a stranger
+    # (<=0.31) at a face 28-31 px wide (about 13 px between the eyes); at 21 px it fell to 0.45.
+    "min_face_px": 30.0,
+    "ask_unknown": False,  # put faces nobody was taught for in the review queue ("Who is this?")
+}
+# What the user can tune, for all sensors (Settings) and per sensor (its Faces card): a sensor's
+# unset value follows the global one, which follows the default (min_face_px above; the
+# similarities: the face model's, faces.json).
+FACE_TUNABLE = ("min_face_px", "match_similarity", "sure_similarity", "ask_similarity", "keep_similarity")
+FACE_LIMITS = {
+    "min_face_px": (16.0, 240.0),
+    "match_similarity": (0.2, 0.95),
+    "sure_similarity": (0.2, 0.95),
+    "ask_similarity": (0.1, 0.95),
+    "keep_similarity": (0.1, 0.95),
+}
+FACES = {
+    "stats_window": 40,  # "a usable face in 9 of the last 40 person boxes looked at"
+    "max_faces": 4,  # person boxes searched for a face per check (most certain first): one detector run
+    # A person box that keeps a sure name is searched again only every this many checks.
+    "recheck_every": 3,
+    # The upper part of a person box where the face is looked for: its height, in box widths
+    # (a standing person is about 3 widths tall), and a margin on every side, in box widths.
+    "region_height": 1.3,
+    "region_margin": 0.1,
+    "detect_score": 0.6,  # a face the face detector finds at all
+    "nms_iou": 0.3,
+    # Quality gate. The face detector's score, how far the face is turned sideways (see faces.turn:
+    # 0 straight, ~0.35 about 30 degrees, ~0.6 about 45) and the sharpness of the aligned face
+    # (variance of the Laplacian at 112 px: sharp faces 32+ down to 13 px between the eyes, the
+    # same faces blurred by 2 px at most 19). A profile has its eyes close together for its width:
+    # 0.41-0.50 of it looking at the camera, a profile 0.08; below turned_eye_share it is turned.
+    "min_score": 0.75,
+    "max_turn": 0.55,
+    "min_sharpness": 25.0,
+    "turned_eye_share": 0.3,
+    # A face that matched nobody counts towards Unknown person only when it is clearly seen: this
+    # many times the smallest face, turned at most this far, and found with at least this detector
+    # score. A smaller or more turned face can be named (a person's own face still looks like them
+    # a bit turned, 0.66-0.78 at 0.26-0.35), but not matching anyone may only mean the angle: "not
+    # sure who", never a stranger. On a phone camera real faces scored 0.86-0.94 and things taken
+    # for a face (a wall, a second face on a profile with the eyes in the wrong place) 0.60-0.79.
+    "unknown_size": 1.5,
+    "unknown_max_turn": 0.35,
+    "unknown_min_score": 0.85,
+    "top_k": 3,  # a person's score: the mean similarity to their this many closest taught faces
+    "margin": 0.05,  # a match must beat the next person by this much
+    # Unknown person: a person box whose usable face matched nobody in this many checks in a row
+    # (the same box: it overlaps the last check's by keep_iou in TEACH) and that never had a name.
+    "unknown_after": 2,
+    "crop_margin": 0.5,  # what is stored of a taught face: the face box plus this share of its size around it
+    "max_faces_per_person": 50,
+    "max_people": 10,  # people VisionState knows (shared; two entities on each sensor that recognises faces, and one of their own)
+    "suggest_faces": 5,  # a person with fewer taught faces gets "teach a few more"
+    "look_alike": 0.9,  # two people whose closest faces are this share of the match threshold apart look alike
+    # A usable face nobody is named for keeps its frame in the history, to name them there later
+    # (someone at a doorbell is gone before the Live tab can be tapped): at most one per this many
+    # seconds per sensor, unless the face came this many times closer (wider) since the last one.
+    "record_cooldown_s": 60,
+    "record_closer": 1.5,
+}
+FACE_UNKNOWN_KEY = "unknown_person"  # the key (and entity) of a face nobody was taught for
+# A person's own entity (sensor.visionstate_<key>) says on which sensor they are seen: its slug,
+# the one that saw them last when several do, or this when none sees them now.
+PERSON_NOT_SEEN = "not_seen"
+
 # --- Triggers: when a sensor checks its camera ---------------------------------
 #
 # 1. The regular interval (SENSOR_DEFAULTS["interval_s"]) is the safety net.
@@ -393,7 +468,8 @@
 # kinds). api/history.py says which rows each one matches, the UI how it is named.
 HISTORY_EVENTS: dict[str, tuple[str, ...]] = {
     KIND_STATES: ("change", "flagged"),  # a published change; a frame put in the review queue
-    KIND_OBJECTS: ("appeared", "cleared", "filtered", "asked"),  # "asked": "Is this Our car?"
+    # "asked": "Is this Our car?"; "face_seen": a face nobody is named for (FACES["record_cooldown_s"]).
+    KIND_OBJECTS: ("appeared", "cleared", "filtered", "asked", "face_seen"),
     KIND_READING: ("value", "rejected", "verified"),  # verified: the user said whether it read right
 }
 
@@ -461,9 +537,24 @@ def merge_objects(stored: dict | None) -> dict:
     merged["classes"] = list(merged.get("classes") or [])
     merged["custom"] = [dict(label) for label in merged.get("custom") or []]
     merged["teach"] = {k: v for k, v in (merged.get("teach") or {}).items() if k in TEACH_TUNABLE and v is not None}
+    merged["faces"] = merge_faces(merged.get("faces"))
     return merged
 
 
+def merge_faces(stored: dict | None) -> dict:
+    """Face settings of an object sensor: whether it is on and asks (stored on top of
+    FACE_DEFAULTS), and its own values of FACE_TUNABLE (only the ones it set; see faces.merge_limits)."""
+    stored = stored or {}
+    merged = {k: stored.get(k, FACE_DEFAULTS[k]) for k in ("enabled", "ask_unknown")}
+    merged.update({k: float(stored[k]) for k in FACE_TUNABLE if stored.get(k) is not None})
+    return merged
+
+
+def faces_on(objects: dict) -> bool:
+    """Whether an object sensor recognises faces: switched on, and it looks for people."""
+    return bool(objects["faces"]["enabled"]) and "person" in objects["classes"]
+
+
 def active_custom(objects: dict) -> list[dict]:
     """The sensor's own labels that are in use: those whose class is still selected."""
     return [label for label in objects["custom"] if label["parent"] in objects["classes"]]
@@ -516,6 +607,12 @@ class Settings:
     def models_dir(self) -> Path:
         return self.data_dir / "models"
 
+    @property
+    def faces_dir(self) -> Path:
+        """Faces taught to object sensors: in the app's own storage, never in the media folder
+        (Home Assistant's media browser shows that to every user)."""
+        return self.data_dir / "faces"
+
     @property
     def heads_dir(self) -> Path:
         return self.data_dir / "heads"
@@ -545,6 +642,7 @@ def ensure_dirs(self) -> None:
             self.data_dir,
             self.models_dir,
             self.heads_dir,
+            self.faces_dir,
             self.samples_dir,
             self.history_dir,
             self.thumbs_dir,
diff --git a/visionstate/backend/visionstate/storage.py b/visionstate/backend/visionstate/storage.py
index 8f5ec07..d79ccad 100644
--- a/visionstate/backend/visionstate/storage.py
+++ b/visionstate/backend/visionstate/storage.py
@@ -18,6 +18,10 @@ def __init__(self, settings: Settings):
     def sample_path(self, sensor_id: int, filename: str) -> Path:
         return self.settings.samples_dir / str(sensor_id) / filename
 
+    def face_path(self, filename: str) -> Path:
+        """A taught face (db.Face): shared by every sensor, so not in a sensor's folder."""
+        return self.settings.faces_dir / filename
+
     def history_path(self, sensor_id: int, filename: str) -> Path:
         return self.settings.history_dir / str(sensor_id) / filename
 
@@ -37,6 +41,14 @@ def save_sample(self, sensor_id: int, image: Image.Image) -> str:
         self._write(self.sample_path(sensor_id, filename), image)
         return filename
 
+    def save_face(self, image: Image.Image) -> str:
+        filename = self.new_filename()
+        self._write(self.face_path(filename), image)
+        return filename
+
+    def delete_face(self, filename: str) -> None:
+        self.face_path(filename).unlink(missing_ok=True)
+
     def save_history(self, sensor_id: int, image: Image.Image) -> str:
         filename = self.new_filename()
         self._write(self.history_path(sensor_id, filename), image)
@@ -67,11 +79,18 @@ def history_bytes(self) -> int:
         return self._folder_bytes(self.settings.history_dir) + self._folder_bytes(self.settings.thumbs_dir / "history")
 
     def samples_bytes(self) -> int:
-        """Training images and their thumbnails on disk."""
-        return self._folder_bytes(self.settings.samples_dir) + self._folder_bytes(self.settings.thumbs_dir / "sample")
+        """Training images (and taught faces) and their thumbnails on disk."""
+        return (
+            self._folder_bytes(self.settings.samples_dir)
+            + self._folder_bytes(self.settings.faces_dir)
+            + self._folder_bytes(self.settings.thumbs_dir / "sample")
+        )
 
     def delete_sensor(self, sensor_id: int) -> None:
         import shutil
 
-        for directory in (self.settings.samples_dir / str(sensor_id), self.settings.history_dir / str(sensor_id)):
+        for directory in (
+            self.settings.samples_dir / str(sensor_id),
+            self.settings.history_dir / str(sensor_id),
+        ):
             shutil.rmtree(directory, ignore_errors=True)
diff --git a/visionstate/config.yaml b/visionstate/config.yaml
index 2825f3e..0261bba 100644
--- a/visionstate/config.yaml
+++ b/visionstate/config.yaml
@@ -1,5 +1,5 @@
 name: VisionState (beta)
-version: "0.8.1b2"
+version: "0.8.1b3"
 slug: visionstate
 description: Teach a local AI to recognise states (open, closed, …), find objects (people, cars, pets) or read numbers in your camera images
 url: https://github.com/oleost/VisionState
diff --git a/visionstate/frontend/e2e/env.ts b/visionstate/frontend/e2e/env.ts
index 9487f73..2907f75 100644
--- a/visionstate/frontend/e2e/env.ts
+++ b/visionstate/frontend/e2e/env.ts
@@ -9,6 +9,8 @@ export const SENSOR_NAME = 'Garage door';
 /** An object sensor looking at a real photo (a person with dogs, see backend/tests/assets). */
 export const OBJECT_SENSOR_NAME = 'Beach';
 export const PHOTO_URL = (name: string) => `${CAMERA_URL}/photo/${name}.jpg`;
+/** An object sensor that recognises faces, looking at two NASA astronauts (public domain); taught one of them. */
+export const FACE_SENSOR_NAME = 'Crew';
 /** A reading sensor looking at a drawn LCD counter. */
 export const READING_SENSOR_NAME = 'Power meter';
 export const DISPLAY_URL = (text: string, style: 'lcd' | 'led' = 'lcd') =>
diff --git a/visionstate/frontend/e2e/helpers.ts b/visionstate/frontend/e2e/helpers.ts
index e5afb78..eb51ab9 100644
--- a/visionstate/frontend/e2e/helpers.ts
+++ b/visionstate/frontend/e2e/helpers.ts
@@ -1,6 +1,6 @@
 // Shared helpers for the UI tests: error collection, layout checks and real touch/mouse gestures.
 import { expect, type APIRequestContext, type Locator, type Page, type TestInfo } from '@playwright/test';
-import { COUNTER_SENSOR_NAME, OBJECT_SENSOR_NAME, READING_SENSOR_NAME, SENSOR_NAME } from './env';
+import { COUNTER_SENSOR_NAME, FACE_SENSOR_NAME, OBJECT_SENSOR_NAME, READING_SENSOR_NAME, SENSOR_NAME } from './env';
 
 export type Point = { x: number; y: number };
 
@@ -113,6 +113,7 @@ export async function seededSensorId(request: APIRequestContext, name = SENSOR_N
 export const seededObjectSensorId = (request: APIRequestContext) => seededSensorId(request, OBJECT_SENSOR_NAME);
 export const seededReadingSensorId = (request: APIRequestContext) => seededSensorId(request, READING_SENSOR_NAME);
 export const seededCounterSensorId = (request: APIRequestContext) => seededSensorId(request, COUNTER_SENSOR_NAME);
+export const seededFaceSensorId = (request: APIRequestContext) => seededSensorId(request, FACE_SENSOR_NAME);
 
 /** API requests per path (without the query) made while `ms` pass on the page. */
 export async function apiRequestsDuring(page: Page, ms: number): Promise<Record<string, number>> {
diff --git a/visionstate/frontend/e2e/seed.setup.ts b/visionstate/frontend/e2e/seed.setup.ts
index b680990..f8108e9 100644
--- a/visionstate/frontend/e2e/seed.setup.ts
+++ b/visionstate/frontend/e2e/seed.setup.ts
@@ -6,6 +6,7 @@ import {
   COUNTER_SENSOR_NAME,
   COUNTER_URL,
   DISPLAY_URL,
+  FACE_SENSOR_NAME,
   OBJECT_SENSOR_NAME,
   PHOTO_URL,
   READING_SENSOR_NAME,
@@ -110,3 +111,39 @@ setup('seed a mechanical counter sensor', async ({ request }) => {
     .poll(async () => (await (await request.get(`api/v1/sensors/${sensor.id}`)).json()).reading.value, { timeout: 60_000 })
     .toBe('89.939');
 });
+
+setup('seed a sensor that recognises faces', async ({ request }) => {
+  const created = await request.post('api/v1/sensors', {
+    data: {
+      name: FACE_SENSOR_NAME,
+      kind: 'objects',
+      source_type: 'http',
+      source: PHOTO_URL('face_crew'),
+      objects: { classes: ['person'], faces: { enabled: true, ask_unknown: false } },
+      interval_s: 5,
+    },
+  });
+  expect(created.ok()).toBeTruthy();
+  const sensor = await created.json();
+  type Box = { key: string; score: number; box: number[]; face?: { box?: number[]; problem: string | null } };
+  // The face model loads in the background (it is in the bundled models folder, as in CI: no download).
+  let frameId = '';
+  let people: Box[] = [];
+  await expect
+    .poll(
+      async () => {
+        const view = await (await request.get(`api/v1/sensors/${sensor.id}`)).json();
+        frameId = view.live.frame_id;
+        people = (view.objects.detections as Box[]).filter((d) => d.key === 'person' && d.face?.box && !d.face.problem);
+        return people.length;
+      },
+      { timeout: 120_000 },
+    )
+    .toBe(2);
+  // Suni Williams stands on the left; the test teaches the other one by hand.
+  const left = people.sort((a, b) => a.box[0] - b.box[0])[0];
+  const taught = await request.post(`api/v1/sensors/${sensor.id}/faces`, {
+    data: { frame_id: frameId, box: left.box, score: left.score, new_person: 'Suni' },
+  });
+  expect(taught.ok(), await taught.text()).toBeTruthy();
+});
diff --git a/visionstate/frontend/e2e/ui.spec.ts b/visionstate/frontend/e2e/ui.spec.ts
index fdf74f4..6295691 100644
--- a/visionstate/frontend/e2e/ui.spec.ts
+++ b/visionstate/frontend/e2e/ui.spec.ts
@@ -14,6 +14,7 @@ import {
   isTouch,
   press,
   seededCounterSensorId,
+  seededFaceSensorId,
   seededObjectSensorId,
   seededReadingSensorId,
   seededSensorId,
@@ -33,6 +34,7 @@ async function allPages(request: APIRequestContext): Promise<[string, string, Re
   const objectId = await seededObjectSensorId(request);
   const readingId = await seededReadingSensorId(request);
   const counterId = await seededCounterSensorId(request);
+  const faceId = await seededFaceSensorId(request);
   return [
     ['dashboard', '', 'Sensors'],
     ['new-sensor', 'sensors/new', 'Name it and pick a camera'],
@@ -50,6 +52,10 @@ async function allPages(request: APIRequestContext): Promise<[string, string, Re
     ['reading-settings', `sensors/${readingId}/settings`, 'Digits after the decimal point'],
     ['reading-quality', `sensors/${readingId}/quality`, 'Readings per day'],
     ['counter-live', `sensors/${counterId}/live`, 'One wheel per field'],
+    ['faces-live', `sensors/${faceId}/live`, 'Unknown person'],
+    ['faces-settings', `sensors/${faceId}/settings`, 'Ask me about faces I do not know'],
+    ['faces-quality', `sensors/${faceId}/quality`, 'Shared by every camera that recognises faces'],
+    ['people', 'people', 'The people you named by their face'],
     ['counter-settings', `sensors/${counterId}/settings`, 'Number of digits'],
     ['review', 'review', 'Frames the AI was unsure about'],
     ['history-all', 'history', 'What every sensor recorded'],
@@ -58,6 +64,7 @@ async function allPages(request: APIRequestContext): Promise<[string, string, Re
 }
 
 test('every page renders without errors and fits the screen', async ({ page, request }, info) => {
+  test.setTimeout(180_000); // every page, with a full-page screenshot each
   const pages = await allPages(request);
   // Native parts of controls are drawn dark (Safari shows light select menus with light text otherwise).
   await page.goto('#/');
@@ -94,6 +101,7 @@ const expectCalm = (counts: Record<string, number>) =>
   expect.soft(Object.entries(counts).filter(([, n]) => n > MAX_PER_PATH), JSON.stringify(counts)).toEqual([]);
 
 test('no page asks the server more often than it polls', async ({ page, request }) => {
+  test.setTimeout(180_000); // every page, 4 s each
   for (const [name, route, ready] of await allPages(request)) {
     await test.step(name, async () => {
       await page.goto(`#/${route}`);
@@ -210,6 +218,7 @@ test('new sensor wizard creates a sensor', async ({ page, request }, info) => {
 
   const image = page.locator('.roi img');
   await expect(image).toBeVisible();
+  await expect(page.getByTestId('low-stream')).toHaveCount(0); // 1280 × 720: no warning
   const box = (await image.boundingBox())!;
   await drag(page, info, { x: box.x + box.width * 0.2, y: box.y + box.height * 0.2 }, { x: box.x + box.width * 0.8, y: box.y + box.height * 0.9 });
   await expect(page.locator('.handle.corner')).toHaveCount(4);
@@ -246,6 +255,9 @@ test('new object sensor through the wizard', async ({ page, request }, info) =>
   await page.getByLabel('Source URL').fill(PHOTO_URL('driveway'));
   await press(page.getByRole('button', { name: 'Next' }), info);
   await expect(page.locator('.roi img')).toBeVisible();
+  // A small picture: maybe the camera's low-resolution stream.
+  await expect(page.getByTestId('low-stream')).toContainText('640×421');
+  await expectNoClipping(page, '.notice');
   await press(page.getByRole('button', { name: 'Next' }), info); // whole frame
 
   await press(page.getByRole('radio', { name: /Objects/ }), info);
@@ -258,6 +270,17 @@ test('new object sensor through the wizard', async ({ page, request }, info) =>
   await page.getByPlaceholder(/Search \d+ objects/).fill('bicy');
   await expect(page.getByRole('button', { name: 'Bicycle' })).toHaveCount(1);
   await expect(page.getByText('binary_sensor.driveway_test_car')).toBeVisible();
+  // With Person: recognising faces can be turned on here, after the same confirmation.
+  const faces = page.getByTestId('wizard-faces').getByRole('checkbox');
+  await expect(faces).not.toBeChecked();
+  await pressCentered(faces, info);
+  const sheet = page.getByRole('dialog', { name: 'Recognise faces' });
+  await expect(sheet.getByText(/It is not a lock/)).toBeVisible();
+  await press(sheet.getByRole('button', { name: 'Cancel' }), info);
+  await expect(faces).not.toBeChecked();
+  await pressCentered(faces, info);
+  await press(sheet.getByRole('button', { name: /^(Turn on|Download and turn on)$/ }), info);
+  await expect(faces).toBeChecked();
   await expectNoHorizontalOverflow(page);
   await page.screenshot({ path: path.join('test-results', 'pages', info.project.name, 'wizard-objects.png'), fullPage: true });
   await expectNoClipping(page, '.btn, .chip-btn, .kind, .card');
@@ -268,6 +291,8 @@ test('new object sensor through the wizard', async ({ page, request }, info) =>
   await expect(page.getByText('Right now')).toBeVisible();
   await expect(page.locator('.count.on').first()).toBeVisible({ timeout: 60_000 });
   const sensorId = Number(page.url().match(/sensors\/(\d+)\//)![1]);
+  const made = await (await request.get(`api/v1/sensors/${sensorId}`)).json();
+  expect(made.objects.faces).toEqual({ enabled: true, ask_unknown: false });
   errors.expectNone();
   await page.goto('about:blank');
   await request.delete(`api/v1/sensors/${sensorId}`);
@@ -1052,15 +1077,20 @@ test('the wheel reader shows in Settings and is offered to counters read as text
   }
 });
 
-test('the AI models can be compared, and the teaching limits set for all object sensors', async ({ page, request }, info) => {
+test('the AI models can be compared, and the teaching and face limits set for all object sensors', async ({ page, request }, info) => {
   const errors = watchErrors(page);
   try {
     await page.goto('#/settings');
-    await press(page.getByRole('button', { name: 'Compare the models' }), info);
+    await press(page.getByRole('button', { name: 'Compare and manage the models' }), info);
     const compare = page.getByTestId('model-compare');
     await expect(compare.getByText('D-FINE L (most accurate)')).toBeVisible();
     await expect(compare.getByText('DINOv2 base · fp32 (most accurate)')).toBeVisible();
-    await expect(compare.getByText('in use')).toHaveCount(4); // one per kind
+    await expect(compare.getByText('AuraFace v1 (accurate)')).toBeVisible();
+    await expect(compare.getByText(/· in use$/)).toHaveCount(5); // one per kind
+    // From the smallest to the largest, in every kind.
+    const settings = await (await request.get('api/v1/settings')).json();
+    const smallest = [...settings.detectors].sort((a, b) => a.size - b.size)[0].name;
+    await expect(compare.locator('.kind').nth(1).locator('.name').first()).toContainText(smallest);
     await expectNoHorizontalOverflow(page);
     await expectNoClipping(page, '.btn, .card');
 
@@ -1084,9 +1114,24 @@ test('the AI models can be compared, and the teaching limits set for all object
     await expect(ask).toHaveValue('');
     await pressCentered(save, info);
     await expect.poll(async () => (await (await request.get('api/v1/teach-limits')).json()).values.ask_similarity).toBeNull();
+
+    // Faces: the face model's defaults, a smallest face for every sensor, nonsense refused.
+    const faces = page.getByTestId('face-limits');
+    const smallestFace = faces.getByRole('spinbutton', { name: 'Smallest face' });
+    const faceView = await (await request.get('api/v1/face-limits')).json();
+    await expect(smallestFace).toHaveAttribute('placeholder', String(faceView.defaults.min_face_px));
+    await faces.getByRole('spinbutton', { name: 'Asks you in the review queue from' }).fill('95');
+    await expect(faces.getByText(/must not be above “Recognised as a person”/)).toBeVisible();
+    await faces.getByRole('spinbutton', { name: 'Asks you in the review queue from' }).fill('');
+    await smallestFace.fill('40');
+    await pressCentered(faces.getByRole('button', { name: 'Save face limits' }), info);
+    await expect(page.getByText('Face limits saved')).toBeVisible();
+    expect((await (await request.get('api/v1/face-limits')).json()).values.min_face_px).toBe(40);
+    await expectNoHorizontalOverflow(page);
     errors.expectNone();
   } finally {
     await request.put('api/v1/teach-limits', { data: {} });
+    await request.put('api/v1/face-limits', { data: {} });
   }
 });
 
@@ -1245,3 +1290,77 @@ test('an answer in the review queue can be changed from the list', async ({ page
   await expectNoHorizontalOverflow(page);
   errors.expectNone();
 });
+
+test('faces: turned on with a confirmation, a person taught from the Live tab, shown under Quality', async ({ page, request }, info) => {
+  const errors = watchErrors(page);
+  const faceId = await seededFaceSensorId(request);
+  // Each device runs this against the same backend: start without the person it teaches.
+  const del = await request.delete('api/v1/people/butch');
+  expect([204, 404]).toContain(del.status());
+
+  // Turning it on asks first, and says what is stored and downloaded.
+  const beach = await seededObjectSensorId(request);
+  await page.goto(`#/sensors/${beach}/settings`);
+  const card = page.getByTestId('faces-card');
+  await expect(card.getByText('Recognise people by their face')).toBeVisible();
+  await pressCentered(card.getByRole('button', { name: 'Turn on…' }), info);
+  const sheet = page.getByRole('dialog', { name: 'Recognise faces' });
+  await expect(sheet.getByText(/not the media folder/)).toBeVisible();
+  await expect(sheet.getByText(/It is not a lock/)).toBeVisible();
+  await expectNoHorizontalOverflow(page);
+  await press(sheet.getByRole('button', { name: /^(Turn on|Download and turn on)$/ }), info);
+  await expect(card.getByText('Ask me about faces I do not know')).toBeVisible();
+  // Its own limits follow the global ones until set.
+  const own = card.getByTestId('sensor-face-limits').getByRole('spinbutton', { name: 'Smallest face' });
+  await expect(own).toHaveValue('');
+  const global = await (await request.get('api/v1/face-limits')).json();
+  await expect(own).toHaveAttribute('placeholder', String(global.effective.min_face_px));
+  await expectNoClipping(page, '.btn, .card');
+  await pressCentered(card.getByRole('checkbox', { name: /Recognise faces/ }), info);
+  await expect(card.getByRole('button', { name: 'Turn on…' })).toBeVisible();
+  // Its toast sits at the bottom of a phone's screen, where the teach sheet's buttons come below.
+  await expect(page.getByText('Face recognition is off')).toHaveCount(0, { timeout: 15_000 });
+
+  // The Live tab: faces drawn in the person boxes, the known one named, the other unknown.
+  await page.goto(`#/sensors/${faceId}/live`);
+  await expect(page.locator('.roi .face').first()).toBeVisible({ timeout: 30_000 });
+  await expect(page.getByText('Person · Suni').first()).toBeVisible({ timeout: 30_000 });
+  // Tap the person nobody named (an unknown person after two checks): "Who is this?" with a new person.
+  await pressCentered(page.locator('button.found', { hasText: /Unknown person|^\s*Person/ }).first(), info);
+  const teach = page.getByRole('dialog', { name: 'Teach this box' });
+  await expect(teach.getByText('Who is this?')).toBeVisible();
+  await press(teach.getByRole('button', { name: 'New person…' }), info);
+  await teach.getByPlaceholder('Name').fill('Butch');
+  await press(teach.getByRole('button', { name: 'Add person' }), info);
+  await expect(page.getByText(/Got it: this is Butch \(1 face\)/)).toBeVisible();
+  await expect(page.getByText('Person · Butch').first()).toBeVisible({ timeout: 30_000 });
+
+  // Quality: one card per person, with their faces.
+  await page.goto(`#/sensors/${faceId}/quality`);
+  const people = page.getByTestId('people');
+  await expect(people).toBeVisible();
+  await expect(page.getByRole('heading', { name: /^Butch/ })).toBeVisible();
+  await expect(page.getByRole('img', { name: 'A face of Butch' })).toBeVisible();
+  await expectNoHorizontalOverflow(page);
+  await expectNoClipping(page, '.btn, .butch');
+
+  // People: shared by every camera, renamed there (the entity IDs stay).
+  await press(page.getByRole('link', { name: 'People' }), info);
+  await expect(page).toHaveURL(/#\/people$/);
+  const butch = page.locator('section.person', { has: page.getByRole('heading', { name: /^Butch/ }) });
+  await expect(butch.getByText(/Seen now on Crew|Last seen .* on Crew/)).toBeVisible({ timeout: 30_000 });
+  await expect(butch.getByText(/· Crew/).first()).toBeVisible(); // the camera each face was taught on
+  const rename = page.locator('form.rename'); // the heading becomes the name field
+  await expect(async () => {
+    await pressCentered(butch.getByRole('button', { name: 'Rename' }), info); // a tap after scrolling can be lost
+    await expect(rename).toBeVisible({ timeout: 2_000 });
+  }).toPass();
+  await rename.getByLabel('Name').fill('Butch W');
+  await press(rename.getByRole('button', { name: 'Save' }), info);
+  await expect(page.getByText('Butch is now Butch W')).toBeVisible();
+  await expect(page.getByRole('heading', { name: /^Butch W/ })).toBeVisible();
+  expect((await (await request.get('api/v1/people')).json()).people.map((p: { key: string }) => p.key)).toContain('butch');
+  await expectNoHorizontalOverflow(page);
+  await expectNoClipping(page, '.btn, .butch');
+  errors.expectNone();
+});
diff --git a/visionstate/frontend/src/App.svelte b/visionstate/frontend/src/App.svelte
index 7fe590e..b61e49f 100644
--- a/visionstate/frontend/src/App.svelte
+++ b/visionstate/frontend/src/App.svelte
@@ -8,6 +8,7 @@
   import Dashboard from './pages/Dashboard.svelte';
   import History from './pages/History.svelte';
   import NewSensor from './pages/NewSensor.svelte';
+  import People from './pages/People.svelte';
   import Review from './pages/Review.svelte';
   import SensorPage from './pages/SensorPage.svelte';
   import Settings from './pages/Settings.svelte';
@@ -39,6 +40,8 @@
     <Review />
   {:else if section === 'history'}
     <History />
+  {:else if section === 'people'}
+    <People />
   {:else if section === 'settings'}
     <Settings />
   {:else}
diff --git a/visionstate/frontend/src/lib/api.ts b/visionstate/frontend/src/lib/api.ts
index afc7a9c..be91a91 100644
--- a/visionstate/frontend/src/lib/api.ts
+++ b/visionstate/frontend/src/lib/api.ts
@@ -1,6 +1,13 @@
 // Typed client for the backend. Every URL the UI uses is built here.
 import type {
   AppConfig,
+  ObjectSettings,
+  FaceLimitsInfo,
+  FaceOverrides,
+  FaceProblem,
+  FaceStatus,
+  ModelKind,
+  People,
   Camera,
   Detection,
   HaEntity,
@@ -91,6 +98,13 @@ export interface HistoryPageOptions {
   newerThan?: string | null;
 }
 
+/** A person box on the frame a check analysed, or on a history frame. */
+export interface FaceBoxInput {
+  frame_id?: string;
+  history_id?: number;
+  box: [number, number, number, number];
+}
+
 export interface Frame {
   frameId: string | null;
   url: string; // object URL; caller revokes it
@@ -112,7 +126,7 @@ export const api = {
   config: () => request<AppConfig>('config'),
   status: () => request<Status>('status'),
   settings: () => request<SettingsInfo>('settings'),
-  saveSettings: (body: { backbone: string; detector?: string; reader?: string }) =>
+  saveSettings: (body: { backbone: string; detector?: string; reader?: string; face_model?: string }) =>
     request<SettingsInfo>('settings', send('PUT', body)),
   storage: () => request<StorageInfo>('storage'),
   saveStorage: (body: { history_days: number; history_max_gb: number }) =>
@@ -134,7 +148,11 @@ export const api = {
   sensors: () => request<Sensor[]>('sensors'),
   sensor: (id: number) => request<Sensor>(`sensors/${id}`),
   createSensor: (body: SensorInput) => request<Sensor>('sensors', send('POST', body)),
-  updateSensor: (id: number, body: Partial<SensorInput> & { clear_roi?: boolean }) =>
+  /** `objects` is merged into what the sensor has: only the fields sent change. */
+  updateSensor: (
+    id: number,
+    body: Partial<Omit<SensorInput, 'objects'>> & { objects?: Partial<ObjectSettings>; clear_roi?: boolean },
+  ) =>
     request<Sensor>(`sensors/${id}`, send('PATCH', body)),
   deleteSensor: (id: number) => request<void>(`sensors/${id}`, send('DELETE')),
   classify: (id: number) => request(`sensors/${id}/classify`, send('POST')),
@@ -202,7 +220,7 @@ export const api = {
   historyFacets: (filter: HistoryFilter) => request<HistoryFacets>(`history/facets?${listQs({ ...filter })}`),
   historyImageUrl: (predictionId: number, size: 'thumb' | 'full' = 'full') =>
     `${BASE}history/${predictionId}/image?size=${size}`,
-  exportUrl: (id: number) => `${BASE}sensors/${id}/export`,
+  exportUrl: (id: number, faces = false) => `${BASE}sensors/${id}/export${faces ? '?faces=true' : ''}`,
 
   teachLimits: () => request<TeachLimitsInfo>('teach-limits'),
   saveTeachLimits: (values: TeachOverrides) => request<TeachLimitsInfo>('teach-limits', send('PUT', values)),
@@ -222,6 +240,9 @@ export const api = {
   /** An object sensor's question: is this box the own label it asks about? The answer is taught. */
   answerQuestion: (predictionId: number, action: 'yes' | 'no' | 'skip') =>
     request(`review/${predictionId}`, send('POST', { action })),
+  /** A question about a face: yes, it is another person (`person`), a new person (`name`), or no/skip. */
+  answerFace: (predictionId: number, action: 'yes' | 'no' | 'skip' | 'person' | 'new', person?: string, name?: string) =>
+    request(`review/${predictionId}`, send('POST', { action, state_key: person ?? null, value: name ?? null })),
   /** A reading: did the reader read the right number? `value` = the right one, when it misread. */
   verifyReading: (predictionId: number, action: 'read_ok' | 'misread' | 'skip', value?: string) =>
     request(`review/${predictionId}`, send('POST', { action, value: value || null })),
@@ -240,6 +261,30 @@ export const api = {
   forgetAll: (id: number) => request<void>(`sensors/${id}/taught`, send('DELETE')),
   removeLabel: (id: number, key: string) => request<void>(`sensors/${id}/labels/${encodeURIComponent(key)}`, send('DELETE')),
 
+  /** Faces: taught on a sensor; the people (shared by every sensor), renamed, moved and forgotten. */
+  people: () => request<People>('people'),
+  checkFace: (id: number, body: FaceBoxInput) =>
+    request<{ face: [number, number, number, number] | null; problem: FaceProblem | null; message: string }>(
+      `sensors/${id}/faces/check`,
+      send('POST', body),
+    ),
+  teachFace: (id: number, body: FaceBoxInput & { score?: number | null; person?: string; new_person?: string }) =>
+    request<{ id: number; label: string; name: string; faces: number }>(`sensors/${id}/faces`, send('POST', body)),
+  faceImageUrl: (faceId: number) => `${BASE}faces/${faceId}/image`,
+  moveFace: (faceId: number, person: string) => request(`faces/${faceId}`, send('PATCH', { person })),
+  forgetFace: (faceId: number) => request<void>(`faces/${faceId}`, send('DELETE')),
+  renamePerson: (key: string, name: string) =>
+    request<{ key: string; name: string }>(`people/${encodeURIComponent(key)}`, send('PATCH', { name })),
+  /** A person (or, without a key, everyone) with their faces and entities on every sensor. */
+  forgetPerson: (key: string) => request<void>(`people/${encodeURIComponent(key)}`, send('DELETE')),
+  forgetPeople: () => request<void>('people', send('DELETE')),
+  retryFaces: () => request<FaceStatus>('faces/retry', send('POST')),
+  faceLimits: () => request<FaceLimitsInfo>('face-limits'),
+  saveFaceLimits: (values: FaceOverrides) => request<FaceLimitsInfo>('face-limits', send('PUT', values)),
+  /** Delete a downloaded AI model that is not in use; `kind` is its list in GET /settings. */
+  deleteModel: (kind: ModelKind, id: string) =>
+    request<SettingsInfo>(`models/${kind}/${encodeURIComponent(id)}`, send('DELETE')),
+
   importBundle(file: File) {
     const form = new FormData();
     form.append('file', file, file.name);
diff --git a/visionstate/frontend/src/lib/components/DetectionBoxes.svelte b/visionstate/frontend/src/lib/components/DetectionBoxes.svelte
index d17987d..2a7ece9 100644
--- a/visionstate/frontend/src/lib/components/DetectionBoxes.svelte
+++ b/visionstate/frontend/src/lib/components/DetectionBoxes.svelte
@@ -3,7 +3,8 @@
   // positioned frame). Boxes are normalised to the whole frame. Labels never overlap: the most
   // certain object gets its label above the box, the next one inside the box if that is free,
   // and a label that fits nowhere is left out (the box stays). Boxes filtered away by what the
-  // sensor was taught are dashed and grey. With `onpick`, every box can be tapped.
+  // sensor was taught are dashed and grey. With `onpick`, every box can be tapped. A face found in a
+  // person box (a sensor that recognises faces) is drawn as a small rounded box inside it.
   import { pct } from '../format';
   import { boxName, objectColor } from '../objects';
   import type { Detection, OwnLabel } from '../types';
@@ -92,6 +93,19 @@
       </div>
     {/if}
   {/each}
+  {#each detections as d, i (i)}
+    {#if d.face?.box && !d.filtered}
+      <div
+        class="face"
+        class:unusable={!!d.face.problem}
+        style:left="{d.face.box[0] * 100}%"
+        style:top="{d.face.box[1] * 100}%"
+        style:width="{(d.face.box[2] - d.face.box[0]) * 100}%"
+        style:height="{(d.face.box[3] - d.face.box[1]) * 100}%"
+        style:--c={objectColor(classes, d.label ?? d.key)}
+      ></div>
+    {/if}
+  {/each}
 </div>
 
 <style>
@@ -137,6 +151,16 @@
     line-height: 15px;
     white-space: nowrap;
   }
+  .face {
+    position: absolute;
+    border: 2px solid var(--c);
+    border-radius: 40%;
+    box-shadow: 0 0 0 1px rgba(0, 0, 0, 0.35);
+  }
+  .face.unusable {
+    border-style: dotted;
+    opacity: 0.7;
+  }
   .tag.inside {
     bottom: auto;
     top: -2px;
diff --git a/visionstate/frontend/src/lib/components/ExportButton.svelte b/visionstate/frontend/src/lib/components/ExportButton.svelte
new file mode 100644
index 0000000..0466527
--- /dev/null
+++ b/visionstate/frontend/src/lib/components/ExportButton.svelte
@@ -0,0 +1,54 @@
+<script lang="ts">
+  // Export a sensor as a bundle to import elsewhere. A sensor where faces were taught asks first
+  // whether to include them (unticked by default), with the names of their people.
+  import { api } from '../api';
+  import type { Sensor } from '../types';
+  import Icon from './Icon.svelte';
+
+  let { sensor }: { sensor: Sensor } = $props();
+
+  const faces = $derived(sensor.objects?.taught_faces ?? 0);
+  let open = $state(false);
+  let withFaces = $state(false);
+</script>
+
+{#if !faces}
+  <a class="btn sm" href={api.exportUrl(sensor.id)} download><Icon name="download" size={14} /> Export</a>
+{:else}
+  <span class="wrap">
+    <button class="btn sm" onclick={() => (open = !open)} aria-expanded={open}>
+      <Icon name="download" size={14} /> Export
+    </button>
+    {#if open}
+      <div class="pop card pad col" role="dialog" aria-label="Export">
+        <label class="check small">
+          <input type="checkbox" bind:checked={withFaces} />
+          Include the faces taught on this camera ({faces})
+        </label>
+        <span class="xsmall faint">With the names of their people; an import adds them to the people there.</span>
+        <a class="btn sm primary" href={api.exportUrl(sensor.id, withFaces)} download onclick={() => (open = false)}>
+          <Icon name="download" size={14} /> Download
+        </a>
+      </div>
+    {/if}
+  </span>
+{/if}
+
+<style>
+  .wrap {
+    position: relative;
+    display: inline-flex;
+  }
+  .pop {
+    position: absolute;
+    right: 0;
+    top: calc(100% + 6px);
+    z-index: 30;
+    width: min(280px, calc(100vw - 32px));
+    gap: var(--space-2);
+    box-shadow: 0 12px 40px rgba(0, 0, 0, 0.5);
+  }
+  .pop .btn {
+    align-self: flex-start;
+  }
+</style>
diff --git a/visionstate/frontend/src/lib/components/FaceConsent.svelte b/visionstate/frontend/src/lib/components/FaceConsent.svelte
new file mode 100644
index 0000000..36a9947
--- /dev/null
+++ b/visionstate/frontend/src/lib/components/FaceConsent.svelte
@@ -0,0 +1,125 @@
+<script lang="ts">
+  // Before face recognition is turned on for a sensor: what it does, what is stored and what is
+  // downloaded, said plainly. Nothing is downloaded until the user confirms. The model's name,
+  // licence, size and source come from the backend (GET /settings). A bottom sheet on phones.
+  import { onMount } from 'svelte';
+  import { size } from '../format';
+  import type { FaceModelInfo } from '../types';
+  import Icon from './Icon.svelte';
+
+  let {
+    name,
+    model,
+    onclose,
+    onconfirm,
+  }: {
+    /** The sensor's name ("" while it is being made). */
+    name: string;
+    model: FaceModelInfo | null;
+    onclose: () => void;
+    onconfirm: () => void;
+  } = $props();
+
+  let panel: HTMLDivElement;
+  onMount(() => panel?.querySelector<HTMLElement>('button.first')?.focus());
+
+  function keydown(e: KeyboardEvent) {
+    if (e.key === 'Escape') onclose();
+  }
+</script>
+
+<svelte:window onkeydown={keydown} />
+
+<div class="backdrop" onclick={onclose} role="presentation"></div>
+<div class="sheet" role="dialog" aria-modal="true" aria-label="Recognise faces" bind:this={panel}>
+  <div class="row head">
+    <strong>Recognise faces{name ? ` on ${name}` : ''}?</strong>
+    <span class="spacer"></span>
+    <button class="btn ghost icon close" onclick={onclose} aria-label="Close"><Icon name="x" /></button>
+  </div>
+  <ul class="small">
+    <li>
+      Faces are looked for only in the people this sensor finds, and compared with faces <strong>you</strong> name —
+      the people are shared: those you named on other cameras are known here too.
+    </li>
+    <li>
+      Stored on this Home Assistant only, in the app's own storage (not the media folder, and in its backups): a small
+      picture of each face you teach. Faces of people you have not named are not kept, apart from the normal history
+      frames. The people you teach should know about it.
+    </li>
+    {#if model}
+      <li>
+        Needs the face model <em>{model.name}</em> with {model.detector.name} ({model.license} and {model.detector.license}{model.installed
+          ? ', already downloaded'
+          : `, ${size(model.size)} downloaded once from Hugging Face`}). {model.data} {model.detector.data}
+      </li>
+    {/if}
+    <li>Cameras that see a street or a neighbour's door may need their consent where you live.</li>
+    <li>It is not a lock: a photo of a face looks like that face. Do not unlock doors or disarm alarms on it alone.</li>
+  </ul>
+  <span class="xsmall faint">More in the app's Documentation tab.</span>
+  <div class="row actions">
+    <button class="btn primary first" onclick={onconfirm}>{model?.installed ? 'Turn on' : 'Download and turn on'}</button>
+    <button class="btn" onclick={onclose}>Cancel</button>
+  </div>
+</div>
+
+<style>
+  .backdrop {
+    position: fixed;
+    inset: 0;
+    z-index: 40;
+    background: rgba(5, 7, 9, 0.6);
+  }
+  .sheet {
+    position: fixed;
+    z-index: 41;
+    left: 50%;
+    top: 50%;
+    transform: translate(-50%, -50%);
+    width: min(480px, calc(100vw - 32px));
+    max-height: calc(100vh - 48px);
+    overflow-y: auto;
+    background: var(--c-surface);
+    border: 1px solid var(--c-border-strong);
+    border-radius: var(--radius-lg);
+    padding: var(--space-4);
+    display: flex;
+    flex-direction: column;
+    gap: var(--space-3);
+    box-shadow: 0 12px 40px rgba(0, 0, 0, 0.5);
+  }
+  @media (max-width: 600px) {
+    .sheet {
+      left: 0;
+      right: 0;
+      top: auto;
+      bottom: 0;
+      transform: none;
+      width: auto;
+      max-height: 85vh;
+      border-radius: var(--radius-lg) var(--radius-lg) 0 0;
+      padding-bottom: calc(var(--space-4) + env(safe-area-inset-bottom));
+    }
+  }
+  .head {
+    align-items: flex-start;
+    gap: var(--space-3);
+  }
+  .close {
+    flex-shrink: 0;
+    margin: -6px -6px 0 0;
+  }
+  ul {
+    margin: 0;
+    padding-left: 1.2em;
+    display: flex;
+    flex-direction: column;
+    gap: var(--space-2);
+    color: var(--c-text-2);
+  }
+  .actions {
+    gap: var(--space-2);
+    margin-top: var(--space-2);
+  }
+</style>
diff --git a/visionstate/frontend/src/lib/components/FaceLimitsEditor.svelte b/visionstate/frontend/src/lib/components/FaceLimitsEditor.svelte
new file mode 100644
index 0000000..b4462fd
--- /dev/null
+++ b/visionstate/frontend/src/lib/components/FaceLimitsEditor.svelte
@@ -0,0 +1,39 @@
+<script lang="ts">
+  // Edits the face limits: the smallest face and the similarities. An empty field inherits (shown
+  // in grey): on the Settings page the face model's default, on a sensor the global value.
+  import { app } from '../app.svelte';
+  import type { FaceLimits, FaceOverrides } from '../types';
+  import { FACE_FIELDS } from '../ui';
+  import LimitsEditor from './LimitsEditor.svelte';
+
+  let {
+    value = $bindable(),
+    inherited,
+    inheritedFrom,
+  }: { value: FaceOverrides; inherited: FaceLimits; inheritedFrom: string } = $props();
+
+  const limits = $derived(app.config?.face_limits);
+  const below = [
+    {
+      pairs: [
+        ['ask_similarity', 'match_similarity'],
+        ['keep_similarity', 'match_similarity'],
+      ],
+      message: 'Asking and keeping the name must not be above “Recognised as a person”.',
+    },
+    { pairs: [['match_similarity', 'sure_similarity']], message: '“Recognised as a person” must not be above “Sure at once”.' },
+  ] as const;
+</script>
+
+{#if limits}
+  <LimitsEditor
+    bind:value
+    {inherited}
+    {inheritedFrom}
+    fields={FACE_FIELDS}
+    {limits}
+    {below}
+    id="face"
+    note="Similarities mean something else for each face model."
+  />
+{/if}
diff --git a/visionstate/frontend/src/lib/components/FaceQuestion.svelte b/visionstate/frontend/src/lib/components/FaceQuestion.svelte
new file mode 100644
index 0000000..e21b79d
--- /dev/null
+++ b/visionstate/frontend/src/lib/components/FaceQuestion.svelte
@@ -0,0 +1,231 @@
+<script lang="ts">
+  // A review question about a face: "Is this Kari?" (not sure enough to say so by itself) or "Who
+  // is this?" (a face nobody was taught for, when the sensor asks about those). The face close-up
+  // sits beside the frame (stacked on phones), with one of the person's taught faces to compare.
+  // A face model's similarity means nothing to people, so no percentage is shown.
+  import { api } from '../api';
+  import { app, toastError } from '../app.svelte';
+  import type { ObjectQuestion, ReviewItem } from '../types';
+  import DetectionBoxes from './DetectionBoxes.svelte';
+  import Icon from './Icon.svelte';
+  import RoiEditor from './RoiEditor.svelte';
+
+  let {
+    item,
+    question,
+    given,
+    onanswer,
+  }: {
+    item: ReviewItem;
+    question: ObjectQuestion;
+    /** The answer given before, when the item is opened again. */
+    given: string | null | undefined;
+    onanswer: (text: string, answer: string | null) => void;
+  } = $props();
+
+  const who = $derived(question.label === app.config?.face_unknown_key);
+  const people = $derived(item.sensor.labels.filter((l) => l.face));
+  const name = $derived(people.find((p) => p.key === question.label)?.name ?? question.label);
+  const others = $derived(people.filter((p) => p.key !== question.label));
+  let busy = $state(false);
+  let naming = $state(false);
+  let newName = $state('');
+
+  // The close-up: the face with some room around it, as a background crop of the frame.
+  const src = $derived(api.historyImageUrl(item.id));
+  let natural = $state<{ w: number; h: number } | null>(null);
+  $effect(() => {
+    const img = new Image();
+    img.onload = () => (natural = { w: img.naturalWidth, h: img.naturalHeight });
+    img.src = src;
+  });
+  const crop = $derived.by(() => {
+    if (!natural || !question.face) return null;
+    const [fx1, fy1, fx2, fy2] = question.face;
+    const mx = (fx2 - fx1) * 0.3;
+    const my = (fy2 - fy1) * 0.3;
+    const [x1, y1, x2, y2] = [Math.max(0, fx1 - mx), Math.max(0, fy1 - my), Math.min(1, fx2 + mx), Math.min(1, fy2 + my)];
+    const w = x2 - x1;
+    const h = y2 - y1;
+    return {
+      aspect: (w * natural.w) / (h * natural.h),
+      size: `${100 / w}% ${100 / h}%`,
+      position: `${w < 1 ? (x1 / (1 - w)) * 100 : 0}% ${h < 1 ? (y1 / (1 - h)) * 100 : 0}%`,
+    };
+  });
+
+  async function send(action: 'yes' | 'no' | 'skip' | 'person' | 'new', person?: string, text?: string) {
+    if (busy) return;
+    busy = true;
+    try {
+      await api.answerFace(item.id, action, person, action === 'new' ? newName.trim() : undefined);
+      const label = person ?? (action === 'new' ? 'new' : action === 'skip' ? null : action);
+      onanswer(text ?? 'Skipped', label);
+    } catch (err) {
+      toastError(err);
+    } finally {
+      busy = false;
+    }
+  }
+
+  function keydown(e: KeyboardEvent) {
+    if (naming || (e.target as HTMLElement).closest('input, select, textarea')) return;
+    if (e.key === 'Enter' && !who) send('yes', undefined, `✓ ${name}`);
+    else if (e.key.toLowerCase() === 's') send('skip');
+  }
+</script>
+
+<svelte:window onkeydown={keydown} />
+
+<div class="pair">
+  <div class="frame">
+    <RoiEditor {src} roi={item.sensor.roi}>
+      <DetectionBoxes
+        detections={[{ key: 'person', score: item.confidence, box: question.box, face: { box: question.face, problem: null } }]}
+        classes={['person']}
+        labels={false}
+      />
+    </RoiEditor>
+  </div>
+  <div class="faces">
+    {#if crop}
+      <figure>
+        <div
+          class="closeup"
+          style:aspect-ratio={crop.aspect}
+          style:background-image="url({src})"
+          style:background-size={crop.size}
+          style:background-position={crop.position}
+        ></div>
+        <figcaption class="xsmall faint">This face</figcaption>
+      </figure>
+    {/if}
+    {#if !who && question.example}
+      <figure>
+        <img src={api.faceImageUrl(question.example)} alt="A face taught as {name}" />
+        <figcaption class="xsmall faint">Taught as {name}</figcaption>
+      </figure>
+    {/if}
+  </div>
+</div>
+
+<div class="col center">
+  <p class="question">
+    {#if who}
+      Who is this?
+      <span class="small muted">A face this sensor does not know.</span>
+    {:else}
+      Is this <strong>{name}</strong>?
+      <span class="small muted">Probably, but not sure enough to say so by itself.</span>
+    {/if}
+  </p>
+  {#if naming}
+    <form
+      class="row wrap center-row"
+      onsubmit={(e) => {
+        e.preventDefault();
+        if (newName.trim()) send('new', undefined, `✓ ${newName.trim()} (new)`);
+      }}
+    >
+      <!-- svelte-ignore a11y_autofocus -->
+      <input class="input" bind:value={newName} maxlength="64" placeholder="Name" aria-label="Name of the new person" autofocus />
+      <button class="btn primary" type="submit" disabled={busy || !newName.trim()}>Add person</button>
+      <button class="btn" type="button" onclick={() => (naming = false)}>Back</button>
+    </form>
+  {:else}
+    <div class="row wrap center-row">
+      {#if !who}
+        <button class="btn primary lg" class:chosen={given === 'yes'} disabled={busy} onclick={() => send('yes', undefined, `✓ ${name}`)}>
+          <Icon name="check" /> Yes, {name}
+        </button>
+        <span class="small faint">No, it is</span>
+      {/if}
+      {#each who ? people : others as p (p.key)}
+        <button class="btn lg" class:chosen={given === p.key} disabled={busy} onclick={() => send('person', p.key, `✓ ${p.name}`)}>
+          {p.name}
+        </button>
+      {/each}
+      <button class="btn lg accent-outline" disabled={busy} onclick={() => (naming = true)}>
+        <Icon name="plus" size={14} /> New person…
+      </button>
+    </div>
+    <div class="row wrap center-row">
+      {#if !who}
+        <button class="btn ghost" class:chosen={given === 'no'} disabled={busy} onclick={() => send('no', undefined, `✓ Not ${name}`)}>
+          No, someone I do not know
+        </button>
+      {/if}
+      <button class="btn ghost" class:chosen={given === null} disabled={busy} onclick={() => send('skip')}>
+        {who ? 'Don’t know' : 'Skip'}
+      </button>
+    </div>
+  {/if}
+  <span class="xsmall faint">A name you give is taught: this face is added to that person.</span>
+  <span class="xsmall faint kbd-only">{who ? 'S = don’t know' : 'Enter = yes · S = skip'}</span>
+</div>
+
+<style>
+  .pair {
+    display: grid;
+    grid-template-columns: minmax(0, 1fr) 140px;
+    gap: var(--space-4);
+    align-items: start;
+  }
+  .faces {
+    display: flex;
+    flex-direction: column;
+    gap: var(--space-3);
+  }
+  figure {
+    margin: 0;
+    display: flex;
+    flex-direction: column;
+    gap: 4px;
+  }
+  .closeup,
+  figure img {
+    width: 100%;
+    border-radius: var(--radius-sm);
+    background-repeat: no-repeat;
+    background-color: var(--c-sunken);
+  }
+  figure img {
+    aspect-ratio: 1;
+    object-fit: cover;
+  }
+  .frame {
+    border-radius: var(--radius-md);
+    overflow: hidden;
+  }
+  .center {
+    align-items: center;
+    text-align: center;
+    gap: var(--space-3);
+    margin-top: var(--space-4);
+  }
+  .center-row {
+    justify-content: center;
+    gap: var(--space-2);
+  }
+  .question {
+    display: flex;
+    flex-direction: column;
+    gap: 4px;
+    margin: 0;
+    font-size: var(--fs-lg);
+  }
+  .chosen {
+    outline: 2px solid var(--c-accent);
+  }
+  @media (max-width: 600px) {
+    .pair {
+      grid-template-columns: minmax(0, 1fr);
+    }
+    .faces {
+      flex-direction: row;
+    }
+    .faces figure {
+      width: 120px;
+    }
+  }
+</style>
diff --git a/visionstate/frontend/src/lib/components/FaceStats.svelte b/visionstate/frontend/src/lib/components/FaceStats.svelte
new file mode 100644
index 0000000..13a5bd7
--- /dev/null
+++ b/visionstate/frontend/src/lib/components/FaceStats.svelte
@@ -0,0 +1,51 @@
+<script lang="ts">
+  // Does the camera give usable faces? "A usable face in 9 of the last 40 people looked at", and
+  // when that share is low, what usually helps for the most common reason. Used on the Live tab,
+  // the Faces card and the Quality tab.
+  import { FACE_PROBLEMS } from '../objects';
+  import type { FaceProblem, SensorObjects } from '../types';
+
+  let { objects }: { objects: SensorObjects } = $props();
+
+  // Below this share, with enough people looked at, a hint names the likely fix.
+  const LOW_SHARE = 0.3;
+  const ENOUGH = 10;
+
+  const stats = $derived(objects.face_stats);
+  const main = $derived(
+    (Object.entries(stats.problems) as [FaceProblem, number][]).sort((a, b) => b[1] - a[1])[0]?.[0] ?? null,
+  );
+  const low = $derived(stats.looked >= ENOUGH && stats.usable / stats.looked < LOW_SHARE);
+  const frame = $derived(stats.frame ? `${stats.frame[0]}×${stats.frame[1]}` : '');
+</script>
+
+<span class="xsmall faint">
+  {#if !stats.looked}
+    No person looked at for a face yet.
+  {:else}
+    A usable face in {stats.usable} of the last {stats.looked} people looked at{main ? ` (mostly: ${FACE_PROBLEMS[main]})` : ''}.
+  {/if}
+</span>
+{#if low}
+  <span class="xsmall warn">
+    {#if main === 'small'}
+      Faces here are mostly too small: a face needs to be {stats.min_face_px ?? ''} pixels wide{frame
+        ? `, and this camera sends ${frame} pictures`
+        : ''}. Use the camera's main stream (its highest resolution) rather than a sub-stream or a small snapshot,
+      or a camera closer to where people pass.
+    {:else if main === 'cut'}
+      Faces here are often cut off by the edge of the picture. A camera that sees people whole helps.
+    {:else if main === 'turned' || main === 'none'}
+      Faces here are rarely usable: people seldom face this camera. A camera that people walk towards helps.
+    {:else}
+      Faces here are rarely usable. A sharper picture (the camera's main stream over RTSP instead of the snapshot)
+      or more light helps.
+    {/if}
+  </span>
+{/if}
+
+<style>
+  .warn {
+    color: var(--c-warn);
+  }
+</style>
diff --git a/visionstate/frontend/src/lib/components/FacesCard.svelte b/visionstate/frontend/src/lib/components/FacesCard.svelte
new file mode 100644
index 0000000..5ca895e
--- /dev/null
+++ b/visionstate/frontend/src/lib/components/FacesCard.svelte
@@ -0,0 +1,172 @@
+<script lang="ts">
+  // Recognising people by their face, on an object sensor's Settings tab. Off by default: turning
+  // it on opens a sheet that says what is stored and what is downloaded, before anything is. Its
+  // changes are saved at once (not with "Save changes"), like teaching is.
+  import { api } from '../api';
+  import { app, toast, toastError } from '../app.svelte';
+  import { size } from '../format';
+  import { href, paths } from '../router.svelte';
+  import type { FaceKey, FaceLimitsInfo, FaceModelInfo, FaceOverrides, FaceSettings, Sensor } from '../types';
+  import { FACE_FIELDS } from '../ui';
+  import FaceConsent from './FaceConsent.svelte';
+  import FaceLimitsEditor from './FaceLimitsEditor.svelte';
+  import FaceStats from './FaceStats.svelte';
+  import Icon from './Icon.svelte';
+
+  let { sensor, onchange }: { sensor: Sensor; onchange: () => void } = $props();
+
+  const settings = $derived<FaceSettings>({ ...app.config!.face_defaults, ...(sensor.objects?.faces ?? {}) });
+  const looksForPeople = $derived(!!sensor.objects?.classes.includes('person'));
+  const status = $derived(app.status?.faces ?? null);
+  const people = $derived(app.status?.faces.people ?? 0);
+
+  let model = $state<FaceModelInfo | null>(null);
+  let asking = $state(false);
+  let busy = $state(false);
+  let globalFaces = $state<FaceLimitsInfo | null>(null);
+  // The sensor's own face limits (null: the global value), saved with their own button.
+  const own = (): FaceOverrides =>
+    Object.fromEntries(FACE_FIELDS.map((f) => [f.key, sensor.objects?.faces?.[f.key as FaceKey] ?? null]));
+  let limitValues = $state<FaceOverrides>(own());
+  const limitsChanged = $derived(JSON.stringify(limitValues) !== JSON.stringify(own()));
+  api
+    .settings()
+    .then((s) => (model = s.face_models.find((m) => m.id === s.face_model) ?? null))
+    .catch(toastError);
+  api
+    .faceLimits()
+    .then((f) => (globalFaces = f))
+    .catch(toastError);
+
+  async function save(faces: Partial<FaceSettings>, message?: string) {
+    busy = true;
+    try {
+      await api.updateSensor(sensor.id, { objects: { faces: { ...settings, ...faces } } });
+      if (message) toast(message);
+      onchange();
+    } catch (err) {
+      toastError(err);
+    } finally {
+      busy = false;
+    }
+  }
+
+  async function retry() {
+    try {
+      await api.retryFaces();
+    } catch (err) {
+      toastError(err);
+    }
+  }
+</script>
+
+<section class="card pad col faces" data-testid="faces-card">
+  <h3>Faces</h3>
+  {#if !looksForPeople}
+    <p class="small muted">Add Person to the objects to recognise people by their face.</p>
+  {:else if !settings.enabled}
+    <strong class="small">Recognise people by their face</strong>
+    <p class="small muted">
+      Name the people you know; each gets an on/off sensor in Home Assistant.
+      {#if people}It knows the {people} {people === 1 ? 'person' : 'people'} you named on other cameras right away.{/if}
+    </p>
+    <div class="row">
+      <button class="btn" onclick={() => (asking = true)}><Icon name="plus" size={14} /> Turn on…</button>
+    </div>
+  {:else}
+    <label class="check">
+      <input type="checkbox" checked disabled={busy} onchange={() => save({ enabled: false }, 'Face recognition is off')} />
+      <span class="col" style="gap:2px">
+        <strong class="small">Recognise faces</strong>
+        <span class="xsmall faint">
+          Off removes the people's entities from this sensor; the people stay (they are shared).
+        </span>
+      </span>
+    </label>
+    <label class="check">
+      <input
+        type="checkbox"
+        checked={settings.ask_unknown}
+        disabled={busy}
+        onchange={(e) => save({ ask_unknown: e.currentTarget.checked })}
+      />
+      <span class="col" style="gap:2px">
+        <strong class="small">Ask me about faces I do not know</strong>
+        <span class="xsmall faint">“Who is this?” in the review queue. Keeps frames of strangers longer.</span>
+      </span>
+    </label>
+    {#if globalFaces}
+      <div class="col limits" data-testid="sensor-face-limits">
+        <strong class="small">Limits for this sensor</strong>
+        <FaceLimitsEditor bind:value={limitValues} inherited={globalFaces.effective} inheritedFrom="the global value" />
+        <button
+          class="btn sm primary"
+          style="align-self:flex-start"
+          disabled={!limitsChanged || busy}
+          onclick={() => save(limitValues, 'Face limits saved')}
+        >
+          Save limits
+        </button>
+      </div>
+    {/if}
+
+    {#if status?.error}
+      <div class="notice warn col" style="gap:6px">
+        <span class="small">The face model could not be loaded: {status.error}</span>
+        <div class="row"><button class="btn sm" onclick={retry}><Icon name="refresh" size={14} /> Try again</button></div>
+      </div>
+    {:else if status?.progress}
+      <div class="col" style="gap:6px">
+        <span class="small muted">
+          Downloading the face model… {size(status.progress.done)} of {size(status.progress.total)}
+        </span>
+        <progress max={status.progress.total} value={status.progress.done}></progress>
+      </div>
+    {:else if status && !status.loaded}
+      <span class="small muted">Loading the face model…</span>
+    {:else if sensor.objects}
+      <FaceStats objects={sensor.objects} />
+    {/if}
+
+    <span class="xsmall faint">
+      {people} {people === 1 ? 'person' : 'people'}, shared by every camera that recognises faces:
+      <a href={href(paths.people())}>People</a>.
+    </span>
+    {#if model}
+      <span class="xsmall faint">
+        {model.name} · {size(model.size)} · change under <a href={href(paths.settings())}>Settings → AI model</a>.
+      </span>
+    {/if}
+  {/if}
+</section>
+
+{#if asking}
+  <FaceConsent
+    name={sensor.name}
+    {model}
+    onclose={() => (asking = false)}
+    onconfirm={async () => {
+      asking = false;
+      await save({ enabled: true }, 'Face recognition is on');
+    }}
+  />
+{/if}
+
+<style>
+  .faces {
+    gap: var(--space-3);
+  }
+  .check {
+    align-items: flex-start;
+  }
+  .check input {
+    margin-top: 3px;
+  }
+  .limits {
+    gap: var(--space-3);
+  }
+  progress {
+    width: 100%;
+    accent-color: var(--c-accent);
+  }
+</style>
diff --git a/visionstate/frontend/src/lib/components/LimitsEditor.svelte b/visionstate/frontend/src/lib/components/LimitsEditor.svelte
new file mode 100644
index 0000000..114a03f
--- /dev/null
+++ b/visionstate/frontend/src/lib/components/LimitsEditor.svelte
@@ -0,0 +1,120 @@
+<script lang="ts">
+  // Edits tunable limits that inherit (teaching of object sensors, faces). An empty field inherits
+  // (shown in grey): on the Settings page the default, on a sensor the global value. Similarities
+  // are shown in percent, pixel values as they are.
+  import Icon from './Icon.svelte';
+
+  type Field = { key: string; label: string; help: string; unit?: 'px' };
+
+  let {
+    value = $bindable(),
+    inherited,
+    inheritedFrom,
+    fields,
+    limits,
+    below = [],
+    id,
+    note = '',
+  }: {
+    value: Record<string, number | null | undefined>;
+    /** What an empty field uses. */
+    inherited: Record<string, number>;
+    /** Where that comes from, e.g. "the global value". */
+    inheritedFrom: string;
+    fields: readonly Field[];
+    /** Lowest and highest value per key. */
+    limits: Record<string, readonly [number, number]>;
+    /** Pairs [a, b]: a must not be above b; with the message shown when it is. */
+    below?: readonly { pairs: readonly (readonly [string, string])[]; message: string }[];
+    /** Prefix of the inputs' ids and test ids. */
+    id: string;
+    note?: string;
+  } = $props();
+
+  const pct = (f: Field) => f.unit !== 'px';
+  const shown = (f: Field, n: number) => (pct(f) ? Math.round(n * 100) : Math.round(n));
+  const effective = (key: string) => value[key] ?? inherited[key];
+  const conflicts = $derived(below.filter((b) => b.pairs.some(([a, c]) => effective(a) > effective(c))));
+
+  function set(f: Field, raw: string) {
+    value[f.key] = raw.trim() === '' ? null : pct(f) ? Number(raw) / 100 : Number(raw);
+  }
+</script>
+
+<div class="col limits">
+  {#each fields as f (f.key)}
+    {@const own = value[f.key] !== null && value[f.key] !== undefined}
+    <div class="line" data-testid={`${id}-${f.key}`}>
+      <label class="col name" for={`${id}-${f.key}`}>
+        <span>{f.label}</span>
+        <span class="xsmall faint">{f.help}</span>
+      </label>
+      <span class="row input-row">
+        <input
+          id={`${id}-${f.key}`}
+          class="input sm num"
+          class:own
+          type="number"
+          min={shown(f, limits[f.key][0])}
+          max={shown(f, limits[f.key][1])}
+          step="1"
+          value={own ? shown(f, value[f.key] as number) : ''}
+          placeholder={String(shown(f, inherited[f.key]))}
+          oninput={(e) => set(f, e.currentTarget.value)}
+          aria-label={f.label}
+        />
+        <span class="unit small muted">{pct(f) ? '%' : 'px'}</span>
+        <button
+          class="btn sm ghost reset"
+          class:hidden={!own}
+          disabled={!own}
+          onclick={() => (value[f.key] = null)}
+          aria-label={`Use ${inheritedFrom} (${shown(f, inherited[f.key])}${pct(f) ? '%' : ' px'})`}
+          title={`Use ${inheritedFrom}`}
+        >
+          <Icon name="x" size={14} />
+        </button>
+      </span>
+    </div>
+  {/each}
+  {#each conflicts as c (c.message)}
+    <div class="notice warn small">{c.message}</div>
+  {/each}
+  <p class="xsmall faint">Empty fields (in grey) use {inheritedFrom}.{note ? ` ${note}` : ''}</p>
+</div>
+
+<style>
+  .limits {
+    gap: var(--space-3);
+  }
+  .line {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    gap: var(--space-4);
+    color: var(--c-text-2);
+  }
+  .name {
+    gap: 2px;
+    min-width: 0;
+  }
+  .input-row {
+    gap: var(--space-2);
+    flex-shrink: 0;
+  }
+  .num {
+    width: 72px;
+  }
+  .num::placeholder {
+    color: var(--c-faint);
+  }
+  .num:not(.own) {
+    color: var(--c-faint);
+  }
+  .unit {
+    width: 18px;
+  }
+  .reset.hidden {
+    visibility: hidden;
+  }
+</style>
diff --git a/visionstate/frontend/src/lib/components/ModelCompare.svelte b/visionstate/frontend/src/lib/components/ModelCompare.svelte
index f677887..fa73f09 100644
--- a/visionstate/frontend/src/lib/components/ModelCompare.svelte
+++ b/visionstate/frontend/src/lib/components/ModelCompare.svelte
@@ -1,16 +1,38 @@
 <script lang="ts">
-  // The AI models side by side, per kind: what they are for, rough memory and time, and how
-  // accurate they are compared with each other. Numbers come from the registries (GET /settings).
-  import { mb } from '../format';
-  import type { BackboneInfo, SettingsInfo } from '../types';
+  // The AI models side by side, per kind and from the smallest to the largest: what they are for,
+  // rough memory and time, how accurate they are compared with each other, and whether they are
+  // on disk — a downloaded model not in use can be deleted here (it is downloaded again when
+  // chosen). Numbers come from the registries (GET /settings).
+  import { api } from '../api';
+  import { toast, toastError } from '../app.svelte';
+  import { bySize, mb } from '../format';
+  import type { BackboneInfo, ModelKind, SettingsInfo } from '../types';
   import { MODEL_COMPARE_NOTE, MODEL_KINDS } from '../ui';
+  import ConfirmButton from './ConfirmButton.svelte';
   import Icon from './Icon.svelte';
 
-  let { info }: { info: SettingsInfo } = $props();
+  let { info, ondeleted }: { info: SettingsInfo; ondeleted: (info: SettingsInfo) => void } = $props();
   let open = $state(false);
 
-  const chosen = (kind: (typeof MODEL_KINDS)[number]['key']) =>
-    ({ backbones: info.backbone, detectors: info.detector, readers: info.reader, wheel_readers: info.wheel_reader })[kind];
+  type Kind = (typeof MODEL_KINDS)[number]['key'];
+  const chosen = (kind: Kind) =>
+    ({
+      backbones: info.backbone,
+      detectors: info.detector,
+      face_models: info.face_model,
+      readers: info.reader,
+      wheel_readers: info.wheel_reader,
+    })[kind];
+  const deletable = (kind: Kind): kind is ModelKind => kind !== 'wheel_readers';
+
+  async function remove(kind: ModelKind, m: BackboneInfo) {
+    try {
+      ondeleted(await api.deleteModel(kind, m.id));
+      toast(`${m.name} deleted`);
+    } catch (err) {
+      toastError(err);
+    }
+  }
   const time = (ms: number) => (ms < 1000 ? `${ms} ms` : `${(ms / 1000).toFixed(1)} s`);
   const ram = (m: BackboneInfo) => (m.ram_mb >= 1000 ? `${(m.ram_mb / 1000).toFixed(1)} GB` : `${m.ram_mb} MB`);
 </script>
@@ -18,7 +40,7 @@
 <div class="col compare">
   <button class="btn sm ghost toggle" onclick={() => (open = !open)} aria-expanded={open}>
     <Icon name="chevron" size={14} />
-    Compare the models
+    Compare and manage the models
   </button>
   {#if open}
     <div class="col kinds" data-testid="model-compare">
@@ -26,7 +48,7 @@
         <div class="col kind">
           <strong class="small">{kind.title}</strong>
           <span class="xsmall faint">{kind.text}</span>
-          {#each info[kind.key] as m (m.id)}
+          {#each bySize(info[kind.key] as BackboneInfo[]) as m (m.id)}
             <div class="model" class:current={m.id === chosen(kind.key)}>
               <span class="name small">
                 {m.name}{#if m.id === chosen(kind.key)}<span class="xsmall muted"> · in use</span>{/if}
@@ -34,11 +56,21 @@
               <span class="facts xsmall muted">
                 <span>~{ram(m)} RAM</span>
                 <span>~{time(m.time_ms)} per check</span>
-                <span>{m.installed ? 'on disk' : `download ${mb(m.size)}`}</span>
+                <span>{!m.installed ? `download ${mb(m.size)}` : m.downloaded ? `downloaded, ${mb(m.size)}` : 'included'}</span>
                 <span class="accuracy" aria-label={`Accuracy ${m.accuracy} of 5`} title="Accuracy compared with the others">
                   {#each [1, 2, 3, 4, 5] as n (n)}<span class="pip" class:on={n <= m.accuracy}></span>{/each}
                 </span>
               </span>
+              {#if m.downloaded && deletable(kind.key)}
+                {#if m.in_use}
+                  <span class="xsmall faint">{m.in_use}</span>
+                {:else}
+                  {@const k = kind.key}
+                  <ConfirmButton class="btn sm ghost delete" onconfirm={() => remove(k, m)} confirmLabel="Tap again to delete">
+                    <Icon name="trash" size={14} /> Delete
+                  </ConfirmButton>
+                {/if}
+              {/if}
             </div>
           {/each}
         </div>
@@ -83,6 +115,9 @@
   .name {
     color: var(--c-text);
   }
+  .model :global(.delete) {
+    align-self: flex-start;
+  }
   .facts {
     display: flex;
     flex-wrap: wrap;
diff --git a/visionstate/frontend/src/lib/components/Nav.svelte b/visionstate/frontend/src/lib/components/Nav.svelte
index b3fd30c..7c4a8c8 100644
--- a/visionstate/frontend/src/lib/components/Nav.svelte
+++ b/visionstate/frontend/src/lib/components/Nav.svelte
@@ -4,11 +4,16 @@
   import { BETA_VERSION_PATTERN } from '../ui';
   import Logo from './Logo.svelte';
 
-  const section = $derived(['review', 'history', 'settings'].includes(route.parts[0]) ? route.parts[0] : 'sensors');
+  const section = $derived(
+    ['review', 'history', 'people', 'settings'].includes(route.parts[0]) ? route.parts[0] : 'sensors',
+  );
+  // People: only once there are people, or a sensor that recognises faces.
+  const faces = $derived(app.status?.faces);
   const items = $derived([
     { id: 'sensors', label: 'Sensors', path: paths.dashboard(), badge: 0 },
     { id: 'review', label: 'Review', path: paths.review(), badge: app.status?.review_count ?? 0 },
     { id: 'history', label: 'History', path: paths.history(), badge: 0 },
+    ...(faces?.people || faces?.sensors ? [{ id: 'people', label: 'People', path: paths.people(), badge: 0 }] : []),
     { id: 'settings', label: 'Settings', path: paths.settings(), badge: 0 },
   ]);
 </script>
diff --git a/visionstate/frontend/src/lib/components/PeopleCards.svelte b/visionstate/frontend/src/lib/components/PeopleCards.svelte
new file mode 100644
index 0000000..9db4a63
--- /dev/null
+++ b/visionstate/frontend/src/lib/components/PeopleCards.svelte
@@ -0,0 +1,238 @@
+<script lang="ts">
+  // The people (shared by every sensor that recognises faces): one card per person with the faces
+  // taught and the camera of each, where they were last seen, whether they need more faces,
+  // look-alikes and faces that may carry the wrong name; renaming and forgetting. On the People
+  // page, and on the Quality tab of a sensor that recognises faces (`sensor`: how faces look on
+  // its camera, and links to it), reloaded with every poll of the sensor there.
+  import { untrack } from 'svelte';
+  import { api } from '../api';
+  import { app, refreshStatus, toast, toastError } from '../app.svelte';
+  import { ago, dateTime } from '../format';
+  import { href, paths } from '../router.svelte';
+  import type { People, Person, Sensor } from '../types';
+  import ConfirmButton from './ConfirmButton.svelte';
+  import FaceStats from './FaceStats.svelte';
+  import Icon from './Icon.svelte';
+
+  let { sensor = null, onchange = () => {} }: { sensor?: Sensor | null; onchange?: () => void } = $props();
+
+  // Faces taught longer ago than this get a hint to add recent ones (children change quickly).
+  const OLD_DAYS = 180;
+
+  let people = $state<People | null>(null);
+  async function load() {
+    try {
+      people = await api.people();
+    } catch (err) {
+      toastError(err);
+    }
+  }
+  $effect(() => {
+    void sensor;
+    untrack(load);
+  });
+
+  const suggest = $derived(app.config?.faces.suggest_faces ?? 0);
+  const nameOf = (key: string) => people?.people.find((p) => p.key === key)?.name ?? key;
+  const lookAlikes = (p: Person) =>
+    (people?.look_alikes ?? []).filter((l) => l.a === p.key || l.b === p.key).map((l) => nameOf(l.a === p.key ? l.b : l.a));
+  const old = (p: Person) => !!p.oldest && Date.now() - Date.parse(p.oldest) > OLD_DAYS * 86_400_000;
+  const enabled = $derived(!sensor || !!people?.sensors.includes(sensor.id));
+
+  let renaming = $state<string | null>(null);
+  let newName = $state('');
+  function startRename(p: Person) {
+    renaming = p.key;
+    newName = p.name;
+  }
+  async function rename(e: SubmitEvent, p: Person) {
+    e.preventDefault();
+    const name = newName.trim();
+    renaming = null;
+    if (name && name !== p.name) await act(() => api.renamePerson(p.key, name), `${p.name} is now ${name}`);
+  }
+  function seen(p: Person): string {
+    const where = p.seen.sensor_name ? ` on ${p.seen.sensor_name}` : '';
+    if (p.seen.now !== 'not_seen') return `Seen now${where}`;
+    return p.seen.at ? `Last seen ${ago(p.seen.at)}${where}` : 'Not seen yet';
+  }
+
+  async function act(action: () => Promise<unknown>, message: string) {
+    try {
+      await action();
+      toast(message);
+      onchange();
+      load();
+      refreshStatus();
+    } catch (err) {
+      toastError(err);
+    }
+  }
+</script>
+
+{#if people && (people.people.length || enabled)}
+  <section class="card pad col" style="gap:var(--space-2)" data-testid="people">
+    {#if sensor}<div class="card-title"><h3>People</h3></div>{/if}
+    {#if sensor && !enabled}
+      <div class="notice warn">
+        Face recognition is off for this sensor under <a href={href(paths.sensor(sensor.id, 'settings'))}>Settings</a>.
+      </div>
+    {:else if sensor?.objects}
+      <FaceStats objects={sensor.objects} />
+    {/if}
+    <p class="small muted">
+      {#if people.people.length}
+        Shared by every camera that recognises faces. Tap a person on a sensor's Live tab or in the history to teach
+        more faces — in other light, from other angles and on each camera.
+      {:else if sensor}
+        Nobody taught yet: tap a person on the <a href={href(paths.sensor(sensor.id, 'live'))}>Live</a> tab and say who it is.
+      {:else}
+        Nobody taught yet: tap a person on the Live tab of a sensor that recognises faces and say who it is.
+      {/if}
+    </p>
+  </section>
+
+  {#each people.people as p (p.key)}
+    <section class="card pad person">
+      <div class="card-title">
+        <div class="col" style="gap:2px">
+          {#if renaming === p.key}
+            <form class="row rename" onsubmit={(e) => rename(e, p)}>
+              <input class="input sm" bind:value={newName} maxlength="64" aria-label="Name" />
+              <button class="btn sm primary" type="submit">Save</button>
+              <button class="btn sm" type="button" onclick={() => (renaming = null)}>Cancel</button>
+            </form>
+          {:else}
+            <h3>{p.name} <span class="faint count">{p.faces.length}</span></h3>
+          {/if}
+          <span class="xsmall faint">
+            {seen(p)}
+            {#if p.faces.length < suggest} · teach a few more faces{/if}
+          </span>
+        </div>
+        <div class="row wrap actions">
+          {#if renaming !== p.key}
+            <button class="btn sm" onclick={() => startRename(p)}>Rename</button>
+          {/if}
+          <ConfirmButton
+            class="btn sm danger"
+            onconfirm={() => act(() => api.forgetPerson(p.key), `${p.name} removed, with their faces and entities`)}
+            confirmLabel="Tap again to remove"
+          >
+            <Icon name="trash" size={14} /> Remove person
+          </ConfirmButton>
+        </div>
+      </div>
+      {#each lookAlikes(p) as other (other)}
+        <div class="notice warn xsmall">
+          {p.name} and {other} look alike to the AI — teach both in more light and from more angles.
+        </div>
+      {/each}
+      {#if old(p)}
+        <div class="notice xsmall">Some faces were taught a long time ago. Add recent ones, above all for children.</div>
+      {/if}
+      <div class="faces">
+        {#each p.faces as f (f.id)}
+          <figure class:suspect={!!f.suspect}>
+            <img src={api.faceImageUrl(f.id)} alt="A face of {p.name}" loading="lazy" />
+            {#if f.is_night}<span class="badge">night</span>{/if}
+            <button
+              class="forget"
+              onclick={() => act(() => api.forgetFace(f.id), 'Face forgotten')}
+              aria-label="Forget this face"
+              title="Forget this face"
+            >
+              <Icon name="x" size={12} />
+            </button>
+            {#if f.suspect}
+              <button
+                class="btn sm move"
+                onclick={() => act(() => api.moveFace(f.id, f.suspect!), `Moved to ${nameOf(f.suspect!)}`)}
+              >
+                Looks like {nameOf(f.suspect)}: move
+              </button>
+            {/if}
+            <figcaption class="xsmall faint" title={dateTime(f.created_at)}>
+              {ago(f.created_at)}{f.sensor_name ? ` · ${f.sensor_name}` : ''}
+            </figcaption>
+          </figure>
+        {/each}
+      </div>
+    </section>
+  {/each}
+{/if}
+
+<style>
+  .count {
+    font-weight: 400;
+    margin-left: 4px;
+  }
+  .actions {
+    gap: var(--space-2);
+    justify-content: flex-end;
+  }
+  .rename {
+    gap: var(--space-2);
+  }
+  .rename input {
+    max-width: 180px;
+  }
+  .person {
+    display: flex;
+    flex-direction: column;
+    gap: var(--space-3);
+  }
+  .faces {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(84px, 1fr));
+    gap: var(--space-3);
+  }
+  figure {
+    position: relative;
+    margin: 0;
+    display: flex;
+    flex-direction: column;
+    gap: 4px;
+  }
+  figure img {
+    width: 100%;
+    aspect-ratio: 1;
+    object-fit: cover;
+    border-radius: var(--radius-sm);
+    background: var(--c-sunken);
+  }
+  figure.suspect img {
+    outline: 2px solid var(--c-warn);
+  }
+  .badge {
+    position: absolute;
+    left: 4px;
+    top: 4px;
+    padding: 0 6px;
+    border-radius: var(--radius-pill);
+    background: rgba(0, 0, 0, 0.6);
+    color: #fff;
+    font-size: var(--fs-xs);
+  }
+  .forget {
+    position: absolute;
+    right: 4px;
+    top: 4px;
+    width: 28px;
+    height: 28px;
+    border: 0;
+    border-radius: 50%;
+    background: rgba(0, 0, 0, 0.6);
+    color: #fff;
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    cursor: pointer;
+  }
+  .move {
+    white-space: normal;
+    height: auto;
+    min-height: 28px;
+    text-align: left;
+  }
+</style>
diff --git a/visionstate/frontend/src/lib/components/TeachLimitsEditor.svelte b/visionstate/frontend/src/lib/components/TeachLimitsEditor.svelte
index 5ee07e5..ae7e573 100644
--- a/visionstate/frontend/src/lib/components/TeachLimitsEditor.svelte
+++ b/visionstate/frontend/src/lib/components/TeachLimitsEditor.svelte
@@ -2,9 +2,9 @@
   // Edits the teaching limits of object sensors. An empty field inherits (shown in grey): on the
   // Settings page the state model's default, on a sensor the global value.
   import { app } from '../app.svelte';
-  import type { TeachKey, TeachLimits, TeachOverrides } from '../types';
+  import type { TeachLimits, TeachOverrides } from '../types';
   import { TEACH_FIELDS } from '../ui';
-  import Icon from './Icon.svelte';
+  import LimitsEditor from './LimitsEditor.svelte';
 
   let {
     value = $bindable(),
@@ -19,93 +19,26 @@
   } = $props();
 
   const limits = $derived(app.config?.teach.limits);
-  const pctOf = (n: number) => Math.round(n * 100);
-  const effective = (key: TeachKey) => value[key] ?? inherited[key];
-  const conflict = $derived(
-    effective('ask_similarity') > effective('match_similarity') || effective('keep_similarity') > effective('match_similarity'),
-  );
-
-  function set(key: TeachKey, raw: string) {
-    value[key] = raw.trim() === '' ? null : Number(raw) / 100;
-  }
+  const below = [
+    {
+      pairs: [
+        ['ask_similarity', 'match_similarity'],
+        ['keep_similarity', 'match_similarity'],
+      ],
+      message: 'The second and third values must not be above the first.',
+    },
+  ] as const;
 </script>
 
-<div class="col limits">
-  {#if limits}
-    {#each TEACH_FIELDS as f (f.key)}
-      {@const own = value[f.key] !== null && value[f.key] !== undefined}
-      <div class="line" data-testid={`teach-${f.key}`}>
-        <label class="col name" for={`teach-${f.key}`}>
-          <span>{f.label}</span>
-          <span class="xsmall faint">{f.help}</span>
-        </label>
-        <span class="row input-row">
-          <input
-            id={`teach-${f.key}`}
-            class="input sm num"
-            class:own
-            type="number"
-            min={pctOf(limits[f.key][0])}
-            max={pctOf(limits[f.key][1])}
-            step="1"
-            value={own ? pctOf(value[f.key] as number) : ''}
-            placeholder={String(pctOf(inherited[f.key]))}
-            oninput={(e) => set(f.key, e.currentTarget.value)}
-            aria-label={f.label}
-          />
-          <span class="unit small muted">%</span>
-          <button
-            class="btn sm ghost reset"
-            class:hidden={!own}
-            disabled={!own}
-            onclick={() => (value[f.key] = null)}
-            aria-label={`Use ${inheritedFrom} (${pctOf(inherited[f.key])}%)`}
-            title={`Use ${inheritedFrom}`}
-          >
-            <Icon name="x" size={14} />
-          </button>
-        </span>
-      </div>
-    {/each}
-  {/if}
-  {#if conflict}
-    <div class="notice warn small">The second and third values must not be above the first.</div>
-  {/if}
-  <p class="xsmall faint">Empty fields (in grey) use {inheritedFrom}. 100% is the very same picture.</p>
-</div>
-
-<style>
-  .limits {
-    gap: var(--space-3);
-  }
-  .line {
-    display: flex;
-    align-items: center;
-    justify-content: space-between;
-    gap: var(--space-4);
-    color: var(--c-text-2);
-  }
-  .name {
-    gap: 2px;
-    min-width: 0;
-  }
-  .input-row {
-    gap: var(--space-2);
-    flex-shrink: 0;
-  }
-  .num {
-    width: 72px;
-  }
-  .num::placeholder {
-    color: var(--c-faint);
-  }
-  .num:not(.own) {
-    color: var(--c-faint);
-  }
-  .unit {
-    width: 14px;
-  }
-  .reset.hidden {
-    visibility: hidden;
-  }
-</style>
+{#if limits}
+  <LimitsEditor
+    bind:value
+    {inherited}
+    {inheritedFrom}
+    fields={TEACH_FIELDS}
+    {limits}
+    {below}
+    id="teach"
+    note="100% is the very same picture."
+  />
+{/if}
diff --git a/visionstate/frontend/src/lib/components/TeachSheet.svelte b/visionstate/frontend/src/lib/components/TeachSheet.svelte
index 866081a..1cda811 100644
--- a/visionstate/frontend/src/lib/components/TeachSheet.svelte
+++ b/visionstate/frontend/src/lib/components/TeachSheet.svelte
@@ -2,12 +2,13 @@
   // Teach an object sensor about one box: right, not what the AI said, something else (one of its
   // objects or own labels, or a new own label), or — for a box the user drew — what the AI missed.
   // The first time a sensor is taught, it asks once whether to start. A bottom sheet on phones,
-  // a small dialog on wider screens.
+  // a small dialog on wider screens. On a sensor that recognises faces, a person box first asks
+  // "Who is this?" with a close-up of the face; a person is only ever taught from a usable face.
   import { onMount, untrack } from 'svelte';
   import { api } from '../api';
   import { app, toast, toastError } from '../app.svelte';
   import { pct } from '../format';
-  import { activeLabels, boxName, boxReason, noneLabel, objectName } from '../objects';
+  import { activeLabels, boxReason, byFace, facesOn, noneLabel, objectName, peopleOf } from '../objects';
   import type { Detection, Sensor, TeachInput } from '../types';
   import Icon from './Icon.svelte';
 
@@ -32,7 +33,7 @@
     ondone: () => void;
   } = $props();
 
-  type View = 'main' | 'other' | 'new' | 'confirm';
+  type View = 'main' | 'other' | 'new' | 'confirm' | 'person';
   let view = $state<View>(untrack(() => detection) ? 'main' : 'other'); // the sheet is made for one box
   let pending = $state<{ label?: string; new_label?: string; summary: string } | null>(null);
   let newName = $state('');
@@ -41,20 +42,55 @@
 
   const own = $derived(sensor.objects?.custom ?? []);
   const classes = $derived(sensor.objects?.classes ?? []);
-  const current = $derived(detection ? (detection.label ?? detection.key) : null);
-  const name = $derived(detection ? boxName(detection, own) : '');
+  // A box named by its face (a person, or the unknown person) is taught here as what it is: a person.
+  const current = $derived(
+    detection ? (detection.label && !byFace(detection.label, own) ? detection.label : detection.key) : null,
+  );
+  const name = $derived(current ? objectName(current, own) : '');
   const objectWord = $derived(detection ? objectName(detection.key).toLowerCase() : '');
   const firstTime = $derived((sensor.objects?.taught ?? 0) === 0);
-  const roomForLabel = $derived(own.length < (app.config?.teach.max_labels ?? 0));
-  // Everything the box could be instead: the sensor's objects and own labels, except what it is now.
+  const roomForLabel = $derived(own.filter((l) => !l.face).length < (app.config?.teach.max_labels ?? 0));
+  // Everything the box could be instead: the sensor's objects and own labels (people are taught by
+  // their face, not here), except what it is now.
   const options = $derived([
     ...classes.filter((k) => k !== current).map((k) => ({ key: k, text: objectName(k), sub: '' })),
     ...activeLabels(sensor)
-      .filter((l) => l.key !== current)
+      .filter((l) => l.key !== current && !l.face)
       .map((l) => ({ key: l.key, text: l.name, sub: `a kind of ${objectName(l.parent).toLowerCase()}` })),
   ]);
 
-  // Close-up of the box with some of its surroundings (needs the image's size for the shape).
+  // Faces: is there a usable face in this person box? Asked once when the sheet opens.
+  const faceMode = $derived(!!detection && detection.key === 'person' && !detection.filtered && facesOn(sensor));
+  const people = $derived(peopleOf(sensor));
+  const roomForPerson = $derived(people.length < (app.config?.faces.max_people ?? 0));
+  let face = $state<{ box: [number, number, number, number] | null; message: string } | null>(null);
+  let personName = $state('');
+  onMount(() => {
+    if (!untrack(() => faceMode)) return;
+    api
+      .checkFace(sensor.id, { ...source, box })
+      .then((r) => (face = { box: r.problem ? null : r.face, message: r.message }))
+      .catch((err) => (face = { box: null, message: err instanceof Error ? err.message : String(err) }));
+  });
+
+  async function teachPerson(person: { key?: string; name?: string }) {
+    busy = true;
+    try {
+      const body = { ...source, box, score: detection?.score ?? null };
+      const r = await api.teachFace(sensor.id, person.key ? { ...body, person: person.key } : { ...body, new_person: person.name });
+      const suggest = app.config?.faces.suggest_faces ?? 0;
+      const more = r.faces < suggest ? ' A few more, in other light and from other angles, make it reliable.' : '';
+      toast(`Got it: this is ${r.name} (${r.faces} face${r.faces === 1 ? '' : 's'}).${more}`);
+      ondone();
+      onclose();
+    } catch (err) {
+      toastError(err);
+      busy = false;
+    }
+  }
+
+  // Close-up of the box with some of its surroundings (needs the image's size for the shape); of
+  // the face, when one was found.
   let natural = $state<{ w: number; h: number } | null>(null);
   $effect(() => {
     if (!src) return;
@@ -63,7 +99,7 @@
     img.src = src;
   });
   const area = $derived.by(() => {
-    const [x1, y1, x2, y2] = box;
+    const [x1, y1, x2, y2] = face?.box ?? box;
     const mx = (x2 - x1) * 0.2;
     const my = (y2 - y1) * 0.2;
     return [Math.max(0, x1 - mx), Math.max(0, y1 - my), Math.min(1, x2 + mx), Math.min(1, y2 + my)];
@@ -160,7 +196,50 @@
         <button class="btn" onclick={onclose}>Cancel</button>
       </div>
     </div>
+  {:else if view === 'person'}
+    <form
+      class="col body"
+      onsubmit={(e) => {
+        e.preventDefault();
+        if (personName.trim()) teachPerson({ name: personName.trim() });
+      }}
+    >
+      <label class="field">
+        Who is this?
+        <!-- svelte-ignore a11y_autofocus -->
+        <input class="input" bind:value={personName} maxlength="64" placeholder="Name" autofocus />
+      </label>
+      <span class="xsmall faint">Gets an on/off sensor and a count in Home Assistant.</span>
+      <div class="row actions">
+        <button class="btn primary" type="submit" disabled={busy || !personName.trim()}>Add person</button>
+        <button class="btn" type="button" onclick={() => (view = 'main')}>Back</button>
+      </div>
+    </form>
   {:else if view === 'main' && detection}
+    {#if faceMode}
+      <div class="col body who">
+        <strong>Who is this?</strong>
+        {#if !face}
+          <span class="small muted">Looking for the face…</span>
+        {:else if !face.box}
+          <span class="small muted">{face.message}</span>
+        {:else}
+          <div class="row wrap people">
+            {#each people as p, i (p.key)}
+              <button class="btn chip-btn" class:first={i === 0} disabled={busy} onclick={() => teachPerson({ key: p.key })}>
+                {p.name}
+              </button>
+            {/each}
+            {#if roomForPerson}
+              <button class="btn chip-btn accent-outline" class:first={!people.length} disabled={busy} onclick={() => (view = 'person')}>
+                <Icon name="plus" size={14} /> New person…
+              </button>
+            {/if}
+          </div>
+          <span class="xsmall faint">Not someone you know? Close this.</span>
+        {/if}
+      </div>
+    {/if}
     <div class="col body">
       {#if detection.filtered}
         <button class="btn first choice" disabled={busy} onclick={() => choose({ label: detection.key }, `Got it: boxes like this count as ${objectWord} again.`)}>
@@ -282,6 +361,16 @@
   .body {
     gap: var(--space-2);
   }
+  .who {
+    padding-bottom: var(--space-3);
+    border-bottom: 1px solid var(--c-border);
+  }
+  .people {
+    gap: var(--space-2);
+  }
+  .chip-btn {
+    height: var(--touch);
+  }
   .choice {
     height: var(--touch);
     justify-content: flex-start;
diff --git a/visionstate/frontend/src/lib/components/history/ObjectRow.svelte b/visionstate/frontend/src/lib/components/history/ObjectRow.svelte
index 11d8f95..b99c152 100644
--- a/visionstate/frontend/src/lib/components/history/ObjectRow.svelte
+++ b/visionstate/frontend/src/lib/components/history/ObjectRow.svelte
@@ -1,7 +1,7 @@
 <script lang="ts">
   // A history row of an object sensor: an object appeared, cleared, was filtered away by what the
-  // sensor was taught, or the sensor asked about it. It opens to the frame with its boxes, which can
-  // be corrected there.
+  // sensor was taught, the sensor asked about it, or it saw a face nobody is named for. It opens to
+  // the frame with its boxes, which can be corrected (or a person named) there.
   import { api } from '../../api';
   import { dateTime, pct } from '../../format';
   import { objectColor, objectCount, objectKeys, objectName } from '../../objects';
@@ -29,7 +29,13 @@
     return [...counts].map(([key, n]) => objectCount(n, key, own)).join(', ');
   });
 
-  const WHAT: Record<string, string> = { on: 'detected', off: 'cleared', filtered: 'filtered away', ask: '? (asked in Review)' };
+  const WHAT: Record<string, string> = {
+    on: 'detected',
+    off: 'cleared',
+    filtered: 'filtered away',
+    ask: '? (asked in Review)',
+    face: '· face seen (tap the person to name them)',
+  };
 </script>
 
 <div class="card item">
diff --git a/visionstate/frontend/src/lib/format.ts b/visionstate/frontend/src/lib/format.ts
index 794cb7b..b798c22 100644
--- a/visionstate/frontend/src/lib/format.ts
+++ b/visionstate/frontend/src/lib/format.ts
@@ -20,6 +20,9 @@ export function dateTime(input: string): string {
 
 export const mb = (bytes: number) => `${Math.round(bytes / 1_000_000)} MB`;
 
+/** AI models from the smallest to the largest (their download size). */
+export const bySize = <T extends { size: number }>(models: readonly T[]): T[] => [...models].sort((a, b) => a.size - b.size);
+
 /** "12 kB", "820 MB", "1.4 GB" (binary units, like the backend's size limit). */
 export function size(bytes: number): string {
   if (bytes >= 1024 ** 3) return `${(bytes / 1024 ** 3).toFixed(1)} GB`;
diff --git a/visionstate/frontend/src/lib/objects.ts b/visionstate/frontend/src/lib/objects.ts
index 56ac17b..9d16982 100644
--- a/visionstate/frontend/src/lib/objects.ts
+++ b/visionstate/frontend/src/lib/objects.ts
@@ -1,12 +1,13 @@
 // Helpers for object sensors: display names and colours of object classes and own labels.
 import { app } from './app.svelte';
-import type { Detection, OwnLabel, Sensor } from './types';
+import type { Detection, FaceProblem, OwnLabel, Sensor } from './types';
 
 /**
  * Display name of an object class ("traffic_light" → "Traffic light"), from the backend label
  * list, or of one of a sensor's own labels ("Our car") when `own` is given.
  */
 export function objectName(key: string, own: OwnLabel[] = []): string {
+  if (key === app.config?.face_unknown_key) return 'Unknown person';
   return (
     own.find((l) => l.key === key)?.name ??
     app.config?.object_labels.find((l) => l.key === key)?.name ??
@@ -14,11 +15,46 @@ export function objectName(key: string, own: OwnLabel[] = []): string {
   );
 }
 
-/** What a sensor reports, in order: its classes, then its own labels in use. */
+/** What a sensor reports, in order: its classes, then its own labels in use (and, recognising
+ * faces, the unknown person). */
 export function objectKeys(sensor: Pick<Sensor, 'objects'>): string[] {
   const objects = sensor.objects;
   if (!objects) return [];
-  return [...objects.classes, ...activeLabels(sensor).map((l) => l.key)];
+  const unknown = facesOn(sensor) && app.config ? [app.config.face_unknown_key] : [];
+  return [...objects.classes, ...activeLabels(sensor).map((l) => l.key), ...unknown];
+}
+
+/** Whether an object sensor recognises people by their face (switched on, and it looks for people). */
+export const facesOn = (sensor: Pick<Sensor, 'objects'>) =>
+  !!sensor.objects?.faces?.enabled && sensor.objects.classes.includes('person');
+
+/** The people (shared by every sensor) among an object sensor's labels while it recognises faces. */
+export const peopleOf = (sensor: Pick<Sensor, 'objects'>): OwnLabel[] =>
+  (sensor.objects?.custom ?? []).filter((l) => l.face);
+
+/** Why the face of a person box cannot be used, in a few words. */
+export const FACE_PROBLEMS: Record<FaceProblem, string> = {
+  none: 'no face seen',
+  cut: 'face cut off by the edge',
+  small: 'face too small',
+  turned: 'face turned away',
+  blurry: 'face blurry',
+  unsure: 'face unclear',
+};
+
+/** What the face search says about a person box, in a few words ("" when nothing was looked at). */
+export function faceNote(d: Detection, own: OwnLabel[] = []): string {
+  const face = d.face;
+  if (!face) return '';
+  if (face.problem) {
+    const kept = face.kept && face.label ? `${objectName(face.label, own)} · ` : '';
+    return `${kept}${FACE_PROBLEMS[face.problem]}`;
+  }
+  if (face.state === 'match' && face.label) return `${objectName(face.label, own)}${face.kept ? ' (still here)' : ''}`;
+  if (face.state === 'pending') return 'checking who it is';
+  if (face.state === 'unclear') return 'not sure who';
+  if (face.state === 'ask' && face.candidate) return `maybe ${objectName(face.candidate, own)}`;
+  return own.some((l) => l.face) ? 'someone you have not named' : 'face seen';
 }
 
 /** Own labels whose object is still selected (the others are kept but not used). */
@@ -47,6 +83,7 @@ export function objectCount(n: number, key: string, own: OwnLabel[] = []): strin
   const label = own.find((l) => l.key === key);
   const name = label ? label.name : objectName(key).toLowerCase();
   if (n === 1 || label) return `${n} ${name}`;
+  if (name.endsWith('person')) return `${n} ${name.slice(0, -'person'.length)}people`; // "unknown people"
   let many = IRREGULAR_PLURALS[key];
   if (!many) {
     if (/(s|sh|ch|x)$/.test(name)) many = `${name}es`;
@@ -67,6 +104,10 @@ export function objectSummary(sensor: Sensor): string {
   return text.charAt(0).toUpperCase() + text.slice(1);
 }
 
+/** Whether a box's label comes from its face: a person taught by face, or the unknown person. */
+export const byFace = (label: string, own: OwnLabel[] = []) =>
+  label === app.config?.face_unknown_key || own.some((l) => l.key === label && l.face);
+
 /** The taught label of a box that is not what the detector said (from the backend config). */
 export const noneLabel = () => app.config?.teach.none_label ?? 'none';
 
@@ -75,6 +116,11 @@ export const boxName = (d: Detection, own: OwnLabel[] = []) => objectName(d.labe
 
 /** Why a box was changed by what the sensor was taught, in a few words ("" when it was not). */
 export function boxReason(d: Detection, own: OwnLabel[] = []): string {
+  if (d.label && byFace(d.label, own)) {
+    return d.label === app.config?.face_unknown_key
+      ? 'A face this sensor does not know'
+      : `Recognised by the face as ${objectName(d.label, own)}${d.face?.kept ? ' (still here, face not seen now)' : ''}`;
+  }
   if (d.filtered) {
     const taughtAs = d.match?.label;
     return taughtAs && taughtAs !== noneLabel()
diff --git a/visionstate/frontend/src/lib/router.svelte.ts b/visionstate/frontend/src/lib/router.svelte.ts
index 472b167..e15cd6c 100644
--- a/visionstate/frontend/src/lib/router.svelte.ts
+++ b/visionstate/frontend/src/lib/router.svelte.ts
@@ -23,6 +23,7 @@ export const paths = {
   sensor: (id: number, tab: SensorTab | '' = '') => (tab ? `sensors/${id}/${tab}` : `sensors/${id}`),
   sensorHome: (id: number, kind: SensorKind) => `sensors/${id}/${TABS_BY_KIND[kind][0]}`,
   review: () => 'review',
+  people: () => 'people',
   /** `query`: a history filter (lib/history.ts toQuery). */
   history: (query = '') => withQuery('history', query),
   settings: () => 'settings',
diff --git a/visionstate/frontend/src/lib/types.ts b/visionstate/frontend/src/lib/types.ts
index 1c29216..7f28000 100644
--- a/visionstate/frontend/src/lib/types.ts
+++ b/visionstate/frontend/src/lib/types.ts
@@ -175,15 +175,109 @@ export interface Detection {
   /** Kept its taught answer: the same object at the same place, a little less alike. */
   kept?: boolean;
   /** May be this own label, but not clearly: asked about in the review queue. */
-  ask?: { id: number; label: string; similarity: number };
+  ask?: { id: number | null; label: string; similarity: number; face?: boolean };
+  /** Person boxes of a sensor that recognises faces: what the face search found (see FaceInfo). */
+  face?: FaceInfo;
 }
 
-/** An object sensor's review question ("Is this Our car?"), in a history row's `probs.ask`. */
+/**
+ * The face search in one person box: `box` the face (normalised to the frame), `problem` why it
+ * cannot be used (null: usable) — 'none' no face, 'cut' by the frame's edge, 'small', 'turned',
+ * 'blurry', 'unsure'; `state` what a usable face is: 'match' (it is `label`), 'pending' (a second
+ * check must agree), 'ask' (maybe `candidate`), 'unknown', 'unclear' (matched nobody, but too small
+ * or turned to call a stranger).
+ */
+export interface FaceInfo {
+  box?: [number, number, number, number];
+  problem: FaceProblem | null;
+  face_px?: number; // the face's width in the frame, in pixels
+  state?: 'match' | 'pending' | 'ask' | 'unknown' | 'unclear';
+  label?: string;
+  candidate?: string | null;
+  similarity?: number;
+  kept?: boolean;
+}
+
+export type FaceProblem = 'none' | 'cut' | 'small' | 'turned' | 'blurry' | 'unsure';
+
+/** Recognising people by their face on an object sensor (defaults: config face_defaults), with its
+ * own face limits: an unset (or null) one follows the global value. */
+export interface FaceSettings extends FaceOverrides {
+  enabled: boolean;
+  /** Put faces nobody was taught for in the review queue ("Who is this?"). */
+  ask_unknown: boolean;
+}
+
+/** The tunable face limits: the smallest face (its width in pixels) and the similarities. */
+export type FaceKey = 'min_face_px' | 'match_similarity' | 'sure_similarity' | 'ask_similarity' | 'keep_similarity';
+export type FaceLimits = Record<FaceKey, number>;
+export type FaceOverrides = Partial<Record<FaceKey, number | null>>;
+
+/** GET /face-limits: the face model's defaults, what the user set and the result. */
+export interface FaceLimitsInfo {
+  model: string;
+  defaults: FaceLimits;
+  values: FaceOverrides;
+  effective: FaceLimits;
+}
+
+/** GET /people: the people (shared by every object sensor that recognises faces). */
+export interface People {
+  model: FaceStatus;
+  /** The sensors that recognise faces. */
+  sensors: number[];
+  people: Person[];
+  /** Pairs of people whose closest faces come near the match threshold. */
+  look_alikes: { a: string; b: string; similarity: number }[];
+}
+
+export interface Person {
+  key: string;
+  name: string;
+  /** Their own entity: the slug of the sensor that sees them now ('not_seen': none), and when and
+   * on which sensor they were last seen. */
+  seen: { now: string; at: number | null; sensor_id: number | null; sensor_name: string | null };
+  /** When the oldest taught face was taught. */
+  oldest: string | null;
+  /** `sensor_name`: the camera it was taught on (null once that sensor is deleted); `suspect`:
+   * the person a face is closer to than to its own (possibly the wrong name). */
+  faces: {
+    id: number;
+    origin: string;
+    is_night: boolean;
+    created_at: string;
+    sensor_id: number | null;
+    sensor_name: string | null;
+    suspect: string | null;
+  }[];
+}
+
+/** The face model: chosen, loaded, downloading (bytes) or why it failed. */
+export interface FaceStatus {
+  model: string;
+  loaded: boolean;
+  loading: boolean;
+  progress: { done: number; total: number } | null;
+  error: string;
+  downloaded: boolean;
+}
+
+export interface FaceModelInfo extends BackboneInfo {
+  /** What its makers say it was trained on. */
+  data: string;
+  detector: { name: string; license: string; data: string; source: string };
+}
+
+/** An object sensor's review question ("Is this Our car?"), in a history row's `probs.ask`. About a
+ * face ("Is this Kari?", "Who is this?" when label is the unknown key): `face` is the face's box. */
 export interface ObjectQuestion {
   label: string;
   similarity: number;
   box: [number, number, number, number];
   detected: string;
+  face?: [number, number, number, number];
+  /** About a face: the closest face taught as that person, to compare with. */
+  example?: number | null;
 }
 
 /** An own label of an object sensor: a kind of one of its objects, e.g. "Our car" (parent "car"). */
@@ -191,6 +285,8 @@ export interface OwnLabel {
   key: string;
   name: string;
   parent: string;
+  /** A person (shared by every sensor, GET /people): added by the backend while the sensor recognises faces. */
+  face?: boolean;
 }
 
 export interface ObjectSettings {
@@ -201,6 +297,7 @@ export interface ObjectSettings {
   use_taught: boolean;
   /** This sensor's own teaching limits; a missing one follows the global value. */
   teach?: TeachOverrides;
+  faces?: FaceSettings;
 }
 
 export type TeachKey = 'match_similarity' | 'keep_similarity' | 'ask_similarity' | 'rescue_similarity' | 'rescue_floor';
@@ -230,9 +327,19 @@ export interface SensorObjects extends ObjectSettings {
   custom: OwnLabel[];
   live: ObjectLive[];
   detections: Detection[];
-  /** Boxes taught so far, and the objects whose boxes are compared with them. */
+  /** Boxes taught so far, faces taught on this camera (the people are shared: GET /people), and the
+   * objects whose boxes are compared with the boxes. */
   taught: number;
+  taught_faces: number;
   taught_keys: string[];
+  /** Of the last person boxes looked at for a face, how many had a usable one, and why the others not. */
+  face_stats: {
+    looked: number;
+    usable: number;
+    problems: Partial<Record<FaceProblem, number>>;
+    frame: [number, number] | null; // width and height of the last frame looked at
+    min_face_px: number | null; // the smallest face it was looked at with
+  };
 }
 
 /** One taught box; label "none" = not what the detector said, detected null = a box it missed. */
@@ -386,6 +493,11 @@ export interface AppConfig {
   object_popular: string[];
   /** Teaching object sensors: the label of a box that is not what the detector said. */
   teach: { none_label: string; max_labels: number; tunable: TeachKey[]; limits: Record<TeachKey, [number, number]> };
+  face_defaults: FaceSettings;
+  face_limits: Record<FaceKey, [number, number]>;
+  /** The key (and entity) of a face nobody was taught for. */
+  face_unknown_key: string;
+  faces: { max_people: number; max_faces_per_person: number; suggest_faces: number; stats_window: number };
   reading_sensor_defaults: { interval_s: number; threshold: number; debounce: number };
   reading_defaults: ReadingSettings;
   reading_limits: Record<'decimals' | 'digits' | 'max_step' | 'spot_rate' | 'rate_window_min', [number, number]>;
@@ -422,6 +534,8 @@ export interface Status {
   wheel_reader: string | null;
   wheel_reader_name: string | null;
   wheel_reader_error: string;
+  /** The face model, and how many people and sensors that recognise faces there are. */
+  faces: FaceStatus & { people: number; sensors: number };
   mqtt: { connected: boolean; host: string | null; error: string };
   home_assistant: boolean;
   ha_events: { enabled: boolean; connected: boolean; entities: number; error: string };
@@ -463,7 +577,8 @@ export interface Prediction {
   review_reason: 'low_confidence' | 'flip' | 'spot_check' | 'rejected' | 'ask' | null;
   reviewed: boolean;
   has_frame: boolean;
-  /** Object sensors: state_key = the class or own label, published_key = 'on' | 'off' | 'filtered' | 'ask'. */
+  /** Object sensors: state_key = the class or own label, published_key = 'on' | 'off' | 'filtered' | 'ask' |
+   * 'face' (a face nobody is named for). */
   detections: Detection[] | null;
   /** Reading sensors: did the reader read the right number (null = not verified)? */
   read_ok: boolean | null;
@@ -541,6 +656,10 @@ export interface BackboneInfo {
   name: string;
   description: string;
   installed: boolean;
+  /** Downloaded (not included in the app): it can be deleted while not in use. */
+  downloaded: boolean;
+  /** Why it cannot be deleted now ("" if it can). */
+  in_use: string;
   size: number;
   license: string;
   source: string;
@@ -551,6 +670,9 @@ export interface BackboneInfo {
   accuracy: number;
 }
 
+/** The AI models a user chooses, by their list in GET /settings. */
+export type ModelKind = 'backbones' | 'detectors' | 'face_models' | 'readers';
+
 /** GET /storage: disk use and the history limits (whichever is reached first applies). */
 export interface StorageInfo {
   history_bytes: number;
@@ -576,6 +698,9 @@ export interface SettingsInfo {
   /** The wheel reader for mechanical counters (one model for now, nothing to choose). */
   wheel_reader: string;
   wheel_readers: DetectorInfo[];
+  /** Recognising faces on object sensors: the recogniser chosen; downloaded when a sensor turns faces on. */
+  face_model: string;
+  face_models: FaceModelInfo[];
   options: Record<string, string | number>;
 }
 
diff --git a/visionstate/frontend/src/lib/ui.ts b/visionstate/frontend/src/lib/ui.ts
index b257916..10f0360 100644
--- a/visionstate/frontend/src/lib/ui.ts
+++ b/visionstate/frontend/src/lib/ui.ts
@@ -87,6 +87,41 @@ export const TEACH_FIELDS = [
   },
 ] as const;
 
+/** The wizard warns of a low-resolution stream: a picture whose longest side is below this, or a
+ * camera entity or URL whose name says so (Reolink "fluent", "sub" streams, "low"). */
+export const SMALL_FRAME_PX = 1280;
+export const LOW_STREAM_NAME = /(^|[^a-z])(fluent|sub|low|lowres)([^a-z]|$)/i;
+
+/** The faces' limits, on the Settings page and an object sensor's Faces card (in this order). */
+export const FACE_FIELDS = [
+  {
+    key: 'min_face_px',
+    label: 'Smallest face',
+    help: 'Its width in the picture. Smaller faces are not used: they are easily mistaken.',
+    unit: 'px',
+  },
+  {
+    key: 'match_similarity',
+    label: 'Recognised as a person from',
+    help: 'How close a face must be to the faces you taught; a second check must agree.',
+  },
+  {
+    key: 'sure_similarity',
+    label: 'Sure at once from',
+    help: 'Named in the first check, without waiting for a second one.',
+  },
+  {
+    key: 'ask_similarity',
+    label: 'Asks you in the review queue from',
+    help: 'Between this and the first similarity: "Is this Kari?"',
+  },
+  {
+    key: 'keep_similarity',
+    label: 'Keeps the name, while they stay, down to',
+    help: 'Someone who stays where they were keeps their name as their face turns.',
+  },
+] as const;
+
 /** The model comparison under Settings → AI model: one row per kind, in the order shown. */
 export const MODEL_KINDS = [
   {
@@ -99,6 +134,11 @@ export const MODEL_KINDS = [
     title: 'Object sensors',
     text: 'Finds people, vehicles and animals. Larger models find more small, far-off and partly hidden objects.',
   },
+  {
+    key: 'face_models',
+    title: 'Faces (object sensors)',
+    text: 'Tells who a person is from their face; downloaded when a sensor recognises faces. Its size includes the face finder.',
+  },
   { key: 'readers', title: 'Displays', text: 'Reads the digits of a display; small helps on unusual fonts.' },
   { key: 'wheel_readers', title: 'Mechanical counters', text: 'Reads each wheel of a counter.' },
 ] as const;
@@ -233,6 +273,7 @@ export const HISTORY_EVENTS: Record<string, string> = {
   cleared: 'Cleared',
   filtered: 'Filtered away',
   asked: 'Asked “is it yours?”',
+  face_seen: 'Face seen, not named',
   value: 'New value',
   rejected: 'Rejected reading',
   verified: 'Checked by hand',
diff --git a/visionstate/frontend/src/pages/NewSensor.svelte b/visionstate/frontend/src/pages/NewSensor.svelte
index bfc2373..ad5b8eb 100644
--- a/visionstate/frontend/src/pages/NewSensor.svelte
+++ b/visionstate/frontend/src/pages/NewSensor.svelte
@@ -15,7 +15,7 @@
   import { isPolygon, toRectangle } from '../lib/roi';
   import { go, href, paths } from '../lib/router.svelte';
   import type { ReadingSettings, Roi, SensorKind, Triggers } from '../lib/types';
-  import { SENSOR_KIND_INFO } from '../lib/ui';
+  import { LOW_STREAM_NAME, SENSOR_KIND_INFO, SMALL_FRAME_PX } from '../lib/ui';
   import ObjectsStep from './wizard/ObjectsStep.svelte';
   import ReadingStep from './wizard/ReadingStep.svelte';
   import StatesStep from './wizard/StatesStep.svelte';
@@ -35,8 +35,12 @@
   let kind = $state<SensorKind>('single_state');
   let states = $state<{ name: string; color?: string }[]>([{ name: 'Open' }, { name: 'Closed' }]);
   let classes = $state<string[]>([...(app.config?.object_defaults.classes ?? [])]);
+  let faces = $state(false); // recognise people by their face (objects with Person)
   let reading = $state<ReadingSettings>({ ...app.config!.reading_defaults });
   let previewUrl = $state<string | null>(null);
+  let previewSize = $state<[number, number] | null>(null);
+  // A small picture, or a source named like a sub-stream: maybe the camera's low-resolution stream.
+  const lowStream = $derived(previewSize !== null && (Math.max(...previewSize) < SMALL_FRAME_PX || LOW_STREAM_NAME.test(source)));
   let previewError = $state('');
   let saving = $state(false);
   // Start from the backend defaults (GET /config), the same values a sensor gets when this step is skipped.
@@ -88,9 +92,13 @@
   function loadPreview() {
     previewError = '';
     previewUrl = null;
+    previewSize = null;
     const url = api.previewUrl(sourceType, source);
     const img = new Image();
-    img.onload = () => (previewUrl = url);
+    img.onload = () => {
+      previewUrl = url;
+      previewSize = [img.naturalWidth, img.naturalHeight];
+    };
     img.onerror = () => (previewError = 'Could not get a frame from this source. Check the camera or URL.');
     img.src = url;
   }
@@ -111,7 +119,15 @@
         source: source.trim(),
         roi,
         states: learned ? states.map((s) => ({ name: s.name.trim(), color: s.color })) : [],
-        ...(kind === 'objects' ? { objects: { ...app.config!.object_defaults, classes } } : {}),
+        ...(kind === 'objects'
+          ? {
+              objects: {
+                ...app.config!.object_defaults,
+                classes,
+                faces: { ...app.config!.face_defaults, enabled: faces && classes.includes('person') },
+              },
+            }
+          : {}),
         ...(kind === 'reading' ? { reading } : {}),
         interval_s,
         triggers,
@@ -182,6 +198,16 @@
         {#if previewError}
           <div class="notice danger"><Icon name="alert" />{previewError}</div>
         {:else}
+          {#if lowStream && previewSize}
+            <div class="notice warn small" data-testid="low-stream">
+              <Icon name="alert" />
+              <span>
+                This picture is {previewSize[0]}×{previewSize[1]}. If the camera has a higher resolution, this may be its
+                low-resolution stream: pick its main stream instead, which Home Assistant often hides (a disabled camera
+                entity of the same device).
+              </span>
+            </div>
+          {/if}
           <div class="frame">
             <RoiEditor src={previewUrl} bind:roi editable />
           </div>
@@ -215,7 +241,7 @@
         {#if kind === 'reading'}
           <ReadingStep bind:this={detectStep} {sourceType} {source} {entity} bind:roi bind:reading />
         {:else if kind === 'objects'}
-          <ObjectsStep bind:this={detectStep} {sourceType} {source} {name} bind:roi bind:classes />
+          <ObjectsStep bind:this={detectStep} {sourceType} {source} {name} bind:roi bind:classes bind:faces />
         {:else}
           <StatesStep {entity} {stateKeys} bind:states />
         {/if}
diff --git a/visionstate/frontend/src/pages/People.svelte b/visionstate/frontend/src/pages/People.svelte
new file mode 100644
index 0000000..e445ad5
--- /dev/null
+++ b/visionstate/frontend/src/pages/People.svelte
@@ -0,0 +1,65 @@
+<script lang="ts">
+  // The people VisionState knows by their face, shared by every object sensor that recognises
+  // faces: their faces (from every camera), where they were seen, renaming and forgetting. Each
+  // person also has their own Home Assistant entity, sensor.visionstate_<key>: the sensor that
+  // sees them now, or not_seen.
+  import { api } from '../lib/api';
+  import { app, refreshStatus, toast, toastError } from '../lib/app.svelte';
+  import ConfirmButton from '../lib/components/ConfirmButton.svelte';
+  import Icon from '../lib/components/Icon.svelte';
+  import PeopleCards from '../lib/components/PeopleCards.svelte';
+
+  let version = $state(0); // a new one reloads the cards
+
+  const faces = $derived(app.status?.faces);
+
+  async function forgetEveryone() {
+    try {
+      await api.forgetPeople();
+      toast('Everyone is forgotten, with their faces and entities');
+      version += 1;
+      refreshStatus();
+    } catch (err) {
+      toastError(err);
+    }
+  }
+</script>
+
+<div class="page">
+  <h1>People</h1>
+  <p class="muted intro">
+    The people you named by their face. Every camera that recognises faces knows them: each gets an on/off sensor
+    there, and their own sensor in Home Assistant (<span class="mono">sensor.visionstate_…</span>) says which camera
+    sees them now.
+  </p>
+  {#if faces && !faces.sensors}
+    <div class="notice">
+      No sensor recognises faces yet: turn it on under an object sensor's Settings → Faces (one that looks for Person).
+    </div>
+  {/if}
+  {#key version}
+    <div class="col cards"><PeopleCards /></div>
+  {/key}
+  {#if faces?.people}
+    <div class="row wrap forget">
+      <ConfirmButton class="btn sm danger" onconfirm={forgetEveryone} confirmLabel="Tap again to forget everyone">
+        <Icon name="trash" size={14} /> Forget everyone
+      </ConfirmButton>
+      <span class="xsmall faint">Every person, their faces and their entities, on every camera.</span>
+    </div>
+  {/if}
+</div>
+
+<style>
+  .intro {
+    max-width: 720px;
+  }
+  .cards {
+    gap: var(--space-4);
+    max-width: 900px;
+  }
+  .forget {
+    gap: var(--space-2);
+    margin-top: var(--space-6);
+  }
+</style>
diff --git a/visionstate/frontend/src/pages/Review.svelte b/visionstate/frontend/src/pages/Review.svelte
index 25db284..6fdafe2 100644
--- a/visionstate/frontend/src/pages/Review.svelte
+++ b/visionstate/frontend/src/pages/Review.svelte
@@ -1,8 +1,9 @@
 <script lang="ts">
   import { api } from '../lib/api';
-  import { refreshStatus, stateInfo, toast, toastError } from '../lib/app.svelte';
+  import { app, refreshStatus, stateInfo, toast, toastError } from '../lib/app.svelte';
   import ConfirmButton from '../lib/components/ConfirmButton.svelte';
   import DetectionBoxes from '../lib/components/DetectionBoxes.svelte';
+  import FaceQuestion from '../lib/components/FaceQuestion.svelte';
   import Icon from '../lib/components/Icon.svelte';
   import ReadingVerdict from '../lib/components/ReadingVerdict.svelte';
   import RoiEditor from '../lib/components/RoiEditor.svelte';
@@ -48,6 +49,7 @@
   /** Sidebar line: what was read, or the state the AI predicted. */
   const summary = (item: ReviewItem) => {
     const q = question(item);
+    if (q?.face && q.label === app.config?.face_unknown_key) return 'Who is this?';
     if (q) return `${labelName(item, q.label)}?`;
     return isReading(item) ? `“${readingDetail(item).text || '—'}”` : `${stateInfo(item.sensor, item.state_key).name} ${pct(item.confidence)}`;
   };
@@ -142,6 +144,7 @@
   function onkey(e: KeyboardEvent) {
     if (!current || isReading(current) || (e.target as HTMLElement).closest('input, select, textarea')) return;
     if (e.key === 'Escape') editing = null;
+    else if (question(current)?.face) return; // FaceQuestion has its own keys
     else if (question(current)) {
       if (e.key === 'Enter') answerQuestion('yes');
       else if (e.key.toLowerCase() === 'n') answerQuestion('no');
@@ -255,6 +258,19 @@
             <ReadingVerdict item={current} reading={current.sensor.reading} unit={unitOf(current)} large onanswer={readingAnswered} />
           {/key}
         </div>
+      {:else if current && question(current)?.face}
+        {@render header(current)}
+        {#key current.id}
+          <FaceQuestion
+            item={current}
+            question={question(current)!}
+            given={current.id in given ? given[current.id] : undefined}
+            onanswer={(text, answer) => {
+              if (current) given[current.id] = answer;
+              answered(text);
+            }}
+          />
+        {/key}
       {:else if current && question(current)}
         {@const q = question(current)!}
         {@const name = labelName(current, q.label)}
diff --git a/visionstate/frontend/src/pages/SensorPage.svelte b/visionstate/frontend/src/pages/SensorPage.svelte
index a686d12..27861f7 100644
--- a/visionstate/frontend/src/pages/SensorPage.svelte
+++ b/visionstate/frontend/src/pages/SensorPage.svelte
@@ -2,6 +2,7 @@
   import { onDestroy } from 'svelte';
   import { api } from '../lib/api';
   import { shownConfidence, stateInfo, toast, toastError } from '../lib/app.svelte';
+  import ExportButton from '../lib/components/ExportButton.svelte';
   import Icon from '../lib/components/Icon.svelte';
   import HistoryView from '../lib/components/history/HistoryView.svelte';
   import ObjectChips from '../lib/components/ObjectChips.svelte';
@@ -95,7 +96,7 @@
         </div>
         <span class="spacer"></span>
         <div class="row actions">
-          <a class="btn sm" href={api.exportUrl(sensor.id)} download><Icon name="download" size={14} /> Export</a>
+          <ExportButton {sensor} />
           <button class="btn sm" onclick={togglePause}>
             <Icon name={sensor.enabled ? 'pause' : 'play'} size={14} />
             {sensor.enabled ? 'Pause' : 'Resume'}
diff --git a/visionstate/frontend/src/pages/Settings.svelte b/visionstate/frontend/src/pages/Settings.svelte
index c530e8b..165305d 100644
--- a/visionstate/frontend/src/pages/Settings.svelte
+++ b/visionstate/frontend/src/pages/Settings.svelte
@@ -1,20 +1,30 @@
 <script lang="ts">
   import { api } from '../lib/api';
   import { app, refreshStatus, toast, toastError } from '../lib/app.svelte';
+  import FaceLimitsEditor from '../lib/components/FaceLimitsEditor.svelte';
   import Icon from '../lib/components/Icon.svelte';
   import ModelCompare from '../lib/components/ModelCompare.svelte';
   import ReminderCard from '../lib/components/ReminderCard.svelte';
   import ReviewRulesEditor from '../lib/components/ReviewRulesEditor.svelte';
   import StorageCard from '../lib/components/StorageCard.svelte';
   import TeachLimitsEditor from '../lib/components/TeachLimitsEditor.svelte';
-  import { mb } from '../lib/format';
+  import { bySize, mb, size } from '../lib/format';
   import { go, paths } from '../lib/router.svelte';
-  import type { ReviewRules, SettingsInfo, TeachLimitsInfo, TeachOverrides } from '../lib/types';
+  import type {
+    FaceLimitsInfo,
+    FaceOverrides,
+    FaceStatus,
+    ReviewRules,
+    SettingsInfo,
+    TeachLimitsInfo,
+    TeachOverrides,
+  } from '../lib/types';
 
   let info = $state<SettingsInfo | null>(null);
   let backbone = $state('');
   let detector = $state('');
   let reader = $state('');
+  let faceModel = $state('');
   let saving = $state(false);
   let importInput: HTMLInputElement;
   let reviewRules = $state<ReviewRules | null>(null);
@@ -24,6 +34,34 @@
   let savingTeach = $state(false);
   const setOnly = (v: TeachOverrides) => JSON.stringify(Object.entries(v).filter(([, x]) => x !== null && x !== undefined).sort());
   const teachChanged = $derived(teach !== null && setOnly(teachValues) !== setOnly(teach.values));
+  let faceLimits = $state<FaceLimitsInfo | null>(null);
+  let faceValues = $state<FaceOverrides>({});
+  let savingFaces = $state(false);
+  const facesChanged = $derived(faceLimits !== null && setOnly(faceValues) !== setOnly(faceLimits.values));
+
+  function loadFaceLimits() {
+    api
+      .faceLimits()
+      .then((f) => {
+        faceLimits = f;
+        faceValues = { ...f.values };
+      })
+      .catch(toastError);
+  }
+  loadFaceLimits();
+
+  async function saveFaceLimits() {
+    savingFaces = true;
+    try {
+      faceLimits = await api.saveFaceLimits(faceValues);
+      faceValues = { ...faceLimits.values };
+      toast('Face limits saved');
+    } catch (err) {
+      toastError(err);
+    } finally {
+      savingFaces = false;
+    }
+  }
 
   function loadTeach() {
     api
@@ -74,6 +112,7 @@
       backbone = s.backbone;
       detector = s.detector;
       reader = s.reader;
+      faceModel = s.face_model;
     })
     .catch(toastError);
 
@@ -81,17 +120,21 @@
   const selectedDetector = $derived(info?.detectors.find((d) => d.id === detector));
   const selectedReader = $derived(info?.readers.find((r) => r.id === reader));
   const wheelReader = $derived(info?.wheel_readers.find((r) => r.id === info?.wheel_reader));
+  const selectedFace = $derived(info?.face_models.find((m) => m.id === faceModel));
   const retrains = $derived(info !== null && backbone !== info.backbone);
-  const changed = $derived(info !== null && (retrains || detector !== info.detector || reader !== info.reader));
+  const changed = $derived(
+    info !== null && (retrains || detector !== info.detector || reader !== info.reader || faceModel !== info.face_model),
+  );
 
   async function save() {
     saving = true;
     try {
       const retrained = retrains;
-      info = await api.saveSettings({ backbone, detector, reader });
+      info = await api.saveSettings({ backbone, detector, reader, face_model: faceModel });
       toast(retrained ? 'AI model changed — state sensors are retraining' : 'AI models changed');
       refreshStatus();
       if (retrained) loadTeach(); // the defaults depend on the state model
+      loadFaceLimits(); // and the face limits' on the face model
     } catch (err) {
       toastError(err);
     } finally {
@@ -113,6 +156,14 @@
     }
   }
 
+  function faceStatus(f: FaceStatus): string {
+    if (f.error) return f.error;
+    if (f.progress) return `downloading… ${size(f.progress.done)} of ${size(f.progress.total)}`;
+    if (f.loaded) return info?.face_models.find((m) => m.id === f.model)?.name ?? f.model;
+    if (f.loading) return 'loading…';
+    return 'loaded when a sensor recognises faces';
+  }
+
   const statusRows = $derived(
     app.status
       ? [
@@ -154,6 +205,11 @@
             value: app.status.wheel_reader_error || app.status.wheel_reader_name || 'loaded when a mechanical counter is read',
             ok: !app.status.wheel_reader_error,
           },
+          {
+            label: 'Face model',
+            value: faceStatus(app.status.faces),
+            ok: !app.status.faces.error,
+          },
         ]
       : [],
   );
@@ -179,7 +235,7 @@
         <label class="field">
           State sensors (learned)
           <select class="input" bind:value={backbone}>
-            {#each info.backbones as b (b.id)}
+            {#each bySize(info.backbones) as b (b.id)}
               <option value={b.id}>{b.name}{b.installed ? '' : ` · download ${mb(b.size)}`}</option>
             {/each}
           </select>
@@ -188,7 +244,7 @@
         <label class="field">
           Object sensors
           <select class="input" bind:value={detector}>
-            {#each info.detectors as d (d.id)}
+            {#each bySize(info.detectors) as d (d.id)}
               <option value={d.id}>{d.name}{d.installed ? '' : ` · download ${mb(d.size)}`}</option>
             {/each}
           </select>
@@ -199,10 +255,26 @@
             >
           {/if}
         </label>
+        <label class="field">
+          Faces (object sensors)
+          <select class="input" bind:value={faceModel}>
+            {#each bySize(info.face_models) as m (m.id)}
+              <option value={m.id}>{m.name}{m.installed ? '' : ` · download ${mb(m.size)}`}</option>
+            {/each}
+          </select>
+          {#if selectedFace}
+            <span class="hint"
+              >{selectedFace.description} {selectedFace.license}, <a href={selectedFace.source} target="_blank" rel="noreferrer">source</a>;
+              {selectedFace.data} Faces are found with {selectedFace.detector.name} ({selectedFace.detector.license},
+              <a href={selectedFace.detector.source} target="_blank" rel="noreferrer">source</a>). Downloaded when a sensor turns
+              face recognition on; changing it compares every taught face again (the people are kept).</span
+            >
+          {/if}
+        </label>
         <label class="field">
           Reading sensors: displays
           <select class="input" bind:value={reader}>
-            {#each info.readers as r (r.id)}
+            {#each bySize(info.readers) as r (r.id)}
               <option value={r.id}>{r.name}{r.installed ? '' : ` · download ${mb(r.size)}`}</option>
             {/each}
           </select>
@@ -231,7 +303,7 @@
           </button>
           <span class="xsmall faint">A new state model retrains every state sensor from its stored images. Object and reading sensors need no training.</span>
         </div>
-        <ModelCompare {info} />
+        <ModelCompare {info} ondeleted={(next) => (info = next)} />
       {/if}
     </section>
 
@@ -268,6 +340,27 @@
       {/if}
     </section>
 
+    <section class="card pad col" data-testid="face-limits">
+      <h3>Recognising faces</h3>
+      <p class="small muted">
+        For object sensors that recognise faces. Each sensor can override these on its Settings tab (Faces).
+      </p>
+      {#if faceLimits}
+        <FaceLimitsEditor
+          bind:value={faceValues}
+          inherited={faceLimits.defaults}
+          inheritedFrom={`the default of ${info?.face_models.find((m) => m.id === faceLimits?.model)?.name ?? 'the face model'}`}
+        />
+        <p class="xsmall faint">
+          The similarities' defaults follow the face model. Values you set yourself stay when you switch it; check them
+          afterwards.
+        </p>
+        <button class="btn primary" style="align-self:flex-start" disabled={!facesChanged || savingFaces} onclick={saveFaceLimits}>
+          {savingFaces ? 'Saving…' : 'Save face limits'}
+        </button>
+      {/if}
+    </section>
+
     <ReminderCard />
 
     <StorageCard />
diff --git a/visionstate/frontend/src/pages/sensor/LiveTab.svelte b/visionstate/frontend/src/pages/sensor/LiveTab.svelte
index caf15da..f2b05e6 100644
--- a/visionstate/frontend/src/pages/sensor/LiveTab.svelte
+++ b/visionstate/frontend/src/pages/sensor/LiveTab.svelte
@@ -5,7 +5,8 @@
   import AnalysedFrame from '../../lib/components/AnalysedFrame.svelte';
   import Icon from '../../lib/components/Icon.svelte';
   import { ago, pct, plural } from '../../lib/format';
-  import { objectColor, objectCount, objectKeys, objectName } from '../../lib/objects';
+  import FaceStats from '../../lib/components/FaceStats.svelte';
+  import { faceNote, facesOn, objectColor, objectCount, objectKeys, objectName } from '../../lib/objects';
   import { href, paths } from '../../lib/router.svelte';
   import type { Detection, Sensor } from '../../lib/types';
   import { TRIGGER_SOURCES } from '../../lib/ui';
@@ -21,6 +22,11 @@
     keys.map((key) => ({ key, n: objects.detections.filter((d) => counts(d, key)).length })).filter((x) => x.n > 0),
   );
   const filteredAway = $derived(objects.detections.filter((d) => d.filtered).length);
+  // Recognising faces: what the face search said about each person box of the last check.
+  const faces = $derived(facesOn(sensor));
+  const faceNotes = $derived(
+    faces ? objects.detections.filter((d) => d.face && !d.filtered).map((d) => faceNote(d, objects.custom)) : [],
+  );
   // Each class, followed by its own labels ("Rex" under "Dog").
   const rows = $derived(
     objects.live
@@ -87,6 +93,12 @@
           </li>
         {/each}
       </ul>
+      {#if faces}
+        <div class="faces">
+          {#each faceNotes as note, i (i)}<span class="xsmall muted">Person · {note}</span>{/each}
+          <FaceStats {objects} />
+        </div>
+      {/if}
       <p class="xsmall faint" style="margin-top:12px">
         An object is reported after {plural(sensor.debounce, 'check')} with it and cleared {objects.clear_after_s} s after it
         was last seen. Boxes below {pct(sensor.threshold)} are ignored.
@@ -174,6 +186,14 @@
     border: 1px solid var(--c-border-strong);
     color: var(--c-muted);
   }
+  .faces {
+    display: flex;
+    flex-direction: column;
+    gap: 4px;
+    margin-top: var(--space-3);
+    padding-top: var(--space-3);
+    border-top: 1px solid var(--c-border);
+  }
   .trigger {
     display: flex;
     flex-direction: column;
diff --git a/visionstate/frontend/src/pages/sensor/ObjectQualityTab.svelte b/visionstate/frontend/src/pages/sensor/ObjectQualityTab.svelte
index 17bfb2a..69660fb 100644
--- a/visionstate/frontend/src/pages/sensor/ObjectQualityTab.svelte
+++ b/visionstate/frontend/src/pages/sensor/ObjectQualityTab.svelte
@@ -6,6 +6,7 @@
   import { toast, toastError } from '../../lib/app.svelte';
   import ConfirmButton from '../../lib/components/ConfirmButton.svelte';
   import Icon from '../../lib/components/Icon.svelte';
+  import PeopleCards from '../../lib/components/PeopleCards.svelte';
   import TeachableFrame from '../../lib/components/TeachableFrame.svelte';
   import { ago, dateTime, pct } from '../../lib/format';
   import { noneLabel, objectName } from '../../lib/objects';
@@ -32,6 +33,8 @@
   });
 
   const own = $derived(sensor.objects?.custom ?? []);
+  // Boxes taught by their look (faces are under People): only then is "What you taught" shown.
+  const boxesTaught = $derived((sensor.objects?.taught ?? 0) > 0 || own.some((l) => !l.face));
   const compared = $derived((sensor.objects?.taught_keys ?? []).map((k) => objectName(k).toLowerCase()));
 
   interface Group {
@@ -46,7 +49,7 @@
   const groups = $derived.by(() => {
     if (!taught) return [] as Group[];
     const result = new Map<string, Group>();
-    for (const label of taught.labels) {
+    for (const label of taught.labels.filter((l) => !l.face)) {
       result.set(label.key, {
         id: label.key,
         title: label.name,
@@ -106,23 +109,26 @@
 
 <div class="layout">
   <div class="col" style="gap:var(--space-5)">
-    <section class="card pad">
-      <div class="card-title"><h3>What you taught</h3></div>
-      {#if taught && !taught.use_taught}
-        <div class="notice warn" style="margin-bottom:12px">
-          Turned off under <a href={href(paths.sensor(sensor.id, 'settings'))}>Settings</a>: the AI alone decides until you turn it on again.
-        </div>
-      {/if}
-      <p class="small muted">
-        {#if compared.length}
-          Each {compared.join(', ')} the AI finds is compared with these boxes. When it clearly looks like one of them it
-          gets that answer; otherwise the AI's own answer stands.
-        {:else}
-          Nothing to compare with yet.
+    <PeopleCards {sensor} {onchange} />
+    {#if boxesTaught}
+      <section class="card pad">
+        <div class="card-title"><h3>What you taught</h3></div>
+        {#if taught && !taught.use_taught}
+          <div class="notice warn" style="margin-bottom:12px">
+            Turned off under <a href={href(paths.sensor(sensor.id, 'settings'))}>Settings</a>: the AI alone decides until you turn it on again.
+          </div>
         {/if}
-        Tap a box on the <a href={href(paths.sensor(sensor.id, 'live'))}>Live</a> tab or in the history to teach more.
-      </p>
-    </section>
+        <p class="small muted">
+          {#if compared.length}
+            Each {compared.join(', ')} the AI finds is compared with these boxes. When it clearly looks like one of them it
+            gets that answer; otherwise the AI's own answer stands.
+          {:else}
+            Nothing to compare with yet.
+          {/if}
+          Tap a box on the <a href={href(paths.sensor(sensor.id, 'live'))}>Live</a> tab or in the history to teach more.
+        </p>
+      </section>
+    {/if}
 
     {#if taught === null}
       <p class="muted">Loading…</p>
diff --git a/visionstate/frontend/src/pages/sensor/SettingsTab.svelte b/visionstate/frontend/src/pages/sensor/SettingsTab.svelte
index 16902eb..86013a7 100644
--- a/visionstate/frontend/src/pages/sensor/SettingsTab.svelte
+++ b/visionstate/frontend/src/pages/sensor/SettingsTab.svelte
@@ -10,6 +10,7 @@
   import PublishSwitch from '../../lib/components/PublishSwitch.svelte';
   import ObjectPicker from '../../lib/components/ObjectPicker.svelte';
   import DigitCells from '../../lib/components/DigitCells.svelte';
+  import FacesCard from '../../lib/components/FacesCard.svelte';
   import ReadingEditor from '../../lib/components/ReadingEditor.svelte';
   import ReadingParams from '../../lib/components/ReadingParams.svelte';
   import ReviewRulesEditor from '../../lib/components/ReviewRulesEditor.svelte';
@@ -48,8 +49,9 @@
   let teachLimits = $state<TeachOverrides>({ ...(initial.objects?.teach ?? {}) });
   let globalTeach = $state<TeachLimitsInfo | null>(null);
   if (objectSensor) api.teachLimits().then((t) => (globalTeach = t)).catch(toastError);
-  const taughtCount = $derived(sensor.objects?.taught ?? 0);
-  const taughtSomething = $derived(!!sensor.objects && (taughtCount > 0 || sensor.objects.custom.length > 0));
+  // Boxes taught by their look; faces are counted on the Faces card.
+  const taughtCount = $derived(sensor.objects?.taught ?? 0); // boxes (faces are under People)
+  const taughtSomething = $derived(!!sensor.objects && (taughtCount > 0 || sensor.objects.custom.some((l) => !l.face)));
   const readingSensor = isReadingSensor(initial);
   const { value: _v, last: _l, has_image: _h, ...initialReading } = initial.reading ?? ({} as NonNullable<typeof initial.reading>);
   let reading = $state({ ...app.config!.reading_defaults, ...initialReading });
@@ -168,6 +170,7 @@
         <p class="small muted">Each object gets an on/off sensor and a count in Home Assistant.</p>
         <ObjectPicker bind:selected={classes} />
       </section>
+      <FacesCard {sensor} {onchange} />
       {#if taughtSomething}
         <section class="card pad col">
           <h3>What you taught</h3>
@@ -189,7 +192,7 @@
             <ConfirmButton class="btn sm danger" onconfirm={forgetTaught} confirmLabel="Tap again to forget everything taught">
               <Icon name="trash" size={14} /> Forget all
             </ConfirmButton>
-            <span class="xsmall faint">Every taught box and own label, with its entities.</span>
+            <span class="xsmall faint">Every taught box, own label and person, with their entities.</span>
           </div>
           {#if globalTeach}
             <div class="col teach-limits" data-testid="sensor-teach-limits">
diff --git a/visionstate/frontend/src/pages/wizard/ObjectsStep.svelte b/visionstate/frontend/src/pages/wizard/ObjectsStep.svelte
index 8a3d0e0..e615395 100644
--- a/visionstate/frontend/src/pages/wizard/ObjectsStep.svelte
+++ b/visionstate/frontend/src/pages/wizard/ObjectsStep.svelte
@@ -4,11 +4,12 @@
   import { onMount, untrack } from 'svelte';
   import { api } from '../../lib/api';
   import DetectionBoxes from '../../lib/components/DetectionBoxes.svelte';
+  import FaceConsent from '../../lib/components/FaceConsent.svelte';
   import ObjectPicker from '../../lib/components/ObjectPicker.svelte';
   import RoiEditor from '../../lib/components/RoiEditor.svelte';
   import { haSlug } from '../../lib/format';
   import { objectCount } from '../../lib/objects';
-  import type { Detection, Roi } from '../../lib/types';
+  import type { Detection, FaceModelInfo, Roi } from '../../lib/types';
   import HaPreview from './HaPreview.svelte';
   import TestCard from './TestCard.svelte';
 
@@ -18,7 +19,23 @@
     name,
     roi = $bindable(),
     classes = $bindable(),
-  }: { sourceType: string; source: string; name: string; roi: Roi | null; classes: string[] } = $props();
+    faces = $bindable(),
+  }: {
+    sourceType: string;
+    source: string;
+    name: string;
+    roi: Roi | null;
+    classes: string[];
+    /** Recognise people by their face (asked first, with what is stored and downloaded). */
+    faces: boolean;
+  } = $props();
+
+  let faceModel = $state<FaceModelInfo | null>(null);
+  let askingFaces = $state(false);
+  api
+    .settings()
+    .then((s) => (faceModel = s.face_models.find((m) => m.id === s.face_model) ?? null))
+    .catch(() => {});
 
   let image = $state<string | null>(null);
   let detections = $state<Detection[]>([]);
@@ -64,6 +81,25 @@
 <div class="col" style="gap:var(--space-3)">
   <h3>Which objects?</h3>
   <ObjectPicker bind:selected={classes} />
+  {#if classes.includes('person')}
+    <label class="check" data-testid="wizard-faces">
+      <input
+        type="checkbox"
+        checked={faces}
+        onclick={(e) => {
+          if (faces) return (faces = false);
+          e.preventDefault(); // on only after the confirmation
+          askingFaces = true;
+        }}
+      />
+      <span class="col" style="gap:2px">
+        <strong class="small">Recognise people by their face</strong>
+        <span class="xsmall faint">
+          You name the people after it is made (tap a person on its Live tab); each gets an on/off sensor.
+        </span>
+      </span>
+    </label>
+  {/if}
 </div>
 <TestCard {busy} onretest={test}>
   {#snippet status()}
@@ -93,7 +129,25 @@
   {/each}
 </HaPreview>
 
+{#if askingFaces}
+  <FaceConsent
+    name={name.trim()}
+    model={faceModel}
+    onclose={() => (askingFaces = false)}
+    onconfirm={() => {
+      askingFaces = false;
+      faces = true;
+    }}
+  />
+{/if}
+
 <style>
+  .check {
+    align-items: flex-start;
+  }
+  .check input {
+    margin-top: 3px;
+  }
   .danger-text {
     color: var(--c-danger);
   }

From 4f09e1b0bed4089442885887694d643a52078f6d Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Sun, 11 Oct 2026 21:40:54 +0200
Subject: [PATCH 10/28] 0.8.1b4: teach a face from photos on the phone

The People page adds a person, or more faces of one, from photos taken with
the phone's camera or picked from its gallery, a short video or a ZIP: the
same upload as a state sensor's, the best face of each picture through the
same quality gate. Only once a sensor recognises faces (its confirmation
fetches the face model).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 docs/SCOPE.md                                 |   4 +-
 visionstate/CHANGELOG.md                      |  10 ++
 visionstate/DOCS.md                           |   6 +
 visionstate/backend/tests/test_faces.py       |  38 ++++++-
 visionstate/backend/visionstate/api/faces.py  | 106 +++++++++++++++++-
 .../backend/visionstate/engine/faces.py       |  10 ++
 visionstate/backend/visionstate/settings.py   |   5 +
 visionstate/config.yaml                       |   2 +-
 visionstate/frontend/e2e/ui.spec.ts           |  17 ++-
 visionstate/frontend/src/lib/api.ts           |   9 ++
 .../src/lib/components/PeopleCards.svelte     |  38 ++++++-
 visionstate/frontend/src/lib/objects.ts       |  11 +-
 visionstate/frontend/src/lib/types.ts         |   9 ++
 visionstate/frontend/src/lib/ui.ts            |   3 +
 visionstate/frontend/src/pages/People.svelte  |  66 +++++++++++
 15 files changed, 324 insertions(+), 10 deletions(-)

diff --git a/docs/SCOPE.md b/docs/SCOPE.md
index 8be10df..3af9009 100644
--- a/docs/SCOPE.md
+++ b/docs/SCOPE.md
@@ -227,7 +227,9 @@ left are in [`FACE_RECOGNITION_PLAN.md`](FACE_RECOGNITION_PLAN.md). In short:
   them last when several do — or `settings.PERSON_NOT_SEEN`; when and where they were last seen
   are attributes. Kept in memory: after a restart everyone is `not_seen` until seen again.
 - `GET /people` and `/faces/{id}` (`api/faces.py`) list, rename, move and forget for all sensors;
-  teaching stays on a sensor (`/sensors/{id}/faces`). Export carries the faces taught on that
+  teaching stays on a sensor (`/sensors/{id}/faces`), or comes from photos (`POST /people/photos`:
+  read like a state sensor's upload, `uploads.frames_from_file`; the best face of each picture
+  through the same gate, at most `FACES["max_photos"]`; origin `upload`, no sensor). Export carries the faces taught on that
   sensor, with their people's keys and names, only with `?faces=true`; import adds them to the
   person with that key or name (or a new one) and turns face recognition off.
 
diff --git a/visionstate/CHANGELOG.md b/visionstate/CHANGELOG.md
index 1e521d8..15d081b 100644
--- a/visionstate/CHANGELOG.md
+++ b/visionstate/CHANGELOG.md
@@ -1,5 +1,15 @@
 # Changelog
 
+## 0.8.1b4
+
+### New
+
+- **Teach a face from your phone.** On the **People** page, **Add a person** or **Add photos** on a
+  person takes photos straight from the phone's camera or its gallery (also in the Home Assistant
+  app), a short video turning the head a little, or a ZIP of photos. Each goes through the same
+  check as a face on a camera, and the message says what was added and why a photo was skipped.
+  A quick start; faces taught on each camera still recognise people best.
+
 ## 0.8.1b3
 
 ### New
diff --git a/visionstate/DOCS.md b/visionstate/DOCS.md
index b07d4c6..308f71d 100644
--- a/visionstate/DOCS.md
+++ b/visionstate/DOCS.md
@@ -221,6 +221,12 @@ everyone.
   person has. Someone who was only there for a moment (at a doorbell) can be named afterwards: a
   face nobody is named for keeps a frame in the history (*Face seen, not named*; at most one a
   minute, or a closer one).
+- **From photos**: on the **People** page, **Add a person** (a name, then **Add photos**) or
+  **Add photos** on a person takes photos from the phone — its camera or gallery — or a short video
+  turning the head a little (a frame every half second), or a ZIP of photos. The best face of each
+  goes through the same check; the message says how many were added and why a photo was skipped.
+  A quick start: faces taught on each camera still recognise people best, as a camera sees them
+  from another angle and in other light. Photos can be added once a sensor recognises faces.
 - **How it decides**: each face is compared with the faces you taught for each person. A clear
   match names the person; a less certain one needs a second check that agrees. A person who stays
   where they were keeps the name while their face is turned away. Close to someone, but not
diff --git a/visionstate/backend/tests/test_faces.py b/visionstate/backend/tests/test_faces.py
index 4502e40..2d41f46 100644
--- a/visionstate/backend/tests/test_faces.py
+++ b/visionstate/backend/tests/test_faces.py
@@ -261,7 +261,8 @@ def test_people_are_taught_recognised_forgotten_exported(settings):  # noqa: F81
         assert wait_for(lambda: person_with_face(client, sid), timeout=30)
         stats = view(client, sid)["objects"]["face_stats"]
         assert stats["looked"] >= 1 and stats["usable"] >= 1
-        assert stats["frame"] == list(Image.open(ASSETS / "face_williams_2006.jpg").size)
+        with Image.open(ASSETS / "face_williams_2006.jpg") as photo:
+            assert stats["frame"] == list(photo.size)
         # A face nobody is named for keeps its frame in the history (once, not every check).
         seen = {"sensor": sid, "event": "face_seen", "limit": 10}
         assert wait_for(lambda: client.get("/api/v1/history", params=seen).json()["total"] == 1, timeout=30)
@@ -487,3 +488,38 @@ def test_each_person_has_an_entity_saying_where_they_are():
     assert config["default_entity_id"] == "sensor.visionstate_ole" and config["name"] == "Ole"
     assert config["unique_id"] == "visionstate_person_ole" and config["device"]["identifiers"] == ["visionstate"]
     assert config["state_topic"] == person_topics("ole")["state"] == "visionstate/people/ole/state"
+
+
+@requires_faces
+def test_people_are_taught_from_photos(settings):  # noqa: F811
+    from visionstate.uploads import zip_bytes
+
+    with TestClient(create_app(settings)) as client:
+        rt = client.app.state.runtime
+        photo = (ASSETS / "face_williams_2006.jpg").read_bytes()
+        files = [("files", ("suni.jpg", photo, "image/jpeg"))]
+        # Only once a sensor recognises faces (its confirmation fetches the face model).
+        assert client.post("/api/v1/people/photos", data={"new_person": "Suni"}, files=files).status_code == 400
+        create(client, objects=faces_patch(True)["objects"])
+        assert wait_for(lambda: rt.face_model is not None, timeout=60)
+        blank = io.BytesIO()
+        Image.new("RGB", (640, 480), (90, 90, 90)).save(blank, "JPEG")
+        files = [("files", ("suni.jpg", photo, "image/jpeg")), ("files", ("wall.jpg", blank.getvalue(), "image/jpeg"))]
+        made = client.post("/api/v1/people/photos", data={"new_person": "Suni"}, files=files)
+        assert made.status_code == 201, made.text
+        body = made.json()
+        assert body["label"] == "suni" and body["added"] == 1 and body["faces"] == 1
+        assert body["skipped"] == [{"file": "wall.jpg", "message": "No face found in this photo."}]
+        # More of an existing person, from a ZIP (as from a state sensor's upload).
+        archive = zip_bytes({"a.jpg": (ASSETS / "face_williams_2018.jpg").read_bytes()})
+        more = client.post(
+            "/api/v1/people/photos", data={"person": "suni"}, files=[("files", ("p.zip", archive, "application/zip"))]
+        )
+        assert more.status_code == 201 and more.json()["faces"] == 2
+        [suni] = client.get("/api/v1/people").json()["people"]
+        assert {f["origin"] for f in suni["faces"]} == {"upload"} and all(f["sensor_id"] is None for f in suni["faces"])
+        # No usable face: nobody is made.
+        empty = [("files", ("wall.jpg", blank.getvalue(), "image/jpeg"))]
+        nobody = client.post("/api/v1/people/photos", data={"new_person": "Nobody"}, files=empty).json()
+        assert nobody["added"] == 0 and len(client.get("/api/v1/people").json()["people"]) == 1
+        assert client.post("/api/v1/people/photos", files=empty).status_code == 400  # who is it?
diff --git a/visionstate/backend/visionstate/api/faces.py b/visionstate/backend/visionstate/api/faces.py
index 5c66059..8752fd8 100644
--- a/visionstate/backend/visionstate/api/faces.py
+++ b/visionstate/backend/visionstate/api/faces.py
@@ -7,21 +7,25 @@
 from __future__ import annotations
 
 import asyncio
+import itertools
+import tempfile
 from datetime import datetime
+from pathlib import Path
 
 import numpy as np
-from fastapi import APIRouter, HTTPException, Request, Response
+from fastapi import APIRouter, File, Form, HTTPException, Request, Response, UploadFile
 from fastapi.responses import FileResponse
 from PIL import Image
 from pydantic import BaseModel, Field, field_validator
 from sqlalchemy import func, select
 from sqlalchemy.orm import Session
 
-from .. import detectors, faces, imaging
+from .. import detectors, faces, imaging, uploads
 from ..db import Face, Person, Sensor
 from ..engine import Runtime, SensorConfig
-from ..settings import FACES, KIND_OBJECTS, TEACH, merge_objects
+from ..settings import FACES, KIND_OBJECTS, TEACH, UPLOAD_LIMITS, merge_objects
 from .common import API_PREFIX, iso, runtime, slugify
+from .samples import copy_limited
 from .teach import RESERVED_KEYS, frame_image
 
 router = APIRouter(prefix=f"{API_PREFIX}/sensors", tags=["faces"])
@@ -38,6 +42,17 @@
 }
 
 
+# The same, about a photo from a phone (People page).
+PHOTO_PROBLEMS = {
+    "none": "No face found in this photo.",
+    "cut": "The edge of this photo cuts the face.",
+    "small": "The face is too small in this photo: take it closer.",
+    "turned": "The face is turned away in this photo: look at the camera.",
+    "blurry": "This photo is blurry.",
+    "unsure": "This does not clearly look like a face.",
+}
+
+
 class FaceBoxIn(BaseModel):
     """A person box on the frame a check analysed (``frame_id``) or on a history frame."""
 
@@ -313,6 +328,91 @@ def _quality(index: faces.FaceIndex, limits: dict[str, float]) -> tuple[dict[int
     return suspects, look_alikes
 
 
+@people_router.post("/people/photos", status_code=201)
+async def add_photos(
+    request: Request,
+    files: list[UploadFile] = File(...),
+    person: str | None = Form(default=None, max_length=64),
+    new_name: str | None = Form(default=None, alias="new_person", max_length=64),
+) -> dict:
+    """Teach faces from photos or short videos (taken with a phone or picked from its gallery;
+    read like a state sensor's upload: images, ZIP archives, videos) as one of the people, or as a
+    new person (made only when there is a usable face). The best face of each picture goes
+    through the same quality gate as on a camera; at most FACES["max_photos"] faces are added."""
+    rt = runtime(request)
+    if not person and not (new_name or "").strip():
+        raise HTTPException(400, "Say who it is: a person or a new person")
+    if not await asyncio.to_thread(rt.face_sensor_ids):  # the face model is only fetched after a sensor's confirmation
+        raise HTTPException(400, "Turn on face recognition on a sensor first (its Settings → Faces)")
+    with rt.db.session() as s:
+        if person:
+            person_by_key(s, person)
+        else:
+            if (s.scalar(select(func.count()).select_from(Person)) or 0) >= FACES["max_people"]:
+                raise HTTPException(400, f"VisionState can know at most {FACES['max_people']} people")
+            _check_name(s, new_name or "")
+    model = await _face_model(rt)
+    min_px = rt.face_limits(None)["min_face_px"]
+    found: list[tuple[Image.Image, faces.Face]] = []
+    skipped: list[dict] = []
+    for upload_file in files:
+        if len(found) >= FACES["max_photos"]:
+            break
+        name = upload_file.filename or "photo"
+        try:
+            pictures = await asyncio.to_thread(_pictures, upload_file, name, FACES["max_photos"] * 3)
+        except (uploads.UploadError, OSError) as err:
+            skipped.append({"file": name, "message": str(err) or "Not an image this app can read."})
+            continue
+        problems: list[str] = []
+        for image in pictures:
+            face, aligned = await rt.face_in_photo(model, image)
+            problem = "none" if face is None or aligned is None else faces.problem(face, aligned, min_px, image.size)
+            if problem or face is None:
+                problems.append(problem or "none")
+                continue
+            found.append((image, face))
+            if len(found) >= FACES["max_photos"]:
+                break
+        if problems and len(problems) == len(pictures):  # nothing usable in this file
+            skipped.append({"file": name, "message": PHOTO_PROBLEMS.get(problems[0], PHOTO_PROBLEMS["none"])})
+    if not found:
+        return {"label": person, "name": None, "added": 0, "skipped": skipped, "faces": None}
+    with rt.db.session() as s:
+        who = person_by_key(s, person) if person else new_person(s, new_name or "")
+        count = s.scalar(select(func.count()).select_from(Face).where(Face.person_id == who.id)) or 0
+        added = 0
+        for image, face in found:
+            if count >= FACES["max_faces_per_person"]:
+                skipped.append({"file": "", "message": f"A person has at most {FACES['max_faces_per_person']} faces."})
+                break
+            crop, local = faces.face_crop(image, face)
+            filename = rt.storage.save_face(crop)
+            add_face_row(s, who.id, None, filename, crop, faces.face_to_dict(local), "upload")
+            count, added = count + 1, added + 1
+        result = {"label": who.key, "name": who.name, "added": added, "skipped": skipped, "faces": count}
+    if not person:
+        await rt.people_changed()
+    else:
+        await asyncio.to_thread(rt.faces_changed)
+    return result
+
+
+def _pictures(upload_file: UploadFile, name: str, limit: int) -> list[Image.Image]:
+    """The pictures in an uploaded file (an image, the images of a ZIP, frames of a video), at
+    most ``limit``; the file goes through a temporary file within the upload limit."""
+    with tempfile.NamedTemporaryFile(delete=False, suffix=Path(name).suffix) as tmp:
+        path = Path(tmp.name)
+        within = copy_limited(upload_file.file, tmp, UPLOAD_LIMITS["max_file_mb"] * 1_000_000)
+    try:
+        if not within:
+            raise uploads.UploadError(f"Larger than {UPLOAD_LIMITS['max_file_mb']} MB.")
+        frames = uploads.frames_from_file(path, name, FACES["photo_video_interval_s"])
+        return [image for image, _origin in itertools.islice(frames, limit)]
+    finally:
+        path.unlink(missing_ok=True)
+
+
 @people_router.patch("/people/{key}")
 async def rename_person(key: str, body: PersonIn, request: Request) -> dict:
     """A person's name (their entities' names follow; their key and entity IDs stay)."""
diff --git a/visionstate/backend/visionstate/engine/faces.py b/visionstate/backend/visionstate/engine/faces.py
index 75948cc..2776652 100644
--- a/visionstate/backend/visionstate/engine/faces.py
+++ b/visionstate/backend/visionstate/engine/faces.py
@@ -289,6 +289,16 @@ async def _publish_person(self, key: str, where: str, force: bool = False) -> No
 
     # --- One check ------------------------------------------------------------------------------
 
+    async def face_in_photo(
+        self, model: faces.FaceModel, image: Image.Image
+    ) -> tuple[faces.Face | None, Image.Image | None]:
+        """The best face in a whole photo (a selfie from a phone), with it aligned."""
+        async with self._sem:
+            [face] = await asyncio.to_thread(model.detector.detect, [image])
+        if face is None:
+            return None, None
+        return face, await asyncio.to_thread(faces.align, image, face, model.recogniser.spec.input_size)
+
     async def find_faces(
         self, model: faces.FaceModel, image: Image.Image, boxes: list[Sequence[float]]
     ) -> list[tuple[faces.Face | None, Image.Image | None]]:
diff --git a/visionstate/backend/visionstate/settings.py b/visionstate/backend/visionstate/settings.py
index e4f36b7..f05d760 100644
--- a/visionstate/backend/visionstate/settings.py
+++ b/visionstate/backend/visionstate/settings.py
@@ -287,6 +287,11 @@
     # seconds per sensor, unless the face came this many times closer (wider) since the last one.
     "record_cooldown_s": 60,
     "record_closer": 1.5,
+    # Photos and short videos from a phone (People page; the same upload as a state sensor's, so
+    # images, ZIP archives and videos): at most this many faces are added from one upload, and a
+    # video gives a frame every this many seconds (turning the head gives several angles).
+    "max_photos": 10,
+    "photo_video_interval_s": 0.5,
 }
 FACE_UNKNOWN_KEY = "unknown_person"  # the key (and entity) of a face nobody was taught for
 # A person's own entity (sensor.visionstate_<key>) says on which sensor they are seen: its slug,
diff --git a/visionstate/config.yaml b/visionstate/config.yaml
index 0261bba..43e3036 100644
--- a/visionstate/config.yaml
+++ b/visionstate/config.yaml
@@ -1,5 +1,5 @@
 name: VisionState (beta)
-version: "0.8.1b3"
+version: "0.8.1b4"
 slug: visionstate
 description: Teach a local AI to recognise states (open, closed, …), find objects (people, cars, pets) or read numbers in your camera images
 url: https://github.com/oleost/VisionState
diff --git a/visionstate/frontend/e2e/ui.spec.ts b/visionstate/frontend/e2e/ui.spec.ts
index 6295691..598f272 100644
--- a/visionstate/frontend/e2e/ui.spec.ts
+++ b/visionstate/frontend/e2e/ui.spec.ts
@@ -1295,8 +1295,10 @@ test('faces: turned on with a confirmation, a person taught from the Live tab, s
   const errors = watchErrors(page);
   const faceId = await seededFaceSensorId(request);
   // Each device runs this against the same backend: start without the person it teaches.
-  const del = await request.delete('api/v1/people/butch');
-  expect([204, 404]).toContain(del.status());
+  for (const key of ['butch', 'kari']) {
+    const del = await request.delete(`api/v1/people/${key}`);
+    expect([204, 404]).toContain(del.status());
+  }
 
   // Turning it on asks first, and says what is stored and downloaded.
   const beach = await seededObjectSensorId(request);
@@ -1360,6 +1362,17 @@ test('faces: turned on with a confirmation, a person taught from the Live tab, s
   await expect(page.getByText('Butch is now Butch W')).toBeVisible();
   await expect(page.getByRole('heading', { name: /^Butch W/ })).toBeVisible();
   expect((await (await request.get('api/v1/people')).json()).people.map((p: { key: string }) => p.key)).toContain('butch');
+
+  // Photos from the phone: more of a person, and a new person (the photo picker is a file input).
+  const assets = path.join('..', 'backend', 'tests', 'assets');
+  await page.locator('#photos-butch').setInputFiles(path.join(assets, 'face_wilmore.jpg'));
+  await expect(page.getByText('1 face added to Butch W')).toBeVisible({ timeout: 30_000 });
+  await expect(butch.getByText(/· photo/)).toBeVisible();
+  const add = page.getByTestId('add-person');
+  await add.getByLabel('Name of the new person').fill('Kari');
+  await add.locator('input[type=file]').setInputFiles(path.join(assets, 'face_williams_2018.jpg'));
+  await expect(page.getByText('1 face added to Kari')).toBeVisible({ timeout: 30_000 });
+  await expect(page.getByRole('heading', { name: /^Kari/ })).toBeVisible();
   await expectNoHorizontalOverflow(page);
   await expectNoClipping(page, '.btn, .butch');
   errors.expectNone();
diff --git a/visionstate/frontend/src/lib/api.ts b/visionstate/frontend/src/lib/api.ts
index be91a91..31482a5 100644
--- a/visionstate/frontend/src/lib/api.ts
+++ b/visionstate/frontend/src/lib/api.ts
@@ -8,6 +8,7 @@ import type {
   FaceStatus,
   ModelKind,
   People,
+  PhotoResult,
   Camera,
   Detection,
   HaEntity,
@@ -278,6 +279,14 @@ export const api = {
   /** A person (or, without a key, everyone) with their faces and entities on every sensor. */
   forgetPerson: (key: string) => request<void>(`people/${encodeURIComponent(key)}`, send('DELETE')),
   forgetPeople: () => request<void>('people', send('DELETE')),
+  /** Faces from photos or short videos (a phone's camera or gallery) for a person or a new one. */
+  addPhotos(who: { person: string } | { new_person: string }, files: File[]) {
+    const form = new FormData();
+    files.forEach((f) => form.append('files', f, f.name));
+    if ('person' in who) form.append('person', who.person);
+    else form.append('new_person', who.new_person);
+    return request<PhotoResult>('people/photos', { method: 'POST', body: form });
+  },
   retryFaces: () => request<FaceStatus>('faces/retry', send('POST')),
   faceLimits: () => request<FaceLimitsInfo>('face-limits'),
   saveFaceLimits: (values: FaceOverrides) => request<FaceLimitsInfo>('face-limits', send('PUT', values)),
diff --git a/visionstate/frontend/src/lib/components/PeopleCards.svelte b/visionstate/frontend/src/lib/components/PeopleCards.svelte
index 9db4a63..6b625b4 100644
--- a/visionstate/frontend/src/lib/components/PeopleCards.svelte
+++ b/visionstate/frontend/src/lib/components/PeopleCards.svelte
@@ -8,8 +8,10 @@
   import { api } from '../api';
   import { app, refreshStatus, toast, toastError } from '../app.svelte';
   import { ago, dateTime } from '../format';
+  import { photoMessage } from '../objects';
   import { href, paths } from '../router.svelte';
   import type { People, Person, Sensor } from '../types';
+  import { PHOTO_ACCEPT } from '../ui';
   import ConfirmButton from './ConfirmButton.svelte';
   import FaceStats from './FaceStats.svelte';
   import Icon from './Icon.svelte';
@@ -51,6 +53,24 @@
     renaming = null;
     if (name && name !== p.name) await act(() => api.renamePerson(p.key, name), `${p.name} is now ${name}`);
   }
+  let uploading = $state<string | null>(null);
+  async function addPhotos(e: Event, p: Person) {
+    const input = e.currentTarget as HTMLInputElement;
+    const files = [...(input.files ?? [])];
+    input.value = '';
+    if (!files.length) return;
+    uploading = p.key;
+    try {
+      const result = await api.addPhotos({ person: p.key }, files);
+      toast(photoMessage(result));
+      load();
+    } catch (err) {
+      toastError(err);
+    } finally {
+      uploading = null;
+    }
+  }
+
   function seen(p: Person): string {
     const where = p.seen.sensor_name ? ` on ${p.seen.sensor_name}` : '';
     if (p.seen.now !== 'not_seen') return `Seen now${where}`;
@@ -111,6 +131,18 @@
           </span>
         </div>
         <div class="row wrap actions">
+          <input
+            type="file"
+            accept={PHOTO_ACCEPT}
+            multiple
+            class="sr-only"
+            id={`photos-${p.key}`}
+            onchange={(e) => addPhotos(e, p)}
+            disabled={uploading !== null}
+          />
+          <label class="btn sm" for={`photos-${p.key}`} class:disabled={uploading !== null}>
+            <Icon name="camera" size={14} /> {uploading === p.key ? 'Adding…' : 'Add photos'}
+          </label>
           {#if renaming !== p.key}
             <button class="btn sm" onclick={() => startRename(p)}>Rename</button>
           {/if}
@@ -153,7 +185,7 @@
               </button>
             {/if}
             <figcaption class="xsmall faint" title={dateTime(f.created_at)}>
-              {ago(f.created_at)}{f.sensor_name ? ` · ${f.sensor_name}` : ''}
+              {ago(f.created_at)}{f.sensor_name ? ` · ${f.sensor_name}` : f.origin === 'upload' ? ' · photo' : ''}
             </figcaption>
           </figure>
         {/each}
@@ -171,6 +203,10 @@
     gap: var(--space-2);
     justify-content: flex-end;
   }
+  label.disabled {
+    opacity: 0.5;
+    pointer-events: none;
+  }
   .rename {
     gap: var(--space-2);
   }
diff --git a/visionstate/frontend/src/lib/objects.ts b/visionstate/frontend/src/lib/objects.ts
index 9d16982..9899c14 100644
--- a/visionstate/frontend/src/lib/objects.ts
+++ b/visionstate/frontend/src/lib/objects.ts
@@ -1,6 +1,6 @@
 // Helpers for object sensors: display names and colours of object classes and own labels.
 import { app } from './app.svelte';
-import type { Detection, FaceProblem, OwnLabel, Sensor } from './types';
+import type { Detection, FaceProblem, OwnLabel, PhotoResult, Sensor } from './types';
 
 /**
  * Display name of an object class ("traffic_light" → "Traffic light"), from the backend label
@@ -32,6 +32,15 @@ export const facesOn = (sensor: Pick<Sensor, 'objects'>) =>
 export const peopleOf = (sensor: Pick<Sensor, 'objects'>): OwnLabel[] =>
   (sensor.objects?.custom ?? []).filter((l) => l.face);
 
+/** What adding photos did, for a toast: "2 faces added to Kari", and why a file was skipped. */
+export function photoMessage(result: PhotoResult): string {
+  const added = result.added
+    ? `${result.added} ${result.added === 1 ? 'face' : 'faces'} added to ${result.name}`
+    : 'No face added';
+  const skipped = result.skipped.map((s) => (s.file ? `${s.file}: ${s.message}` : s.message));
+  return [added, ...skipped].join(' · ');
+}
+
 /** Why the face of a person box cannot be used, in a few words. */
 export const FACE_PROBLEMS: Record<FaceProblem, string> = {
   none: 'no face seen',
diff --git a/visionstate/frontend/src/lib/types.ts b/visionstate/frontend/src/lib/types.ts
index 7f28000..58419d8 100644
--- a/visionstate/frontend/src/lib/types.ts
+++ b/visionstate/frontend/src/lib/types.ts
@@ -252,6 +252,15 @@ export interface Person {
   }[];
 }
 
+/** POST /people/photos: faces added from photos or videos, and the files that had none usable. */
+export interface PhotoResult {
+  label: string | null;
+  name: string | null;
+  added: number;
+  skipped: { file: string; message: string }[];
+  faces: number | null;
+}
+
 /** The face model: chosen, loaded, downloading (bytes) or why it failed. */
 export interface FaceStatus {
   model: string;
diff --git a/visionstate/frontend/src/lib/ui.ts b/visionstate/frontend/src/lib/ui.ts
index 10f0360..92e7923 100644
--- a/visionstate/frontend/src/lib/ui.ts
+++ b/visionstate/frontend/src/lib/ui.ts
@@ -92,6 +92,9 @@ export const TEACH_FIELDS = [
 export const SMALL_FRAME_PX = 1280;
 export const LOW_STREAM_NAME = /(^|[^a-z])(fluent|sub|low|lowres)([^a-z]|$)/i;
 
+/** Files a phone offers for faces: photos (its camera or gallery), short videos, ZIP archives. */
+export const PHOTO_ACCEPT = 'image/*,video/*,.zip';
+
 /** The faces' limits, on the Settings page and an object sensor's Faces card (in this order). */
 export const FACE_FIELDS = [
   {
diff --git a/visionstate/frontend/src/pages/People.svelte b/visionstate/frontend/src/pages/People.svelte
index e445ad5..7160b4f 100644
--- a/visionstate/frontend/src/pages/People.svelte
+++ b/visionstate/frontend/src/pages/People.svelte
@@ -8,10 +8,37 @@
   import ConfirmButton from '../lib/components/ConfirmButton.svelte';
   import Icon from '../lib/components/Icon.svelte';
   import PeopleCards from '../lib/components/PeopleCards.svelte';
+  import { photoMessage } from '../lib/objects';
+  import { PHOTO_ACCEPT } from '../lib/ui';
 
   let version = $state(0); // a new one reloads the cards
 
   const faces = $derived(app.status?.faces);
+  const room = $derived((faces?.people ?? 0) < (app.config?.faces.max_people ?? 0));
+
+  let newName = $state('');
+  let adding = $state(false);
+  async function addPerson(e: Event) {
+    const input = e.currentTarget as HTMLInputElement;
+    const files = [...(input.files ?? [])];
+    input.value = '';
+    const name = newName.trim();
+    if (!files.length || !name) return;
+    adding = true;
+    try {
+      const result = await api.addPhotos({ new_person: name }, files);
+      toast(photoMessage(result));
+      if (result.added) {
+        newName = '';
+        version += 1;
+        refreshStatus();
+      }
+    } catch (err) {
+      toastError(err);
+    } finally {
+      adding = false;
+    }
+  }
 
   async function forgetEveryone() {
     try {
@@ -37,6 +64,30 @@
       No sensor recognises faces yet: turn it on under an object sensor's Settings → Faces (one that looks for Person).
     </div>
   {/if}
+  {#if room && faces?.sensors}
+    <section class="card pad col add" data-testid="add-person">
+      <h3>Add a person</h3>
+      <p class="small muted">
+        A quick start: a few photos of their face, taken with the phone or picked from its gallery, or a short video
+        turning the head a little. Faces taught on each camera (tap a person on its Live tab) recognise them best.
+      </p>
+      <div class="row wrap add-row">
+        <input class="input" bind:value={newName} placeholder="Name" maxlength="64" aria-label="Name of the new person" />
+        <input
+          type="file"
+          accept={PHOTO_ACCEPT}
+          multiple
+          class="sr-only"
+          id="new-person-photos"
+          onchange={addPerson}
+          disabled={!newName.trim() || adding}
+        />
+        <label class="btn primary" for="new-person-photos" class:disabled={!newName.trim() || adding}>
+          <Icon name="camera" size={14} /> {adding ? 'Adding…' : 'Add photos'}
+        </label>
+      </div>
+    </section>
+  {/if}
   {#key version}
     <div class="col cards"><PeopleCards /></div>
   {/key}
@@ -54,6 +105,21 @@
   .intro {
     max-width: 720px;
   }
+  .add {
+    gap: var(--space-2);
+    max-width: 900px;
+    margin-bottom: var(--space-4);
+  }
+  .add-row {
+    gap: var(--space-2);
+  }
+  .add-row .input {
+    max-width: 240px;
+  }
+  label.disabled {
+    opacity: 0.5;
+    pointer-events: none;
+  }
   .cards {
     gap: var(--space-4);
     max-width: 900px;

From 039b7e96cb577c63300e1aca38e4f20e355431a7 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Sun, 11 Oct 2026 22:25:50 +0200
Subject: [PATCH 11/28] 0.8.1b5: boxes on the Live tab always match the picture

An object sensor announced a check's frame before its detections were
found, so the Live tab drew the previous check's boxes (a person and their
face) on the new frame. The frame and its detections now change together.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 visionstate/CHANGELOG.md                      |  9 ++++++++
 visionstate/backend/tests/test_objects.py     | 23 +++++++++++++++++++
 .../backend/visionstate/engine/checks.py      |  6 +++--
 .../backend/visionstate/engine/objects.py     |  8 ++++---
 visionstate/config.yaml                       |  2 +-
 5 files changed, 42 insertions(+), 6 deletions(-)

diff --git a/visionstate/CHANGELOG.md b/visionstate/CHANGELOG.md
index 15d081b..6531ce0 100644
--- a/visionstate/CHANGELOG.md
+++ b/visionstate/CHANGELOG.md
@@ -1,5 +1,14 @@
 # Changelog
 
+## 0.8.1b5
+
+### Fixed
+
+- **Boxes on the Live tab now always match the picture.** While a check was still looking for
+  objects and faces, the Live tab could show its new frame with the boxes of the check before — a
+  person (and their face) drawn where nobody stood any more. A new frame now appears together with
+  what was found in it.
+
 ## 0.8.1b4
 
 ### New
diff --git a/visionstate/backend/tests/test_objects.py b/visionstate/backend/tests/test_objects.py
index 6a6e1f9..65aaa41 100644
--- a/visionstate/backend/tests/test_objects.py
+++ b/visionstate/backend/tests/test_objects.py
@@ -353,3 +353,26 @@ def test_detector_choice_is_stored_and_switchable(settings):
             "detector": "nope",
         }
         assert client.put("/api/v1/settings", json=body).status_code == 400
+
+
+def test_the_frame_on_screen_changes_together_with_its_boxes(settings):
+    """While a check looks for objects (and faces), the sensor still shows the last frame with its
+    boxes: a new frame is announced only together with what was found in it."""
+    import asyncio
+
+    with TestClient(create_app(settings)) as client:
+        rt = client.app.state.runtime
+        rt.grabber = PhotoCamera("beach")
+        sid = create(client, objects={"classes": ["dog"]}, interval_s=3600).json()["id"]
+        live = rt.live_state(sid)
+        live.frame_id, live.detections = "before", [{"key": "dog", "score": 0.9, "box": [0, 0, 0.1, 0.1]}]
+        seen_during: list = []
+
+        async def detect(*_args, **_kwargs):
+            seen_during.append((live.frame_id, len(live.detections)))
+            return []
+
+        rt.detect_objects = detect
+        asyncio.run_coroutine_threadsafe(rt.run_once(rt.load_sensor(sid)), rt._loop).result(timeout=30)
+        assert seen_during == [("before", 1)]  # the old frame with its own boxes, meanwhile
+        assert live.frame_id not in (None, "before") and live.detections == []
diff --git a/visionstate/backend/visionstate/engine/checks.py b/visionstate/backend/visionstate/engine/checks.py
index 9e56ea9..54d7337 100644
--- a/visionstate/backend/visionstate/engine/checks.py
+++ b/visionstate/backend/visionstate/engine/checks.py
@@ -248,12 +248,14 @@ async def run_once(
             if live.signature is not None:
                 live.change_score = imaging.change_score(live.signature, signature)
             live.signature = signature
-        live.frame_id = frame_id
         await self._send(cfg, t["availability"], "online", retain=True)
         if cfg.is_objects:
             live.analysed.append((frame_id, data))
-            await self._run_objects(cfg, image)
+            # The frame on screen changes together with its boxes, once they are found (the UI
+            # draws the boxes of live.detections on live.frame_id).
+            await self._run_objects(cfg, image, frame_id)
             return
+        live.frame_id = frame_id
         if cfg.is_reading:
             await self._run_reading(cfg, image, lit)
             return
diff --git a/visionstate/backend/visionstate/engine/objects.py b/visionstate/backend/visionstate/engine/objects.py
index b2ee2b4..be5fb38 100644
--- a/visionstate/backend/visionstate/engine/objects.py
+++ b/visionstate/backend/visionstate/engine/objects.py
@@ -27,8 +27,9 @@
 
 
 class ObjectChecksMixin(FacesMixin, TeachingMixin, PublishingMixin, RuntimeBase):
-    async def _run_objects(self, cfg: SensorConfig, image: Image.Image) -> None:
-        """One check of an object sensor: detect, compare with what was taught, update and publish."""
+    async def _run_objects(self, cfg: SensorConfig, image: Image.Image, frame_id: str | None) -> None:
+        """One check of an object sensor: detect, compare with what was taught, update and publish.
+        ``frame_id`` (the frame analysed) is announced together with what was found in it."""
         live = self.live_state(cfg.id)
         classes = cfg.objects["classes"]
         index = await self.taught_index(cfg)
@@ -39,6 +40,7 @@ async def _run_objects(self, cfg: SensorConfig, image: Image.Image) -> None:
             found = await self.detect_objects(image, cfg.roi, cfg.objects, floor, all_classes=rescue)
         except Exception as err:  # noqa: BLE001 - detector missing or failed to load
             live.error = f"Object detector unavailable: {redact(str(err))}"
+            live.frame_id, live.detections = frame_id, []
             return
         candidates: list[dict] = []
         if rescue:
@@ -50,7 +52,7 @@ async def _run_objects(self, cfg: SensorConfig, image: Image.Image) -> None:
             found = await self.apply_faces(cfg, image, found, live.detections)
         counted = [d for d in found if not d.get("filtered")]
         now = time.time()
-        live.detections = found
+        live.frame_id, live.detections = frame_id, found  # together: the boxes belong to that frame
         best = max(counted, key=lambda d: d["score"], default=None)
         live.top, live.confidence = (best.get("label") or best["key"], best["score"]) if best else (None, 0.0)
         keys = cfg.object_keys
diff --git a/visionstate/config.yaml b/visionstate/config.yaml
index 43e3036..691cef1 100644
--- a/visionstate/config.yaml
+++ b/visionstate/config.yaml
@@ -1,5 +1,5 @@
 name: VisionState (beta)
-version: "0.8.1b4"
+version: "0.8.1b5"
 slug: visionstate
 description: Teach a local AI to recognise states (open, closed, …), find objects (people, cars, pets) or read numbers in your camera images
 url: https://github.com/oleost/VisionState

From be179e4ae2602a9cac68ef06870e47778718cd24 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Sun, 11 Oct 2026 22:45:21 +0200
Subject: [PATCH 12/28] 0.8.1b5: let the face model go when it was turned off
 while loading

A check in flight could load the face model again right after the last
sensor turned faces off; it then stayed in memory. Once loaded, it is let
go again when no sensor recognises faces. The new frame test is paused so
only its own check runs.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 visionstate/CHANGELOG.md                        | 2 ++
 visionstate/backend/tests/test_objects.py       | 3 ++-
 visionstate/backend/visionstate/engine/faces.py | 7 ++++++-
 3 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/visionstate/CHANGELOG.md b/visionstate/CHANGELOG.md
index 6531ce0..131be18 100644
--- a/visionstate/CHANGELOG.md
+++ b/visionstate/CHANGELOG.md
@@ -8,6 +8,8 @@
   objects and faces, the Live tab could show its new frame with the boxes of the check before — a
   person (and their face) drawn where nobody stood any more. A new frame now appears together with
   what was found in it.
+- **The face model is let go when face recognition is turned off** while a check was still busy:
+  that check could load it again, and it then stayed in memory until a restart.
 
 ## 0.8.1b4
 
diff --git a/visionstate/backend/tests/test_objects.py b/visionstate/backend/tests/test_objects.py
index 65aaa41..6540863 100644
--- a/visionstate/backend/tests/test_objects.py
+++ b/visionstate/backend/tests/test_objects.py
@@ -363,7 +363,8 @@ def test_the_frame_on_screen_changes_together_with_its_boxes(settings):
     with TestClient(create_app(settings)) as client:
         rt = client.app.state.runtime
         rt.grabber = PhotoCamera("beach")
-        sid = create(client, objects={"classes": ["dog"]}, interval_s=3600).json()["id"]
+        # Paused: only the check run here looks at the camera.
+        sid = create(client, objects={"classes": ["dog"]}, interval_s=3600, enabled=False).json()["id"]
         live = rt.live_state(sid)
         live.frame_id, live.detections = "before", [{"key": "dog", "score": 0.9, "box": [0, 0, 0.1, 0.1]}]
         seen_during: list = []
diff --git a/visionstate/backend/visionstate/engine/faces.py b/visionstate/backend/visionstate/engine/faces.py
index 2776652..58e545c 100644
--- a/visionstate/backend/visionstate/engine/faces.py
+++ b/visionstate/backend/visionstate/engine/faces.py
@@ -125,7 +125,12 @@ async def _load_faces(self) -> None:
             log.info("Face model %s loaded", spec.id)
         if replaced is not None:
             await asyncio.to_thread(backbones.release_memory)
-        for sensor_id in await asyncio.to_thread(self.face_sensor_ids):
+        sensor_ids = await asyncio.to_thread(self.face_sensor_ids)
+        if not sensor_ids:
+            # Turned off while it loaded (a check in flight asked for it): let it go again.
+            await self.faces_settled()
+            return
+        for sensor_id in sensor_ids:
             self.wake(sensor_id, force=True)
 
     def face_sensor_ids(self) -> list[int]:

From 8e472b80275a03c4577ab28d42a4e9a6963bc80d Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 00:01:34 +0200
Subject: [PATCH 13/28] Docs: tighter README, new screenshots and a demo set of
 sensors

The README is shorter: the three kinds of sensor in one table, six highlights,
a three-step install. New screenshots from a real setup: a driveway where Emma
is recognised by her face and "Our car" is told apart from any car, a real
water meter read wheel by wheel, the People page, and the driveway as a Home
Assistant device.

scripts/seed_demo.py seeds the same three sensors (and Emma) on any instance,
locally against the fake camera or on an installed app through Ingress, and
e2e/promo.mjs takes the README screenshots from it. The photos are CC0 and
public domain (tests/assets/README.md).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 CLAUDE.md                                     |  12 +-
 README.md                                     | 225 +++++--------
 docs/ARCHITECTURE.md                          |   3 +-
 docs/promo/dashboard.png                      | Bin 482027 -> 417134 bytes
 docs/promo/ha-objects.png                     | Bin 99565 -> 191694 bytes
 docs/promo/objects.png                        | Bin 864393 -> 814979 bytes
 docs/promo/people.png                         | Bin 0 -> 123510 bytes
 docs/promo/reading.png                        | Bin 795871 -> 320453 bytes
 docs/promo/wizard.png                         | Bin 739022 -> 0 bytes
 scripts/seed_demo.py                          | 303 ++++++++++++++++++
 visionstate/DOCS.md                           |   2 +-
 visionstate/backend/tests/assets/README.md    |  11 +
 .../backend/tests/assets/demo_driveway.jpg    | Bin 0 -> 87955 bytes
 .../backend/tests/assets/demo_emma_car.jpg    | Bin 0 -> 82267 bytes
 .../backend/tests/assets/demo_emma_guitar.jpg | Bin 0 -> 195864 bytes
 .../backend/tests/assets/demo_garage.jpg      | Bin 0 -> 100336 bytes
 visionstate/frontend/e2e/promo.mjs            |  40 +++
 17 files changed, 439 insertions(+), 157 deletions(-)
 create mode 100644 docs/promo/people.png
 delete mode 100644 docs/promo/wizard.png
 create mode 100644 scripts/seed_demo.py
 create mode 100644 visionstate/backend/tests/assets/demo_driveway.jpg
 create mode 100644 visionstate/backend/tests/assets/demo_emma_car.jpg
 create mode 100644 visionstate/backend/tests/assets/demo_emma_guitar.jpg
 create mode 100644 visionstate/backend/tests/assets/demo_garage.jpg
 create mode 100644 visionstate/frontend/e2e/promo.mjs

diff --git a/CLAUDE.md b/CLAUDE.md
index 337c18f..09135e4 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -144,9 +144,15 @@ Many users run Home Assistant on phones and tablets.
 - Handy: only the phone `npx playwright test --project=mobile`; one test `-g "reading sensor"`;
   watch it `--headed` or step through it `--ui`; after a failure `npx playwright show-trace
   test-results/<test>/trace.zip`.
-- **Docs screenshots** (`docs/promo/`): Playwright at 1440 px wide with device scale 1.2, against the
-  fake camera and CC0 photos, with a local MQTT broker so the header shows *MQTT connected*. Keep
-  images in one row the same height, and show the maintainer before publishing.
+- **Demo sensors:** `python scripts/seed_demo.py --url <app> --images <camera>` seeds one sensor of
+  each kind (Driveway with Emma and "Our car", Garage door, Water meter) on any instance: locally
+  with `--images http://127.0.0.1:8765/photo` (the fake camera), or on an installed app with
+  `--app <slug>` (photos from GitHub). Running it twice changes nothing.
+- **Docs screenshots** (`docs/promo/`): seed the demo sensors on a local backend with a local MQTT
+  broker (so the header shows *MQTT connected*), then `node e2e/promo.mjs <app URL>` in
+  `visionstate/frontend` (1440 px wide, device scale 1.2, one row of the README the same height).
+  The Home Assistant screenshots (`ha-*.png`) are taken by hand on a real Home Assistant. Show the
+  maintainer before publishing.
 
 ## Test pitfalls found the hard way
 
diff --git a/README.md b/README.md
index a778716..90a7bea 100644
--- a/README.md
+++ b/README.md
@@ -6,10 +6,8 @@
 </p>
 
 <p align="center">
-  <b>Teach a local AI what your camera sees — and turn it into a Home Assistant sensor.</b><br>
-  Garage door open, closed or halfway? Gate shut? Click a few examples and you have a sensor.<br>
-  Person at the door, car in the driveway, cat on the lawn? Pick the objects — no training needed.<br>
-  kWh on the meter, minutes left on the washer? Read the number.
+  <b>Turn your cameras into Home Assistant sensors — with a local AI you teach in minutes.</b><br>
+  Is the garage door open? Who just came home, and is that our car? What does the water meter say?
 </p>
 
 <p align="center">
@@ -28,121 +26,56 @@
 </p>
 
 <p align="center">
-  <img src="docs/promo/dashboard.png" alt="VisionState dashboard with a garage door, a driveway and a power meter sensor" width="900">
+  <img src="docs/promo/dashboard.png" alt="VisionState with a driveway, a garage door and a water meter sensor" width="900">
 </p>
 
-## Why VisionState?
-
-Your cameras already see whether *the garage door is half open*, *the gate is shut* or *the
-lights in the shed are still on*. VisionState turns what they see into **states** you can use in
-Home Assistant — and because every home is different, you teach it yourself, in minutes,
-without writing code or leaving Home Assistant:
-
-- 📸 **Train by clicking** — look at the live image and press the matching state (or keys `1`–`9`).
-  The model retrains in about a second after every click.
-- 🐕 **Find objects without training** — people, cars, bicycles, cats, dogs and 75 more, with a
-  count and an on/off sensor for each. Pick them, done. Wrong about your garden statue? Tap the
-  box: *not a person*. Want *our car* apart from any car? Give it its own label.
-- 🙂 **Know who is there** — name the people of your household once, by tapping them in the live
-  image, and every camera knows them: an on/off sensor per person and camera, and one per person
-  that says which camera sees them now. *Unknown person* turns on for a face it was not taught.
-  Off until you turn it on, and the faces never leave your Home Assistant.
-- 🔢 **Read numbers** — power meters, the rolling digits of water and gas meters (read wheel by
-  wheel, also while a wheel is half way between two digits), prices, the minutes left on the
-  washing machine. Counters only go up and land straight in the Energy dashboard; implausible readings are rejected, and a
-  Quality tab shows how reliably your meter is read.
-- 🎯 **Watch only what matters** — draw a box (or any shape) around the door or the driveway;
-  the AI ignores everything else.
-- 📦 **Bulk upload** — drop images, ZIP archives or a **video**; frames are extracted, duplicates
-  skipped, and the current model suggests a label for each one.
-- ⚡ **Smart triggers** — check when a motion sensor, door contact or the garage opener changes
-  (or only when it becomes one state), or when the image itself changes. No more polling every
-  few seconds.
-- 💡 **Cameras in the dark** — a light or switch is turned on for each check, and while you frame
-  the image, then off again — for a meter in a cabinet.
-- 🩺 **Finds its own mistakes** — flags training images whose label looks wrong, so one slip
-  doesn't drag the sensor down.
-- 🔁 **Gets better as you use it** — a review queue collects the frames the AI was unsure about;
-  one click turns them into training data. Forgot about it? A reminder shows up in Home Assistant
-  (or on your phone) after a week — never a ping for every frame.
-- 🕓 **Everything in one history** — every state change, object that came and went and reading,
-  with its frame; filter by sensor, by what was seen, by event and by time, and bookmark the view.
-- 🧠 **Runs locally on any CPU** — Intel, AMD and Raspberry Pi 4/5. No cloud, no GPU, no subscription.
-- 🏠 **Native Home Assistant** — sidebar app with Ingress; sensors appear through MQTT discovery,
-  named like any other integration's. Keep a sensor out of Home Assistant while you tune it.
-- 🎥 **Any camera** — every `camera.*` entity in Home Assistant (ESP32-CAM, IP cameras, NVRs…),
-  or a direct RTSP / HTTP snapshot URL.
-
-## A look inside
+## Three kinds of sensor
+
+| | What you get | How |
+|---|---|---|
+| 🚪 **States** | *Garage door open, closed or halfway*, gate shut, lights on in the shed | Click a few examples of each state; it learns in about a second |
+| 🧍 **Objects & people** | *Emma is home*, *our car is in the driveway*, a cat on the lawn, an unknown person at the door | Pick from 80 objects — no training. Name people by their face once; every camera knows them |
+| 🔢 **Readings** | The water, gas or power meter, the minutes left on the washer | Draw a box around the number. Counters only go up and land in the Energy dashboard |
+
+<p align="center">
+  <img src="docs/promo/objects.png" alt="An object sensor recognising Emma by her face and telling our car from any car" width="900">
+</p>
 
 <table>
   <tr>
-    <td width="50%" valign="top"><img src="docs/promo/objects.png" alt="An object sensor finding dogs and a person" width="100%"></td>
-    <td width="50%" valign="top"><img src="docs/promo/reading.png" alt="A reading sensor reading a power meter" width="100%"></td>
-  </tr>
-  <tr>
-    <td valign="top"><b>Objects</b> — people, cars, animals and more, found without any training. Each one becomes an on/off sensor and a count in Home Assistant.</td>
-    <td valign="top"><b>Reading</b> — the number on a display or on the rolling wheels of a water or gas meter, checked before it is published: a counter never goes down.</td>
-  </tr>
-  <tr>
-    <td colspan="2" valign="top"><img src="docs/promo/wizard.png" alt="The new sensor wizard testing an object sensor on a fresh frame" width="100%"></td>
+    <td width="50%" valign="top"><img src="docs/promo/reading.png" alt="A reading sensor reading the wheels of a real water meter" width="100%"></td>
+    <td width="50%" valign="top"><img src="docs/promo/people.png" alt="The People page with the faces taught for Emma" width="100%"></td>
   </tr>
   <tr>
-    <td colspan="2" valign="top"><b>New sensor</b> — pick a camera, draw the region and choose what to detect: your own states, objects or a number. The wizard tests it on a fresh frame right away.</td>
+    <td valign="top"><b>Real meters</b> — the rolling wheels of a water meter, read right even while a wheel is half way between two digits.</td>
+    <td valign="top"><b>People</b> — a few photos from your phone are enough to start; faces never leave your Home Assistant.</td>
   </tr>
 </table>
 
-## In Home Assistant
+## Highlights
+
+- 🧠 **100 % local on any CPU** — Intel, AMD, Raspberry Pi 4/5. No cloud, no GPU, no subscription, no telemetry.
+- 🏠 **Native Home Assistant** — a sidebar app; every sensor is a normal device through MQTT discovery.
+- 🎥 **Any camera** — every `camera.*` entity, or an RTSP / HTTP snapshot URL.
+- 🎯 **Watch only what matters** — draw a region around the door or the driveway; the rest is ignored.
+- ⚡ **Checks when something happens** — on motion, a door contact or a change in the image, and can switch on a light for a camera in the dark.
+- 🔁 **Gets better as you use it** — unsure frames go to a review queue, wrong boxes are fixed with a tap, and suspicious labels are flagged.
 
-Every sensor is a normal Home Assistant device, set up automatically through MQTT discovery —
-ready for dashboards, automations and the history graph.
+## In Home Assistant
 
 <table>
   <tr>
     <td width="50%" valign="top"><img src="docs/promo/ha-states.png" alt="A garage door state sensor as a Home Assistant device" width="100%"></td>
-    <td width="50%" valign="top"><img src="docs/promo/ha-objects.png" alt="An object sensor as a Home Assistant device" width="100%"></td>
+    <td width="50%" valign="top"><img src="docs/promo/ha-objects.png" alt="The driveway object sensor as a Home Assistant device, with Emma and our car" width="100%"></td>
   </tr>
   <tr>
-    <td valign="top"><b>State sensor</b> — the state (<code>closed</code>, <code>open</code>, …) with its confidence, the last frame, a button to check now and a switch to pause it.</td>
-    <td valign="top"><b>Object sensor</b> — an on/off sensor and a count for every object you picked, plus the last frame with the boxes drawn in.</td>
+    <td valign="top"><b>State sensor</b> — the state with its confidence, the last frame, a button to check now and a switch to pause it.</td>
+    <td valign="top"><b>Object sensor</b> — an on/off sensor and a count for every object, person and own label.</td>
   </tr>
 </table>
 
-## Install
-
-**Requirements:** Home Assistant OS or Supervised (2025.10 or newer), the **Mosquitto broker**
-app and the **MQTT** integration, and at least one camera.
-
-1. Click **Add repository** above — or go to **Settings → Apps** (called *Add-ons* in older
-   versions) **→ App store → ⋮ → Repositories** and add `https://github.com/oleost/VisionState`.
-2. Install **VisionState** (it downloads a ready-made image for your machine).
-3. Start it and open **VisionState** from the sidebar.
-
-### Beta channel
-
-New features are released as **beta** first and move to the stable app once they are tested. To
-help test them, add `https://github.com/oleost/VisionState#beta` as a repository and install
-**VisionState (beta)**.
-
-- The beta is a separate app with its own data; move sensors over with **Export** (stable) and
-  **Import** (beta). Entity ids stay the same, so automations keep working.
-- Run only one of the two apps at a time — both publish the same sensors.
-
-## Get your first sensor in five minutes
-
-1. **New sensor** → name it, pick a camera.
-2. Draw a box around the thing to watch (drag its corners to shape it to the object).
-3. Name the states, e.g. *Open*, *Closed*, *Partial*.
-4. On the **Label** tab, press the matching state a few times for each situation — about
-   **20 per state**, including some at night.
-5. Under **Settings → When to check**, add your motion sensor or garage opener as a trigger.
-
-That's it — `sensor.garage_door` is now in Home Assistant, with its confidence, the last frame,
-a button to check now and a switch to pause it:
-
 ```yaml
-# Example: notify when the garage door has been open for 10 minutes
-alias: Garage door left open
+# Example: tell me when the garage door has been open for 10 minutes
 triggers:
   - trigger: state
     entity_id: sensor.garage_door
@@ -154,73 +87,61 @@ actions:
       message: The garage door has been open for 10 minutes.
 ```
 
-The full user guide — every entity, object and reading sensors, triggers, review — is in
-[`visionstate/DOCS.md`](visionstate/DOCS.md) (also on the app's *Documentation* tab).
+## Install
+
+**Requirements:** Home Assistant OS or Supervised (2025.10 or newer), the **Mosquitto broker** app
+with the **MQTT** integration, and a camera.
+
+1. Click **Add repository** above — or add `https://github.com/oleost/VisionState` under
+   **Settings → Apps → App store → ⋮ → Repositories**.
+2. Install **VisionState**, start it and open it from the sidebar.
+3. **New sensor** → pick a camera, draw a region, choose what to watch. The wizard tests it on a
+   fresh frame right away.
+
+New features come to a separate **beta** app first: add `https://github.com/oleost/VisionState#beta`
+instead. Run only one of the two at a time (they publish the same sensors); move sensors with
+**Export** and **Import**.
+
+The user guide — every entity, triggers, faces, readings, review — is on the app's
+*Documentation* tab ([`visionstate/DOCS.md`](visionstate/DOCS.md)).
 
 ## How it works
 
 ```
-camera ─► crop to your region ─► DINOv2 (ONNX, 8-bit) ─► feature vector ─► tiny per-sensor classifier
-                                                                                     │
-Home Assistant ◄── MQTT discovery ◄── debounce + "unknown" below threshold ◄─────────┘
+camera ─► your region ─► pretrained model (ONNX, CPU) ─► small per-sensor model ─► debounce ─► MQTT ─► Home Assistant
 ```
 
-A pre-trained vision model ([DINOv2](https://github.com/facebookresearch/dinov2)) turns the
-region into a feature vector. On top of that, each sensor gets its own small classifier trained
-on *your* labelled images — which is why a handful of examples is enough and training takes
-about a second. Results are debounced so someone walking past doesn't flip the state.
-
-Object sensors use a pretrained detector instead ([D-FINE](https://github.com/Peterande/D-FINE),
-Apache-2.0, trained on the COCO objects). It finds every object in the region; each object you
-picked is reported with a count and cleared a while after it was last seen. Boxes you corrected
-teach it your camera: later boxes that clearly look like one you taught get your answer.
-
-Object sensors that recognise faces look for a face in the upper part of each person
-([YuNet](https://github.com/opencv/opencv_zoo), MIT), skip faces that are too small, turned away,
-cut off or blurry, and compare the rest with the faces you named
-([SFace](https://github.com/opencv/opencv_zoo), Apache-2.0, or for a fast PC
-[AuraFace](https://huggingface.co/fal/AuraFace-v1), Apache-2.0). Only a face seen clearly can make
-an unknown person, so someone walking past side-on is not reported as a stranger.
-
-Reading sensors read the digits in the region with a small text recognizer
-([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR), Apache-2.0) that may only output digits,
-then check the value (a counter never goes down) before publishing it. Mechanical counters are
-read by a small model made for VisionState that tells how far each wheel has turned; the wheels
-are read together, the way they turn ([`tools/wheelreader`](tools/wheelreader), trained on
-public-domain meter photos and drawn wheels).
-
-Everything stays on your machine: images live in `/media/visionstate`, models and settings in
-the app's data folder (included in Home Assistant backups).
+- **States:** [DINOv2](https://github.com/facebookresearch/dinov2) turns the region into a feature
+  vector; a tiny classifier per sensor, trained on *your* examples, picks the state.
+- **Objects:** [D-FINE](https://github.com/Peterande/D-FINE) finds the COCO objects; boxes you
+  corrected or gave an own label teach it your camera.
+- **Faces:** [YuNet](https://github.com/opencv/opencv_zoo) finds a face on each person, and only a
+  clear one is compared ([SFace](https://github.com/opencv/opencv_zoo), or
+  [AuraFace](https://huggingface.co/fal/AuraFace-v1) on a fast PC) with the faces you named.
+- **Readings:** [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) reads digits; mechanical
+  counters use a small wheel reader made for VisionState ([`tools/wheelreader`](tools/wheelreader)).
+  Every value is checked before it is published.
 
 ## Privacy & security
 
-- 100 % local — no cloud services, no telemetry.
-- Only reachable through Home Assistant Ingress (requires a Home Assistant login).
-- Camera passwords are hidden in logs and removed from exported sensor bundles.
-- Face recognition is off until you turn it on for a camera. The faces you teach stay in the app's
-  own storage (not the media folder); an export includes them only if you tick it.
-
-## Development
-
-```
-visionstate/          the Home Assistant app — backend/ (Python 3.14, FastAPI, ONNX Runtime) and frontend/ (Svelte 5)
-tools/wheelreader/    training code of the wheel reader model
-scripts/              fake camera for local testing, channel switch, CI helpers
-docs/                 design (SCOPE), map of the code (ARCHITECTURE), releases (RELEASING)
-```
+- Nothing leaves your machine; there is no telemetry.
+- Only reachable through Home Assistant Ingress (a Home Assistant login).
+- Camera passwords are hidden in logs and removed from exports.
+- Face recognition is off until you turn it on for a camera; faces are kept in the app's own
+  storage and exported only if you tick it.
 
-New features land on the `beta` branch first and reach `main` (stable) only after testing; please
-open pull requests against `beta`. [`CLAUDE.md`](CLAUDE.md) is the contributor guide — how to run it
-locally against a fake camera, the checks, the UI tests on desktop and phones, and the pitfalls we
-ran into — written for people and AI coding assistants alike. Security issues: see
-[`SECURITY.md`](SECURITY.md).
+## Contributing
 
-Issues and ideas are welcome in [GitHub Issues](https://github.com/oleost/VisionState/issues).
+Pull requests go to the `beta` branch. [`CLAUDE.md`](CLAUDE.md) is the contributor guide (run it
+locally against a fake camera, checks, UI tests on desktop and phones) and
+[`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md) maps the code. Issues and ideas:
+[GitHub Issues](https://github.com/oleost/VisionState/issues). Security: [`SECURITY.md`](SECURITY.md).
 
 ## Licence
 
-[Apache-2.0](LICENSE). The bundled models are Apache-2.0 as well: DINOv2 (Meta), D-FINE,
-PaddleOCR PP-OCRv6 and the VisionState wheel reader. The face models are downloaded only when a
-sensor recognises faces: YuNet (MIT) with SFace or AuraFace (Apache-2.0).
+[Apache-2.0](LICENSE). Bundled models: DINOv2 (Meta), D-FINE, PaddleOCR PP-OCRv6 and the
+VisionState wheel reader, all Apache-2.0. Face models, downloaded only when a camera recognises
+faces: YuNet (MIT), SFace or AuraFace (Apache-2.0). The screenshots use CC0 and public-domain
+images.
 
 If VisionState is useful to you, you can [buy me a coffee](https://buymeacoffee.com/o1ep) ☕
diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index 5d989b8..75ead75 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -11,7 +11,8 @@ repository.yaml, README.md, CLAUDE.md, SECURITY.md
 visionstate/            the Home Assistant app: config.yaml, Dockerfile, DOCS.md, CHANGELOG.md, icon/logo
   backend/              Python package `visionstate` + tests (the Dockerfile builds from visionstate/)
   frontend/             Svelte app, Playwright tests in e2e/
-scripts/                channel.py (stable/beta config), fake_camera.py (test camera), CI helpers:
+scripts/                channel.py (stable/beta config), fake_camera.py (test camera), seed_demo.py
+                        (the demo sensors), CI helpers:
                         check_options.py, cpu_probe.py, upgrade_test.sh, discovery_ids.py
 tools/wheelreader/      training code of the wheel reader model (not part of the app image)
 docs/                   SCOPE.md, ARCHITECTURE.md, RELEASING.md, WHEEL_READER_PLAN.md,
diff --git a/docs/promo/dashboard.png b/docs/promo/dashboard.png
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diff --git a/scripts/seed_demo.py b/scripts/seed_demo.py
new file mode 100644
index 0000000..35748ef
--- /dev/null
+++ b/scripts/seed_demo.py
@@ -0,0 +1,303 @@
+"""Seed the demo sensors: one of each kind, the sensors on the README screenshots.
+
+    python scripts/seed_demo.py --url http://127.0.0.1:8099 --images http://127.0.0.1:8765/photo
+    python scripts/seed_demo.py --app c088851d_visionstate      # an installed app, through Ingress
+
+- Driveway (objects): a person and a car; Emma is recognised by her face (taught from two other
+  photos of her) and the car is taught as the own label "Our car".
+- Garage door (states): trained on 36 frames drawn from one photo (closed, open, partial).
+- Water meter (reading): the wheels of a real water meter, one of them half way between digits.
+
+The cameras are HTTP snapshot URLs: ``--images`` + ``/<name>.jpg``, by default the test photos on
+GitHub (reachable from any Home Assistant). With the fake camera use ``http://127.0.0.1:<port>/photo``.
+Sensors and people that already exist (same name) are left alone, so running it twice is harmless.
+``--app`` needs HA_URL and HA_TOKEN (a long-lived token of an administrator) in the environment.
+Run it with the backend's Python (it needs httpx, numpy, Pillow and, for --app, websockets).
+Images: visionstate/backend/tests/assets (sources and licences in its README.md).
+"""
+
+import argparse
+import asyncio
+import io
+import json
+import os
+import random
+import sys
+import time
+from pathlib import Path
+
+import httpx
+import numpy as np
+from PIL import Image, ImageDraw, ImageEnhance, ImageFilter
+
+ASSETS = (
+    Path(__file__).resolve().parent.parent
+    / "visionstate"
+    / "backend"
+    / "tests"
+    / "assets"
+)
+GITHUB = "https://raw.githubusercontent.com/oleost/VisionState/beta/visionstate/backend/tests/assets"
+
+# The garage door in demo_garage.jpg (pixels) and the sensor's region around it.
+DOOR = {"tl": (130, 45), "tr": (620, 130), "br": (615, 585), "bl": (130, 685)}
+GARAGE_ROI = {"x": 0.1, "y": 0.06, "w": 0.43, "h": 0.94}
+
+
+def garage_frames(per_state: int = 12) -> dict[str, list[bytes]]:
+    """Frames of the garage door closed, open and half open, with some light and camera jitter."""
+    rnd = random.Random(7)
+    base = Image.open(ASSETS / "demo_garage.jpg").convert("RGB")
+    w, h = base.size
+
+    def lerp(a, b, t):
+        return (a[0] + (b[0] - a[0]) * t, a[1] + (b[1] - a[1]) * t)
+
+    def raised(frac: float) -> Image.Image:
+        """The door lifted by ``frac``: daylight shows below it."""
+        top_l, top_r = (
+            lerp(DOOR["tl"], DOOR["bl"], 1 - frac),
+            lerp(DOOR["tr"], DOOR["br"], 1 - frac),
+        )
+        mask = Image.new("L", (w, h), 0)
+        ImageDraw.Draw(mask).polygon([top_l, top_r, DOOR["br"], DOOR["bl"]], fill=255)
+        m = (
+            np.asarray(mask.filter(ImageFilter.GaussianBlur(2)), dtype=np.float32)[
+                :, :, None
+            ]
+            / 255
+        )
+        outside = np.clip(
+            235
+            - 70 * np.linspace(0, 1, h)[:, None]
+            + np.random.default_rng(1).normal(0, 6, (h, w)),
+            0,
+            255,
+        )
+        out = np.asarray(base, dtype=np.float32) * (1 - m) + outside[:, :, None] * m
+        img = Image.fromarray(np.clip(out, 0, 255).astype("uint8"))
+        ImageDraw.Draw(img).line([top_l, top_r], fill=(70, 70, 70), width=6)
+        glow = Image.new("L", (w, h), 0)
+        ImageDraw.Draw(glow).polygon(
+            [DOOR["bl"], DOOR["br"], (w, h), (0, h)], fill=int(90 * frac)
+        )
+        return Image.composite(
+            Image.new("RGB", (w, h), (255, 255, 255)),
+            img,
+            glow.filter(ImageFilter.GaussianBlur(40)),
+        )
+
+    def jitter(img: Image.Image) -> bytes:
+        img = ImageEnhance.Brightness(img).enhance(rnd.uniform(0.75, 1.2))
+        img = ImageEnhance.Contrast(img).enhance(rnd.uniform(0.85, 1.15))
+        dx, dy = rnd.randint(-10, 10), rnd.randint(-8, 8)
+        img = img.transform(
+            img.size,
+            Image.Transform.AFFINE,
+            (1, 0, dx, 0, 1, dy),
+            fillcolor=(40, 40, 40),
+        )
+        noise = np.random.default_rng(rnd.randint(0, 1 << 30)).normal(
+            0, rnd.uniform(2, 7), (h, w, 3)
+        )
+        img = Image.fromarray(
+            np.clip(np.asarray(img, dtype=np.float32) + noise, 0, 255).astype("uint8")
+        )
+        buf = io.BytesIO()
+        img.filter(ImageFilter.GaussianBlur(rnd.uniform(0, 1.2))).save(
+            buf, format="JPEG", quality=88
+        )
+        return buf.getvalue()
+
+    make = {
+        "closed": lambda: base,
+        "open": lambda: raised(rnd.uniform(0.85, 0.95)),
+        "partial": lambda: raised(rnd.uniform(0.35, 0.6)),
+    }
+    return {key: [jitter(fn()) for _ in range(per_state)] for key, fn in make.items()}
+
+
+class Api:
+    """The app's API, directly (``--url``) or through Home Assistant Ingress (``--app``)."""
+
+    def __init__(self, base: str, cookies: dict | None = None):
+        self.client = httpx.Client(
+            base_url=base.rstrip("/") + "/api/v1", cookies=cookies, timeout=180
+        )
+
+    def call(self, method: str, path: str, **kwargs):
+        r = self.client.request(method, path, **kwargs)
+        if r.status_code >= 400:
+            sys.exit(f"{method} {path}: {r.status_code} {r.text[:300]}")
+        return r.json() if r.content else None
+
+
+def ingress(app: str) -> Api:
+    from websockets.asyncio.client import connect
+
+    ha = os.environ["HA_URL"].rstrip("/")
+    token = os.environ["HA_TOKEN"]
+
+    async def supervisor(endpoint: str, method: str = "get") -> dict:
+        async with connect(ha.replace("http", "ws", 1) + "/api/websocket") as ws:
+            await ws.recv()
+            await ws.send(json.dumps({"type": "auth", "access_token": token}))
+            await ws.recv()
+            await ws.send(
+                json.dumps(
+                    {
+                        "id": 1,
+                        "type": "supervisor/api",
+                        "endpoint": endpoint,
+                        "method": method,
+                    }
+                )
+            )
+            while True:
+                msg = json.loads(await ws.recv())
+                if msg.get("id") == 1:
+                    if not msg.get("success"):
+                        sys.exit(f"Supervisor {endpoint}: {msg.get('error')}")
+                    return msg["result"]
+
+    session = asyncio.run(supervisor("/ingress/session", "post"))["session"]
+    entry = asyncio.run(supervisor(f"/addons/{app}/info"))["ingress_entry"]
+    return Api(ha + entry, {"ingress_session": session})
+
+
+def wait(what: str, check, timeout: float = 180):
+    end = time.time() + timeout
+    while time.time() < end:
+        value = check()
+        if value:
+            return value
+        time.sleep(2)
+    sys.exit(f"Timed out waiting for {what}")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
+    target = parser.add_mutually_exclusive_group(required=True)
+    target.add_argument("--url", help="the app's address, e.g. http://127.0.0.1:8099")
+    target.add_argument(
+        "--app", help="slug of an installed app, reached through Ingress"
+    )
+    parser.add_argument(
+        "--images",
+        default=GITHUB,
+        help="where the cameras get the photos (default: GitHub)",
+    )
+    args = parser.parse_args()
+    api = Api(args.url) if args.url else ingress(args.app)
+    photo = lambda name: f"{args.images.rstrip('/')}/{name}.jpg"
+    existing = {s["name"]: s for s in api.call("GET", "/sensors")}
+
+    def create(spec: dict) -> dict | None:
+        if spec["name"] in existing:
+            print(f"{spec['name']}: already there")
+            return None
+        sensor = api.call("POST", "/sensors", json=spec)
+        print(f"{spec['name']}: created (id {sensor['id']})")
+        return sensor
+
+    garage = create(
+        {
+            "name": "Garage door",
+            "source_type": "http",
+            "source": photo("demo_garage"),
+            "roi": GARAGE_ROI,
+            "states": [{"name": "Closed"}, {"name": "Open"}, {"name": "Partial"}],
+            "interval_s": 30,
+        }
+    )
+    if garage:
+        for key, frames in garage_frames().items():
+            files = [
+                ("files", (f"{key}_{i:02d}.jpg", data, "image/jpeg"))
+                for i, data in enumerate(frames)
+            ]
+            api.call(
+                "POST",
+                f"/sensors/{garage['id']}/uploads",
+                files=files,
+                data={"state_key": key},
+            )
+
+    meter = create(
+        {
+            "name": "Water meter",
+            "kind": "reading",
+            "source_type": "http",
+            "source": photo("counter_turning"),
+            "reading": {
+                "mode": "counter",
+                "display": "counter",
+                "digits": 7,
+                "decimals": 3,
+                "unit": "m³",
+                "device_class": "water",
+            },
+            "interval_s": 30,
+            "debounce": 1,
+        }
+    )
+
+    driveway = create(
+        {
+            "name": "Driveway",
+            "kind": "objects",
+            "source_type": "http",
+            "source": photo("demo_driveway"),
+            "objects": {
+                "classes": ["person", "car"],
+                "faces": {"enabled": True, "ask_unknown": False},
+            },
+            "interval_s": 10,
+        }
+    )
+    if not any(p["name"] == "Emma" for p in api.call("GET", "/people")["people"]):
+        # The face model loads (or downloads) once a sensor recognises faces.
+        files = [
+            ("files", (f"{n}.jpg", (ASSETS / f"{n}.jpg").read_bytes(), "image/jpeg"))
+            for n in ("demo_emma_car", "demo_emma_guitar")
+        ]
+        for _ in range(60):
+            r = api.client.post(
+                "/people/photos", files=files, data={"new_person": "Emma"}
+            )
+            if r.status_code != 409:
+                break
+            time.sleep(3)
+        print("Emma:", r.status_code, r.json().get("added"), "faces")
+    if driveway:
+
+        def car():
+            view = api.call("GET", f"/sensors/{driveway['id']}")
+            cars = [d for d in view["objects"]["detections"] if d["key"] == "car"]
+            return (view["live"]["frame_id"], cars[0]) if cars else None
+
+        frame_id, box = wait("the car on the driveway", car)
+        api.call(
+            "POST",
+            f"/sensors/{driveway['id']}/taught",
+            json={
+                "frame_id": frame_id,
+                "box": box["box"],
+                "detected": "car",
+                "score": box["score"],
+                "new_label": "Our car",
+            },
+        )
+        print("Our car: taught")
+    if meter:
+        value = wait(
+            "the water meter",
+            lambda: api.call("GET", f"/sensors/{meter['id']}")["reading"]["value"],
+        )
+        print("Water meter reads", value)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/visionstate/DOCS.md b/visionstate/DOCS.md
index 308f71d..e8bb72e 100644
--- a/visionstate/DOCS.md
+++ b/visionstate/DOCS.md
@@ -1,6 +1,6 @@
 # VisionState
 
-![VisionState with a garage door, a driveway and a power meter sensor](https://raw.githubusercontent.com/oleost/VisionState/main/docs/promo/dashboard.png)
+![VisionState with a driveway, a garage door and a water meter sensor](https://raw.githubusercontent.com/oleost/VisionState/main/docs/promo/dashboard.png)
 
 Teach a local AI to recognise **states** in camera images — a garage door that is `open`,
 `closed` or `partial`, a gate, a parking spot, a light — and use the result as a normal
diff --git a/visionstate/backend/tests/assets/README.md b/visionstate/backend/tests/assets/README.md
index a198e78..665c6ff 100644
--- a/visionstate/backend/tests/assets/README.md
+++ b/visionstate/backend/tests/assets/README.md
@@ -30,3 +30,14 @@ the region the same way.
 
 `lcd.png` is drawn by `tests/displays.py` (our own, no licence questions) and used by the CI smoke
 test for the number reader; the reading tests draw their displays on the fly.
+
+The demo sensors (`scripts/seed_demo.py`, the README screenshots) use:
+
+| File | What | Source |
+|---|---|---|
+| `demo_driveway.jpg` | Emma on her car, cut to 16:9 with a camera's date and name drawn on | [Woman Car](https://stocksnap.io/photo/woman-car-WKVLDV0K0G) by Candace McDaniel, StockSnap (CC0) |
+| `demo_emma_car.jpg` | Emma in the same car: a face to teach | [Woman Car](https://stocksnap.io/photo/woman-car-XHLANQWHBS), same author (CC0) |
+| `demo_emma_guitar.jpg` | Emma on the car with a guitar: a face to teach | [Woman Car](https://stocksnap.io/photo/woman-car-TV3QFON1JP), same author (CC0) |
+| `demo_garage.jpg` | A garage door from inside, cut; the script draws it open and half open | [Utility building interior view of wood panel overhead garage doors](https://commons.wikimedia.org/wiki/File:UTILITY_BUILDING_INTERIOR_VIEW_OF_WOOD_PANEL_OVERHEAD_GARAGE_DOORS,_LOOKING_NORTHWEST._-_Tucson_Plant_Materials_Center,_General_Utility_Building,_3241_North_Romero_Road,_HABS_ARIZ,10-TUCSO,32C-7.tif), HABS (public domain) |
+
+"Emma" is a made-up name. The water meter is `counter_turning.jpg` above.
diff --git a/visionstate/backend/tests/assets/demo_driveway.jpg b/visionstate/backend/tests/assets/demo_driveway.jpg
new file mode 100644
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A?*IS*

literal 0
HcmV?d00001

diff --git a/visionstate/frontend/e2e/promo.mjs b/visionstate/frontend/e2e/promo.mjs
new file mode 100644
index 0000000..a8d3827
--- /dev/null
+++ b/visionstate/frontend/e2e/promo.mjs
@@ -0,0 +1,40 @@
+// The README screenshots (docs/promo/), from an app seeded with scripts/seed_demo.py.
+//   node e2e/promo.mjs [app URL, default http://127.0.0.1:8099] [output folder, default ../../docs/promo]
+// 1440 px wide at device scale 1.2; images in one row of the README get the same height.
+import { chromium } from '@playwright/test';
+import path from 'node:path';
+
+const base = (process.argv[2] ?? 'http://127.0.0.1:8099').replace(/\/$/, '');
+const out = process.argv[3] ?? path.resolve(import.meta.dirname, '..', '..', '..', 'docs', 'promo');
+
+const sensors = await (await fetch(`${base}/api/v1/sensors`)).json();
+const id = (name) => {
+  const sensor = sensors.find((s) => s.name === name);
+  if (!sensor) throw new Error(`No sensor called ${name}: run scripts/seed_demo.py first`);
+  return sensor.id;
+};
+// [page, file, height in CSS pixels]
+const shots = [
+  ['', 'dashboard', 640],
+  [`sensors/${id('Driveway')}`, 'objects', 890],
+  [`sensors/${id('Water meter')}`, 'reading', 790],
+  ['people', 'people', 790],
+];
+
+// A fresh check of every sensor, so none shows "unknown" from before it was trained.
+for (const s of sensors) await fetch(`${base}/api/v1/sensors/${s.id}/classify`, { method: 'POST' });
+await new Promise((resolve) => setTimeout(resolve, 5000));
+
+const browser = await chromium.launch();
+const page = await browser.newPage({ viewport: { width: 1440, height: 1100 }, deviceScaleFactor: 1.2 });
+const errors = [];
+page.on('console', (m) => m.type() === 'error' && errors.push(m.text()));
+for (const [hash, name, height] of shots) {
+  await page.goto(`${base}/#/${hash}`);
+  await page.waitForTimeout(4000); // the live frame and its boxes
+  await page.waitForFunction(() => [...document.images].every((img) => img.complete));
+  await page.screenshot({ path: path.join(out, `${name}.png`), clip: { x: 0, y: 0, width: 1440, height } });
+  console.log(`${name}.png`);
+}
+await browser.close();
+if (errors.length) console.log('Console errors:', errors);

From 2e3462222ecf5c4da0be89894fa01a638d3ad573 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 00:04:03 +0200
Subject: [PATCH 14/28] seed_demo.py: say clearly when an app cannot teach
 faces from photos

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 scripts/seed_demo.py | 60 +++++++++++---------------------------------
 1 file changed, 15 insertions(+), 45 deletions(-)

diff --git a/scripts/seed_demo.py b/scripts/seed_demo.py
index 35748ef..3831c55 100644
--- a/scripts/seed_demo.py
+++ b/scripts/seed_demo.py
@@ -30,13 +30,7 @@
 import numpy as np
 from PIL import Image, ImageDraw, ImageEnhance, ImageFilter
 
-ASSETS = (
-    Path(__file__).resolve().parent.parent
-    / "visionstate"
-    / "backend"
-    / "tests"
-    / "assets"
-)
+ASSETS = Path(__file__).resolve().parent.parent / "visionstate" / "backend" / "tests" / "assets"
 GITHUB = "https://raw.githubusercontent.com/oleost/VisionState/beta/visionstate/backend/tests/assets"
 
 # The garage door in demo_garage.jpg (pixels) and the sensor's region around it.
@@ -61,16 +55,9 @@ def raised(frac: float) -> Image.Image:
         )
         mask = Image.new("L", (w, h), 0)
         ImageDraw.Draw(mask).polygon([top_l, top_r, DOOR["br"], DOOR["bl"]], fill=255)
-        m = (
-            np.asarray(mask.filter(ImageFilter.GaussianBlur(2)), dtype=np.float32)[
-                :, :, None
-            ]
-            / 255
-        )
+        m = np.asarray(mask.filter(ImageFilter.GaussianBlur(2)), dtype=np.float32)[:, :, None] / 255
         outside = np.clip(
-            235
-            - 70 * np.linspace(0, 1, h)[:, None]
-            + np.random.default_rng(1).normal(0, 6, (h, w)),
+            235 - 70 * np.linspace(0, 1, h)[:, None] + np.random.default_rng(1).normal(0, 6, (h, w)),
             0,
             255,
         )
@@ -78,9 +65,7 @@ def raised(frac: float) -> Image.Image:
         img = Image.fromarray(np.clip(out, 0, 255).astype("uint8"))
         ImageDraw.Draw(img).line([top_l, top_r], fill=(70, 70, 70), width=6)
         glow = Image.new("L", (w, h), 0)
-        ImageDraw.Draw(glow).polygon(
-            [DOOR["bl"], DOOR["br"], (w, h), (0, h)], fill=int(90 * frac)
-        )
+        ImageDraw.Draw(glow).polygon([DOOR["bl"], DOOR["br"], (w, h), (0, h)], fill=int(90 * frac))
         return Image.composite(
             Image.new("RGB", (w, h), (255, 255, 255)),
             img,
@@ -97,16 +82,10 @@ def jitter(img: Image.Image) -> bytes:
             (1, 0, dx, 0, 1, dy),
             fillcolor=(40, 40, 40),
         )
-        noise = np.random.default_rng(rnd.randint(0, 1 << 30)).normal(
-            0, rnd.uniform(2, 7), (h, w, 3)
-        )
-        img = Image.fromarray(
-            np.clip(np.asarray(img, dtype=np.float32) + noise, 0, 255).astype("uint8")
-        )
+        noise = np.random.default_rng(rnd.randint(0, 1 << 30)).normal(0, rnd.uniform(2, 7), (h, w, 3))
+        img = Image.fromarray(np.clip(np.asarray(img, dtype=np.float32) + noise, 0, 255).astype("uint8"))
         buf = io.BytesIO()
-        img.filter(ImageFilter.GaussianBlur(rnd.uniform(0, 1.2))).save(
-            buf, format="JPEG", quality=88
-        )
+        img.filter(ImageFilter.GaussianBlur(rnd.uniform(0, 1.2))).save(buf, format="JPEG", quality=88)
         return buf.getvalue()
 
     make = {
@@ -121,9 +100,7 @@ class Api:
     """The app's API, directly (``--url``) or through Home Assistant Ingress (``--app``)."""
 
     def __init__(self, base: str, cookies: dict | None = None):
-        self.client = httpx.Client(
-            base_url=base.rstrip("/") + "/api/v1", cookies=cookies, timeout=180
-        )
+        self.client = httpx.Client(base_url=base.rstrip("/") + "/api/v1", cookies=cookies, timeout=180)
 
     def call(self, method: str, path: str, **kwargs):
         r = self.client.request(method, path, **kwargs)
@@ -176,14 +153,10 @@ def wait(what: str, check, timeout: float = 180):
 
 
 def main() -> None:
-    parser = argparse.ArgumentParser(
-        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
-    )
+    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
     target = parser.add_mutually_exclusive_group(required=True)
     target.add_argument("--url", help="the app's address, e.g. http://127.0.0.1:8099")
-    target.add_argument(
-        "--app", help="slug of an installed app, reached through Ingress"
-    )
+    target.add_argument("--app", help="slug of an installed app, reached through Ingress")
     parser.add_argument(
         "--images",
         default=GITHUB,
@@ -214,10 +187,7 @@ def create(spec: dict) -> dict | None:
     )
     if garage:
         for key, frames in garage_frames().items():
-            files = [
-                ("files", (f"{key}_{i:02d}.jpg", data, "image/jpeg"))
-                for i, data in enumerate(frames)
-            ]
+            files = [("files", (f"{key}_{i:02d}.jpg", data, "image/jpeg")) for i, data in enumerate(frames)]
             api.call(
                 "POST",
                 f"/sensors/{garage['id']}/uploads",
@@ -264,13 +234,13 @@ def create(spec: dict) -> dict | None:
             for n in ("demo_emma_car", "demo_emma_guitar")
         ]
         for _ in range(60):
-            r = api.client.post(
-                "/people/photos", files=files, data={"new_person": "Emma"}
-            )
+            r = api.client.post("/people/photos", files=files, data={"new_person": "Emma"})
             if r.status_code != 409:
                 break
             time.sleep(3)
-        print("Emma:", r.status_code, r.json().get("added"), "faces")
+        if r.status_code >= 400:
+            sys.exit(f"Emma: {r.status_code} {r.text[:300]} (an app older than 0.8.1b4 cannot teach faces from photos)")
+        print(f"Emma: {r.json()['added']} faces taught")
     if driveway:
 
         def car():

From 7d90e81db8c98ebe8df2540043bb0252b9e66f61 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 00:18:45 +0200
Subject: [PATCH 15/28] Docs: the demo sensors and README screenshots in the
 release checklist and the code map

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 docs/ARCHITECTURE.md | 2 +-
 docs/RELEASING.md    | 6 ++++--
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index 75ead75..3a99d63 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -10,7 +10,7 @@ it is in [`CLAUDE.md`](../CLAUDE.md).
 repository.yaml, README.md, CLAUDE.md, SECURITY.md
 visionstate/            the Home Assistant app: config.yaml, Dockerfile, DOCS.md, CHANGELOG.md, icon/logo
   backend/              Python package `visionstate` + tests (the Dockerfile builds from visionstate/)
-  frontend/             Svelte app, Playwright tests in e2e/
+  frontend/             Svelte app, Playwright tests in e2e/ (and promo.mjs: the README screenshots)
 scripts/                channel.py (stable/beta config), fake_camera.py (test camera), seed_demo.py
                         (the demo sensors), CI helpers:
                         check_options.py, cpu_probe.py, upgrade_test.sh, discovery_ids.py
diff --git a/docs/RELEASING.md b/docs/RELEASING.md
index ffb2081..7b3c323 100644
--- a/docs/RELEASING.md
+++ b/docs/RELEASING.md
@@ -82,12 +82,14 @@ all of it and tell the maintainer what was checked and what was found.
    - open the app in Home Assistant at desktop width and on a phone (Ingress);
    - prepare the **update from the last stable release**: the stable app is installed there too. It
      shares MQTT topics and unique IDs with the beta app, so never run both: stop the beta app,
-     start the stable one, and give it one sensor of each kind (exports of the standing test sensors).
+     start the stable one, and give it one sensor of each kind (exports of the standing test sensors,
+     or `scripts/seed_demo.py --app <slug>` once the stable release can teach faces from photos).
 3. **What changed since the last stable release**: new or upgraded dependencies have a licence that
    fits Apache-2.0 (`git diff vX.Y.Z -- visionstate/backend/requirements.txt
    visionstate/frontend/package.json`); `homeassistant:` in config.yaml still names the oldest Home
    Assistant that works; the docs describe what is being released (the roadmap in `SCOPE.md` and
-   the status in `WHEEL_READER_PLAN.md` included).
+   the status in `WHEEL_READER_PLAN.md` included), and the README screenshots show today's app
+   (`docs/promo/`, how to take them in `CLAUDE.md`).
 
 ## Promote to stable
 

From 2e66ddffc84e9078ae3e950fd0e9412bef6a8309 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 10:53:09 +0200
Subject: [PATCH 16/28] Docs: faces and the larger models in the 0.9.0 roadmap;
 AI Act guidelines checked

The high-risk guidelines were still a draft on 2026-10-12. Of phase 4, teaching from photos is built.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 docs/FACE_RECOGNITION_PLAN.md | 7 +++++--
 docs/SCOPE.md                 | 4 ++--
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/docs/FACE_RECOGNITION_PLAN.md b/docs/FACE_RECOGNITION_PLAN.md
index 419ceed..5560455 100644
--- a/docs/FACE_RECOGNITION_PLAN.md
+++ b/docs/FACE_RECOGNITION_PLAN.md
@@ -150,7 +150,9 @@ Not legal advice; the points to design for, and to say plainly in DOCS.md.
     or must be left out.
   - Timing: the Annex III obligations apply from 2 December 2027 after the "Digital Omnibus"
     (Regulation (EU) 2026/1744, per secondary sources). The Commission's draft guidelines on
-    high-risk classification (May 2026) are to be checked once final.
+    high-risk classification (May 2026) are to be checked once final; on 2026-10-12, before face
+    recognition reached the stable channel, no final version was found — only the draft of 19 May
+    2026 — and nothing in the draft changes the verdict below.
   - The Art. 5 prohibition on *untargeted* scraping of facial images from the internet or CCTV to
     build or expand recognition databases applies to everyone: VisionState must never grow a
     gallery of strangers by itself.
@@ -484,7 +486,8 @@ Where the build differs from §3–§9:
   frame" is not built), and a history row of a person shows the frame, not the face.
 - History has no extra filter for people: people are own labels, so the *What* filter already
   lists them, and *Unknown person* with them.
-- Separate day and night faces per person (§11) and phase 4 are not built.
+- Separate day and night faces per person (§11) are not built. Of phase 4, only teaching from
+  photos is: a person's photos, a short video or a ZIP on the **People** page (origin `upload`).
 - After the first tries the maintainer asked for: the faces' limits (smallest face and the four
   similarities) set for all sensors under **Settings → Recognising faces** and per sensor, like the
   teaching limits (§9.6 had only *Smallest face*, per sensor); turning faces on in the wizard too
diff --git a/docs/SCOPE.md b/docs/SCOPE.md
index 3af9009..fb83334 100644
--- a/docs/SCOPE.md
+++ b/docs/SCOPE.md
@@ -605,8 +605,8 @@ is part of backups and needs no restart.
 | **History** ✅ | History page with filters shared by every sensor's History tab, the images behind the Quality tab's mix-ups | 0.7.0 |
 | **Wheel reader** ✅ | Mechanical counters read wheel by wheel with a model made for VisionState; wheels mid-turn read right; export v2 of readings | 0.8.0 (betas 0.7.1b3–b6) |
 | **Review reminder** ✅ | A notification in Home Assistant (optionally a push) when frames have waited for review a long time | 0.8.0 (beta 0.7.1b1) |
-| **Larger models, tunable teaching** ✅ | D-FINE M and L, DINOv2 base, a model comparison in Settings, teaching limits globally and per sensor | beta 0.8.1b1 |
-| **Recognising faces** | Faces in person boxes (YuNet), people taught by face and shared by every camera (SFace, or AuraFace for a fast PC), a People page, an entity per person saying where they are, unknown person, review questions, export opt-in — phases 1–3 of [`FACE_RECOGNITION_PLAN.md`](FACE_RECOGNITION_PLAN.md) | in development |
+| **Larger models, tunable teaching** ✅ | D-FINE M and L, DINOv2 base, a model comparison in Settings, teaching limits globally and per sensor | 0.9.0 (beta 0.8.1b1) |
+| **Recognising faces** ✅ | Faces in person boxes (YuNet), people taught by face and shared by every camera (SFace, or AuraFace for a fast PC), a People page, an entity per person saying where they are, unknown person, review questions, export opt-in — phases 1–3 of [`FACE_RECOGNITION_PLAN.md`](FACE_RECOGNITION_PLAN.md), and teaching a face from photos | 0.9.0 (betas 0.8.1b3–b5) |
 | **Better counter readings** | Benchmark, the last value as an expectation, calibrated confidence, extra decimal, self-calibrating regions, wheels-v2, learning per sensor — status per phase in [`WHEEL_READER_PLAN.md`](WHEEL_READER_PLAN.md) | planned |
 
 **Open ideas** (not scheduled): more on the History page (export of a filtered selection, a

From 411e7a18f7681719a3957eb9000f5ccfe2aed000 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 10:53:10 +0200
Subject: [PATCH 17/28] Log only ONNX Runtime errors: SFace's file made 174
 warnings per load

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 visionstate/backend/tests/test_faces.py      | 7 +++++++
 visionstate/backend/visionstate/backbones.py | 4 +++-
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/visionstate/backend/tests/test_faces.py b/visionstate/backend/tests/test_faces.py
index 2d41f46..19d3cc7 100644
--- a/visionstate/backend/tests/test_faces.py
+++ b/visionstate/backend/tests/test_faces.py
@@ -183,6 +183,13 @@ def photo_faces(recogniser, name):
     return result
 
 
+@pytest.mark.parametrize("spec", [on_disk(s) for s in faces.RECOGNISERS.values()])
+def test_loading_a_face_model_keeps_the_log_quiet(spec, capfd):
+    """SFace's file lists its weights as graph inputs: ONNX Runtime wrote 174 warnings per load."""
+    faces.Recogniser(spec, MODEL_DIR / spec.filename)
+    assert "onnxruntime" not in capfd.readouterr().err.replace("\x00", "")
+
+
 @pytest.mark.parametrize("spec", [on_disk(s) for s in faces.RECOGNISERS.values()])
 def test_every_face_model_tells_people_apart_in_real_photos(spec):
     recogniser = faces.Recogniser(spec, MODEL_DIR / spec.filename)
diff --git a/visionstate/backend/visionstate/backbones.py b/visionstate/backend/visionstate/backbones.py
index 23ce9a5..887b845 100644
--- a/visionstate/backend/visionstate/backbones.py
+++ b/visionstate/backend/visionstate/backbones.py
@@ -159,11 +159,13 @@ def cpu_session(model_path: Path):
     """An ONNX Runtime session on the CPU — the only target, deliberately (docs/SCOPE.md).
 
     Only the CPU provider is passed, so nothing else ONNX Runtime ships with (such as its
-    Azure provider) is ever used.
+    Azure provider) is ever used. Only errors are logged: SFace's file lists its weights as graph
+    inputs too, and ONNX Runtime warned about every one of them (174 lines) each time it loaded.
     """
     import onnxruntime as ort
 
     options = ort.SessionOptions()
+    options.log_severity_level = 3
     options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
     return ort.InferenceSession(str(model_path), sess_options=options, providers=["CPUExecutionProvider"])
 

From af03d0b6a35034da29d00b1a5a9d229ef429b5ae Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 10:53:17 +0200
Subject: [PATCH 18/28] 0.9.0b1

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 visionstate/CHANGELOG.md | 8 ++++++++
 visionstate/config.yaml  | 2 +-
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/visionstate/CHANGELOG.md b/visionstate/CHANGELOG.md
index 131be18..eb01a15 100644
--- a/visionstate/CHANGELOG.md
+++ b/visionstate/CHANGELOG.md
@@ -1,5 +1,13 @@
 # Changelog
 
+## 0.9.0b1
+
+### Fixed
+
+- **A quiet log when faces are recognised.** Each time the SFace face model was loaded, the app's
+  log filled with 174 harmless warnings from ONNX Runtime about how the model file is stored. The
+  model works exactly as before; only errors from ONNX Runtime are logged now.
+
 ## 0.8.1b5
 
 ### Fixed
diff --git a/visionstate/config.yaml b/visionstate/config.yaml
index 691cef1..3e4deae 100644
--- a/visionstate/config.yaml
+++ b/visionstate/config.yaml
@@ -1,5 +1,5 @@
 name: VisionState (beta)
-version: "0.8.1b5"
+version: "0.9.0b1"
 slug: visionstate
 description: Teach a local AI to recognise states (open, closed, …), find objects (people, cars, pets) or read numbers in your camera images
 url: https://github.com/oleost/VisionState

From f0130a816761f13ea574863b698b0fe796f90184 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 14:18:19 +0200
Subject: [PATCH 19/28] test_docs: a module name counts if any module of that
 name defines it

faces.py, engine/faces.py and api/faces.py share a name; only the last one was looked at.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 visionstate/backend/tests/test_docs.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/visionstate/backend/tests/test_docs.py b/visionstate/backend/tests/test_docs.py
index 6af736f..3570d0c 100644
--- a/visionstate/backend/tests/test_docs.py
+++ b/visionstate/backend/tests/test_docs.py
@@ -52,8 +52,12 @@ def _files() -> list[Path]:
 
 FILES = _files()
 NAMES = {p.name for p in FILES}
-MODULES = {p.stem: p for p in BACKEND.rglob("*.py") if "__pycache__" not in p.parts and p.stem != "__init__"}
-BACKEND_TEXT = "\n".join(p.read_text(encoding="utf-8") for p in MODULES.values())
+# Modules by name; several share one (faces.py, engine/faces.py, api/faces.py): any of them counts.
+MODULES: dict[str, list[Path]] = {}
+for _path in BACKEND.rglob("*.py"):
+    if "__pycache__" not in _path.parts and _path.stem != "__init__":
+        MODULES.setdefault(_path.stem, []).append(_path)
+BACKEND_TEXT = "\n".join(p.read_text(encoding="utf-8") for paths in MODULES.values() for p in paths)
 # Tables whose columns a doc may name (`prediction.read_ok`); "sensor" is also a Home Assistant domain.
 TABLES = set(re.findall(r'__tablename__ = "(\w+)"', (BACKEND / "db.py").read_text(encoding="utf-8"))) - {"sensor"}
 # A plan names code that is still to be written; only its links and paths are checked.
@@ -92,7 +96,7 @@ def _problem(token: str, doc: Path) -> str | None:
             return None
         first, name = m[1], m[2]
         if first in MODULES:
-            text = MODULES[first].read_text(encoding="utf-8")
+            text = "\n".join(p.read_text(encoding="utf-8") for p in MODULES[first])
             if _defines(text, name) or name.endswith("_"):
                 return None
             return f"{first}.py does not define {name}"

From c63613dd1c9bf53a796409d784f851fb2e61dc1c Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 14:18:19 +0200
Subject: [PATCH 20/28] Faces: even out a backlit face's light, ask sooner and
 in a burst, say after import

Tried on a real doorbell looking out at the sky: the face was nearly black, so it was named late
and hardly ever asked about.
- faces.even_light before every embedding (cached embeddings carry EMBEDDING_VERSION and are
  made again): named right 39 % -> 64 % in that test, other people less alike.
- SFace's ask_similarity 0.38 -> 0.32 (registry limits may change; CLAUDE.md says so now).
- Up to FACES["ask_burst"] questions about one person per review cooldown, FACES["ask_gap_s"]
  apart; an answer opens the next one at once. Own labels still ask one at a time.
- An import of a sensor that recognised faces says face recognition is off and opens its
  Settings tab; the text of "Ask me about faces I do not know" says what it does.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 CLAUDE.md                                     |  2 +-
 docs/FACE_RECOGNITION_PLAN.md                 | 14 +++++
 docs/SCOPE.md                                 |  8 ++-
 visionstate/DOCS.md                           | 12 +++--
 visionstate/backend/tests/test_faces.py       | 52 +++++++++++++++++++
 .../backend/visionstate/api/imports.py        | 14 +++--
 visionstate/backend/visionstate/api/review.py |  1 +
 .../backend/visionstate/engine/faces.py       |  8 +--
 .../backend/visionstate/engine/objects.py     | 27 ++++++----
 .../backend/visionstate/engine/state.py       |  4 +-
 visionstate/backend/visionstate/faces.json    |  2 +-
 visionstate/backend/visionstate/faces.py      | 21 ++++++--
 visionstate/backend/visionstate/settings.py   |  5 ++
 visionstate/frontend/src/lib/api.ts           |  3 +-
 visionstate/frontend/src/lib/app.svelte.ts    | 16 +++++-
 .../src/lib/components/FacesCard.svelte       |  5 +-
 visionstate/frontend/src/lib/types.ts         |  7 +++
 .../frontend/src/pages/Dashboard.svelte       |  8 +--
 .../frontend/src/pages/Settings.svelte        |  8 +--
 19 files changed, 175 insertions(+), 42 deletions(-)

diff --git a/CLAUDE.md b/CLAUDE.md
index 09135e4..2b599ce 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -61,7 +61,7 @@ Every fact has **one** home; other files link to it instead of repeating it.
 - New engine code goes into the part (mixin) it belongs to. A long-running loop must survive any
   exception (log it once, try again) — only `CancelledError` may end it.
 - Model registries (`backbones.json`, `detectors.json`, `readers.json`, `faces.json`): entries are never changed
-  or removed once released (their texts and measured footprint may be); a new model gets a new id, pinned by Hugging Face revision and SHA-256,
+  or removed once released (their texts, measured footprint and similarity limits may be); a new model gets a new id, pinned by Hugging Face revision and SHA-256,
   and is checked against real inputs in the tests (one public ONNX conversion turned out to be
   broken and only a test with real photos showed it).
 - Frontend: colours/type/spacing only in `tokens.css`, UI constants in `ui.ts`, API URLs only in
diff --git a/docs/FACE_RECOGNITION_PLAN.md b/docs/FACE_RECOGNITION_PLAN.md
index 5560455..64805f7 100644
--- a/docs/FACE_RECOGNITION_PLAN.md
+++ b/docs/FACE_RECOGNITION_PLAN.md
@@ -499,3 +499,17 @@ Where the build differs from §3–§9:
   (renamed and forgotten there, and on each sensor's Quality tab), and has their own entity saying
   which sensor sees them now. Deleting a sensor keeps the faces taught on it. §9's per-sensor
   people and *Forget all faces* on the Faces card are replaced by this.
+- **A backlit doorbell** (the maintainer's, 2026-10-12: 2560 × 1920 fisheye, half of it sky). The
+  person was found in nearly every frame, and the face was usable (67–73 px) from about two metres,
+  but against the bright sky it was nearly black: its SFace embedding was 0.30–0.51 from the faces
+  taught earlier and only 0.15–0.53 from one taken seconds before, so it was named late and asked
+  about rarely. Two changes, measured with the faces of one walk taught and those of a second walk
+  as queries (CC0 NASA faces as other people):
+  - the face's brightness is evened out before it is embedded (`faces.even_light`, a histogram
+    equalisation of its luminance): named right from 39 % to 64 %, all of them at least in the ask
+    band (86 % before), and other people less alike (the highest from 0.30 to 0.26; Williams
+    2006–2018 0.64 → 0.63, Williams–Wilmore 0.09 → 0.01);
+  - SFace's `ask_similarity` from 0.38 to 0.32, so an uncertain face of a taught person becomes a
+    question (and an answer a taught face) instead of *not sure who*.
+  Faces taught on the camera itself, in its own light, still help most: answering the questions is
+  how a camera learns someone.
diff --git a/docs/SCOPE.md b/docs/SCOPE.md
index fb83334..bc091b5 100644
--- a/docs/SCOPE.md
+++ b/docs/SCOPE.md
@@ -154,7 +154,9 @@ knew). Instead a second step compares boxes with boxes the user taught:
 - Unsure (`ask_similarity` 0.75 up to `match_similarity`, to an own label of the box's class): the
   box carries `ask`, counts neither for nor against that label (it keeps its published state), and
   goes to the review queue ("Is this Our car?", a history row `published_key = "ask"`, reason
-  `ask`) — one waiting question per label, at most one per review cooldown, none with review off.
+  `ask`) — one waiting question per label, at most one per review cooldown, none with review off;
+  about a face up to `FACES["ask_burst"]` (waiting, and per cooldown), `FACES["ask_gap_s"]` apart,
+  and an answer resets that person's cooldown.
   Yes/no teach the box from that history frame (as the label / as its class); answering again
   replaces that taught box. Below 0.75 it is simply not that label.
 - Switching off needs `DETECTION["clear_misses"]` (2) checks in a row without the object as well
@@ -199,7 +201,9 @@ left are in [`FACE_RECOGNITION_PLAN.md`](FACE_RECOGNITION_PLAN.md). In short:
   cut by the frame's edge — the detector then guesses the eyes —, the face's width `min_face_px` —
   not the distance between the eyes, which shrinks as a head turns —, detector score, how far it
   is turned, sharpness) makes a bad face *no face*, never *unknown*. A usable face is aligned on
-  its five points (Umeyama + Pillow, no OpenCV) and embedded.
+  its five points (Umeyama + Pillow, no OpenCV), its brightness evened out (`faces.even_light`: a
+  face in front of a bright sky is nearly black) and embedded; cached embeddings carry
+  `faces.EMBEDDING_VERSION`, so a change to that step recomputes them.
 - A person's score is the mean similarity to their `top_k` closest taught faces. Thresholds belong
   to the recogniser's registry entry (cosine values of one model mean nothing for another): a
   match needs `match_similarity` and `FACES["margin"]` over the next person, and is published at
diff --git a/visionstate/DOCS.md b/visionstate/DOCS.md
index e8bb72e..a7b736c 100644
--- a/visionstate/DOCS.md
+++ b/visionstate/DOCS.md
@@ -137,7 +137,9 @@ clearly (say a car in light you never taught it in), goes to the **review queue*
 car?* Meanwhile the label stays as it was — it is not switched off, nor on. **Yes** teaches the box
 as your label, **No** as an ordinary car, both in that light, so the next time it knows. A box that
 looks little like it is simply not yours, and it does not ask. At most one question per label is
-waiting, and no more often than the review cooldown (**Settings → Review queue**).
+waiting, and no more often than the review cooldown (**Settings → Review queue**). About a person's
+face it may ask a few times in a row (someone walking up shows several faces worth teaching), a
+few seconds apart, and answering one lets the next come right away.
 
 - Every check decides anew, box by box, by what a box looks like — not where it is, so an object
   that moves a little or is lit differently is still recognised. Boxes filtered away in a check
@@ -235,8 +237,9 @@ everyone.
   camera) that matched nobody in two checks in a row turns **Unknown person** on. A face further
   away or more turned can still be named, but if it looks like nobody it is only *not sure who*:
   a face seen from the side often does not look like the faces you taught. **Ask me about faces I
-  do not know** (off by default) also asks *Who is this?* about strangers; that keeps frames of
-  strangers in the history longer.
+  do not know** (off by default) also asks *Who is this?* about faces nobody is named for. Without
+  it such a face is still kept in the history (*Face seen, not named*) to name later; a question
+  waiting for an answer keeps its frame twice as long as the history.
 - It never learns by itself: only faces you name are taught.
 - **People** (in the menu, once there are people or a sensor recognises faces) shows every person
   with their faces and the camera each was taught on (× forgets one), where they were last seen,
@@ -479,7 +482,8 @@ How long history is kept is set under **Settings → Storage** (below).
   sensor, the boxes it was taught and its own labels. Camera passwords are removed from the file.
   For a sensor where faces were taught it asks first whether to **include the faces** taught on it
   (unticked by default), with their people's names. An import adds them to the people of the same
-  name (or makes them), but face recognition stays off until you turn it on there.
+  name (or makes them), but face recognition stays off until you turn it on there: the import
+  says so and opens the sensor's Settings tab.
 - **Import** (dashboard or Settings) adds it as a new sensor (named "… (2)" when the name is
   taken) — also on another installation — and
   trains it automatically. If the camera URL needed a password, enter it again on the sensor's
diff --git a/visionstate/backend/tests/test_faces.py b/visionstate/backend/tests/test_faces.py
index 19d3cc7..aaffabd 100644
--- a/visionstate/backend/tests/test_faces.py
+++ b/visionstate/backend/tests/test_faces.py
@@ -12,8 +12,10 @@
 import pytest
 from fastapi.testclient import TestClient
 from PIL import Image, ImageFilter
+from sqlalchemy import select
 
 from visionstate import detectors, faces
+from visionstate.db import FaceEmbedding
 from visionstate.main import create_app
 from visionstate.settings import FACE_UNKNOWN_KEY, FACES, merge_objects
 
@@ -49,6 +51,18 @@ def test_align_puts_the_five_points_on_the_template():
         assert aligned[round(y), round(x)] > 128
 
 
+def test_even_light_spreads_a_dark_face_over_the_full_range():
+    # A face in front of a bright sky: dark and flat. Evened out it uses the full range, its
+    # colours kept (a red face stays red), at the same size.
+    dark = Image.effect_noise((112, 112), 20).point(lambda v: 20 + v // 8).convert("RGB")
+    dark = Image.merge("RGB", (dark.getchannel(0).point(lambda v: v + 10), dark.getchannel(1), dark.getchannel(2)))
+    even = faces.even_light(dark)
+    low, high = even.convert("L").getextrema()
+    assert even.size == dark.size and low < 40 and high > 215
+    r, g, b = (np.asarray(even.getchannel(c), dtype=np.float32).mean() for c in range(3))
+    assert r > g and r > b
+
+
 def test_turn_eye_distance_and_quality_gate():
     straight = face_at([(40, 50), (80, 50), (60, 70), (45, 90), (75, 90)])  # 60 px wide
     half = face_at([(40, 50), (80, 50), (75, 70), (45, 90), (75, 90)])  # turned, but can be named
@@ -311,6 +325,10 @@ def teach(name):
         face_id = people["people"][0]["faces"][0]["id"]
         first = people["people"][0]["faces"][0]
         assert first["suspect"] is None and first["sensor_id"] == sid and people["look_alikes"] == []
+        # Embeddings are cached per model and version (an older version is recomputed).
+        with rt.db.session() as s:
+            keys = set(s.scalars(select(FaceEmbedding.model)))
+        assert keys == {f"{faces.DEFAULT_RECOGNISER}/{faces.EMBEDDING_VERSION}"}
         # Each person's own entity says where they are seen: on this sensor now.
         assert people["people"][0]["seen"]["now"] == "beach" and people["people"][0]["seen"]["sensor_id"] == sid
         image = client.get(f"/api/v1/faces/{face_id}/image")
@@ -342,6 +360,7 @@ def teach(name):
         assert imported.status_code == 201, imported.text
         copy = imported.json()["id"]
         assert view(client, copy)["objects"]["faces"]["enabled"] is False  # turned on there, by the user
+        assert imported.json()["faces_off"] is True  # so the UI says to turn it on
         # The same people (no copies of them), each with the face again, from the new sensor.
         people = client.get("/api/v1/people").json()["people"]
         assert [(p["key"], len(p["faces"])) for p in people] == [("suni", 2), ("butch", 2)]
@@ -530,3 +549,36 @@ def test_people_are_taught_from_photos(settings):  # noqa: F811
         nobody = client.post("/api/v1/people/photos", data={"new_person": "Nobody"}, files=empty).json()
         assert nobody["added"] == 0 and len(client.get("/api/v1/people").json()["people"]) == 1
         assert client.post("/api/v1/people/photos", files=empty).status_code == 400  # who is it?
+
+
+def test_questions_about_a_face_come_in_a_burst_and_an_answer_opens_the_next(settings):  # noqa: F811
+    """Someone walking up to a doorbell shows several faces worth teaching: up to
+    FACES["ask_burst"] questions about one person per review cooldown, FACES["ask_gap_s"] apart,
+    and an answer lets the next one come right away. An own label is asked about one at a time."""
+    with TestClient(create_app(settings)) as client:
+        rt = client.app.state.runtime
+        sid = create(client, objects={"classes": ["person", "car"]}, enabled=False).json()["id"]
+        cfg = rt.load_sensor(sid)
+        image = Image.new("RGB", (64, 48))
+        kari = {"key": "person", "score": 0.9, "box": [0.1, 0.1, 0.5, 0.9], "face": {"box": [0.2, 0.1, 0.3, 0.2]}}
+        kari["ask"] = {"id": None, "label": "kari", "similarity": 0.4, "face": True}
+        car = {"key": "car", "score": 0.9, "box": [0.5, 0.5, 0.9, 0.9], "ask": {"label": "our_car", "similarity": 0.8}}
+
+        def waiting(label):
+            items = client.get("/api/v1/review").json()["items"]
+            return [i for i in items if i["probs"]["ask"]["label"] == label]
+
+        start = 1_000_000.0
+        for n in range(8):  # a check every 2 s: a question at most every FACES["ask_gap_s"]
+            client.portal.call(rt._ask_unsure, cfg, image, [kari, car], start + 2 * n)
+        assert len(waiting("kari")) == 4  # 16 s of checks every 2 s: at 0, 4, 8 and 12 s
+        assert len(waiting("our_car")) == 1
+        for n in range(8, 20):
+            client.portal.call(rt._ask_unsure, cfg, image, [kari, car], start + 2 * n)
+        assert len(waiting("kari")) == FACES["ask_burst"]  # then it waits for the cooldown
+        # An answer opens the next question at once (no 5 minutes to wait in front of the camera).
+        first = waiting("kari")[0]["id"]
+        assert client.post(f"/api/v1/review/{first}", json={"action": "skip"}).status_code == 200
+        client.portal.call(rt._ask_unsure, cfg, image, [kari, car], start + 41)
+        assert len(waiting("kari")) == FACES["ask_burst"] and waiting("kari")[0]["id"] != first
+        assert len(waiting("our_car")) == 1
diff --git a/visionstate/backend/visionstate/api/imports.py b/visionstate/backend/visionstate/api/imports.py
index 95ba043..b81e20c 100644
--- a/visionstate/backend/visionstate/api/imports.py
+++ b/visionstate/backend/visionstate/api/imports.py
@@ -46,7 +46,7 @@ async def import_bundle(request: Request, file: UploadFile = File(...)) -> dict:
         path.unlink(missing_ok=True)
         raise HTTPException(413, f"Bundle is larger than {UPLOAD_LIMITS['max_file_mb']} MB")
     try:
-        sensor_id, skipped = await asyncio.to_thread(_import_sync, rt, path)
+        sensor_id, skipped, faces_off = await asyncio.to_thread(_import_sync, rt, path)
     except (ValueError, KeyError, TypeError, OSError, zipfile.BadZipFile) as err:
         raise HTTPException(400, f"Import failed: {redact(str(err))}") from err
     finally:
@@ -56,11 +56,13 @@ async def import_bundle(request: Request, file: UploadFile = File(...)) -> dict:
     cfg = await asyncio.to_thread(rt.load_sensor, sensor_id)
     if cfg and cfg.kind == KIND_STATES:
         rt.schedule_retrain(sensor_id, delay=0)
-    return {"id": sensor_id, "skipped": skipped}
+    # faces_off: it recognised faces where it was exported; the UI says to turn that on again here.
+    return {"id": sensor_id, "skipped": skipped, "faces_off": faces_off}
 
 
-def _import_sync(rt: Runtime, path: Path) -> tuple[int, int]:
-    """Creates the sensor of a bundle; returns its ID and how many of its images were unreadable."""
+def _import_sync(rt: Runtime, path: Path) -> tuple[int, int, bool]:
+    """Creates the sensor of a bundle; returns its ID, how many of its images were unreadable and
+    whether face recognition, on where it was exported, was turned off."""
     from pydantic import ValidationError
 
     from .sensors import SensorIn
@@ -97,9 +99,11 @@ def _import_sync(rt: Runtime, path: Path) -> tuple[int, int]:
         raise ValueError(f"invalid sensor in bundle: {err.errors()[0].get('msg')}") from err
     except HTTPException as err:
         raise ValueError(f"invalid sensor in bundle: {err.detail}") from err
+    faces_off = False
     if spec.objects is not None and spec.objects.faces is not None:
         # Taught faces come along, face recognition does not: it is turned on there, with its
         # own confirmation (and download).
+        faces_off = bool(spec.objects.faces.enabled)
         spec.objects.faces.enabled = False
     samples = manifest.get("samples", []) if spec.kind in (KIND_STATES, KIND_OBJECTS) else []
     if not isinstance(samples, list) or len(samples) > UPLOAD_LIMITS["max_zip_members"]:
@@ -136,7 +140,7 @@ def _import_sync(rt: Runtime, path: Path) -> tuple[int, int]:
         except (ValueError, KeyError, TypeError, AttributeError, OSError, HTTPException) as err:
             skipped += 1
             log.warning("Import: skipping a face: %s", getattr(err, "detail", err))
-    return sensor_id, skipped
+    return sensor_id, skipped, faces_off
 
 
 def _import_face(rt: Runtime, path: Path, sensor_id: int, item: dict) -> None:
diff --git a/visionstate/backend/visionstate/api/review.py b/visionstate/backend/visionstate/api/review.py
index e775e55..a21feb5 100644
--- a/visionstate/backend/visionstate/api/review.py
+++ b/visionstate/backend/visionstate/api/review.py
@@ -208,6 +208,7 @@ async def _answer_object(rt: Runtime, request: Request, prediction_id: int, body
             rt.taught_changed(sensor_id)
     if is_face:
         await _answer_face(rt, request, prediction_id, sensor_id, question, score, body)
+        rt.live_state(sensor_id).asked.pop(question["label"], None)  # answered: the next may come now
     elif action != "skip":
         label = question["label"] if action == "yes" else question["detected"]
         taught_box = TeachIn(
diff --git a/visionstate/backend/visionstate/engine/faces.py b/visionstate/backend/visionstate/engine/faces.py
index 58e545c..e798a0d 100644
--- a/visionstate/backend/visionstate/engine/faces.py
+++ b/visionstate/backend/visionstate/engine/faces.py
@@ -13,7 +13,7 @@
 
 import numpy as np
 from PIL import Image
-from sqlalchemy import select
+from sqlalchemy import delete, select
 
 from .. import backbones, faces, imaging
 from ..db import Face, FaceEmbedding, Person, Sensor
@@ -176,7 +176,9 @@ async def face_index_for(self, model: faces.FaceModel) -> faces.FaceIndex:
         return index
 
     def _build_face_index(self, model: faces.FaceModel, generation: int) -> faces.FaceIndex:
+        cache_key = f"{model.id}/{faces.EMBEDDING_VERSION}"  # cached embeddings of another version are not used
         with self.db.session() as s:
+            s.execute(delete(FaceEmbedding).where(FaceEmbedding.model == model.id))  # before versions
             examples = [
                 (face_id, key, filename, dict(face))
                 for face_id, key, filename, face in s.execute(
@@ -189,7 +191,7 @@ def _build_face_index(self, model: faces.FaceModel, generation: int) -> faces.Fa
                 r.face_id: np.frombuffer(r.vector, dtype=np.float32)
                 for r in s.scalars(
                     select(FaceEmbedding).where(
-                        FaceEmbedding.face_id.in_([e[0] for e in examples]), FaceEmbedding.model == model.id
+                        FaceEmbedding.face_id.in_([e[0] for e in examples]), FaceEmbedding.model == cache_key
                     )
                 )
             }
@@ -204,7 +206,7 @@ def _build_face_index(self, model: faces.FaceModel, generation: int) -> faces.Fa
                 aligned = faces.align(crop, faces.face_from_dict(face), model.recogniser.spec.input_size)
                 vector = model.recogniser.embed([aligned])[0]
                 with self.db.session() as s:
-                    s.merge(FaceEmbedding(face_id=face_id, model=model.id, vector=vector.tobytes()))
+                    s.merge(FaceEmbedding(face_id=face_id, model=cache_key, vector=vector.tobytes()))
             ids.append(face_id)
             labels.append(key)
             vectors.append(vector)
diff --git a/visionstate/backend/visionstate/engine/objects.py b/visionstate/backend/visionstate/engine/objects.py
index be5fb38..e6a5c93 100644
--- a/visionstate/backend/visionstate/engine/objects.py
+++ b/visionstate/backend/visionstate/engine/objects.py
@@ -8,7 +8,7 @@
 from datetime import UTC, datetime
 
 from PIL import Image
-from sqlalchemy import select
+from sqlalchemy import func, select
 
 from .. import detectors, imaging, teach
 from ..db import Prediction
@@ -124,8 +124,8 @@ async def _record_face(self, cfg: SensorConfig, image: Image.Image, found: list[
 
     async def _ask_unsure(self, cfg: SensorConfig, image: Image.Image, found: list[dict], now: float) -> None:
         """Put a box that may be an own label, but not clearly, in the review queue ("Is this Our
-        car?"); the answer is taught. One question per label at a time, at most one per review
-        cooldown, and none while the review rules are off."""
+        car?"); the answer is taught. One question per label per review cooldown (about a face: up
+        to FACES["ask_burst"], FACES["ask_gap_s"] apart), and none while the review rules are off."""
         live = self.live_state(cfg.id)
         rules = self.review_rules(cfg)
         unsure = [d for d in found if d.get("ask") and not d.get("filtered")]
@@ -133,27 +133,32 @@ async def _ask_unsure(self, cfg: SensorConfig, image: Image.Image, found: list[d
             return
         asked = False
         for label in sorted({d["ask"]["label"] for d in unsure}):
-            if now - live.asked.get(label, 0.0) < rules["cooldown_s"]:
-                continue
             box = max((d for d in unsure if d["ask"]["label"] == label), key=lambda d: d["ask"]["similarity"])
-            live.asked[label] = now
-            asked = await asyncio.to_thread(self._record_question, cfg.id, image, box, found) or asked
+            burst = FACES["ask_burst"] if box["ask"].get("face") else 1
+            times = [t for t in live.asked.get(label, []) if now - t < rules["cooldown_s"]]
+            if len(times) >= burst or (times and now - times[-1] < FACES["ask_gap_s"]):
+                live.asked[label] = times
+                continue
+            recorded = await asyncio.to_thread(self._record_question, cfg.id, image, box, found, burst)
+            live.asked[label] = [*times, now] if recorded else times
+            asked = asked or recorded
         if asked:
             await self.publish_review_count()
 
-    def _record_question(self, sensor_id: int, image: Image.Image, box: dict, found: list[dict]) -> bool:
-        """A history row waiting for review: may ``box`` be its own label? False: one is waiting already."""
+    def _record_question(self, sensor_id: int, image: Image.Image, box: dict, found: list[dict], most: int = 1) -> bool:
+        """A history row waiting for review: may ``box`` be its own label? False: ``most`` about
+        that label are waiting already."""
         label = box["ask"]["label"]
         with self.db.session() as s:
             waiting = s.scalar(
-                select(Prediction.id).where(
+                select(func.count(Prediction.id)).where(
                     Prediction.sensor_id == sensor_id,
                     Prediction.published_key == "ask",
                     Prediction.state_key == label,
                     Prediction.reviewed.is_(False),
                 )
             )
-        if waiting is not None:
+        if (waiting or 0) >= most:
             return False
         frame = self.storage.save_history(sensor_id, image)
         question = {"label": label, "similarity": box["ask"]["similarity"], "box": box["box"], "detected": box["key"]}
diff --git a/visionstate/backend/visionstate/engine/state.py b/visionstate/backend/visionstate/engine/state.py
index 8b0e256..6438e7d 100644
--- a/visionstate/backend/visionstate/engine/state.py
+++ b/visionstate/backend/visionstate/engine/state.py
@@ -202,7 +202,9 @@ class LiveState:
     filtered_keys: set[str] = field(default_factory=set)
     filtered_logged: dict[str, float] = field(default_factory=dict)
     recheck_at: float = 0.0  # an object that was there went missing: check again then (0: no)
-    asked: dict[str, float] = field(default_factory=dict)  # own label -> when the review last asked
+    asked: dict[str, list[float]] = field(
+        default_factory=dict
+    )  # own label/person -> when the review asked (this cooldown)
     # Faces: what the face search gave for the last person boxes it looked at ("ok" or the
     # problem: "none", "cut", "small", "turned", "blurry", "unsure"), for "a usable face in 9 of 40".
     face_stats: deque = field(default_factory=lambda: deque(maxlen=FACES["stats_window"]))
diff --git a/visionstate/backend/visionstate/faces.json b/visionstate/backend/visionstate/faces.json
index a398793..638ded2 100644
--- a/visionstate/backend/visionstate/faces.json
+++ b/visionstate/backend/visionstate/faces.json
@@ -27,7 +27,7 @@
       "std": 1.0,
       "match_similarity": 0.5,
       "sure_similarity": 0.6,
-      "ask_similarity": 0.38,
+      "ask_similarity": 0.32,
       "keep_similarity": 0.4,
       "license": "Apache-2.0",
       "data": "The model's paper trained it on CASIA-WebFace, VGGFace2 and MS-Celeb-1M, research data sets (non-commercial terms).",
diff --git a/visionstate/backend/visionstate/faces.py b/visionstate/backend/visionstate/faces.py
index d3d5991..8976d26 100644
--- a/visionstate/backend/visionstate/faces.py
+++ b/visionstate/backend/visionstate/faces.py
@@ -5,7 +5,7 @@
 points — the eyes, the nose tip and the mouth corners) and the recognisers the user can choose
 from (a face embedding from a face aligned on those five points). None of them is bundled: they
 are downloaded when a sensor first turns face recognition on. Entries are never changed or
-removed once released (their texts and footprint may be).
+removed once released (their texts, footprint and similarity limits may be).
 
 A face is only looked for in the upper part of a person box the object detector found, cut from
 the full frame. A face that fails the quality gate (too small, blurry, turned away, unsure) is
@@ -23,13 +23,17 @@
 from pathlib import Path
 
 import numpy as np
-from PIL import Image
+from PIL import Image, ImageOps
 
 from .backbones import Footprint, cpu_session
 from .settings import FACE_DEFAULTS, FACE_TUNABLE, FACES
 
 REGISTRY_FILE = Path(__file__).with_name("faces.json")
 
+# How a face becomes an embedding besides the model (see ``Recogniser.embed``); part of the key of
+# cached embeddings, so a change here recomputes them. 2: the brightness of the face is evened out.
+EMBEDDING_VERSION = 2
+
 # Where the five points of a face go in a 112 × 112 aligned face (the ArcFace template, which
 # SFace uses too): right eye, left eye (as seen in the picture: left, right), nose tip, mouth corners.
 TEMPLATE_112 = np.array(
@@ -405,6 +409,17 @@ def clearly_seen(face: Face, min_face_px: float) -> bool:
 # --- Telling who it is ------------------------------------------------------------------------
 
 
+def even_light(face: Image.Image) -> Image.Image:
+    """The face with its brightness spread over the full range (its colours kept).
+
+    A doorbell looks out at a bright sky, so a face in front of it is often nearly black: its
+    embedding then hardly looked like the same person a few seconds later. Evening out the light
+    raised the faces named right in a backlit test from 39 % to 64 % and made other people look
+    less alike, not more (docs/FACE_RECOGNITION_PLAN.md §12)."""
+    y, cb, cr = face.convert("YCbCr").split()
+    return Image.merge("YCbCr", (ImageOps.equalize(y), cb, cr)).convert("RGB")
+
+
 class Recogniser:
     """Turns aligned faces into L2-normalised embeddings with one ONNX recogniser."""
 
@@ -418,7 +433,7 @@ def embed(self, faces: list[Image.Image]) -> np.ndarray:
         """One embedding per aligned face; one at a time (some models take a batch of one only)."""
         vectors = []
         for face in faces:
-            arr = np.asarray(face.convert("RGB"), dtype=np.float32)
+            arr = np.asarray(even_light(face), dtype=np.float32)
             arr = ((arr - self.spec.mean) / self.spec.std).transpose(2, 0, 1)[None]
             with self._lock:
                 out = np.asarray(self.session.run(None, {self.input_name: arr})[0])[0]
diff --git a/visionstate/backend/visionstate/settings.py b/visionstate/backend/visionstate/settings.py
index f05d760..a243db0 100644
--- a/visionstate/backend/visionstate/settings.py
+++ b/visionstate/backend/visionstate/settings.py
@@ -287,6 +287,11 @@
     # seconds per sensor, unless the face came this many times closer (wider) since the last one.
     "record_cooldown_s": 60,
     "record_closer": 1.5,
+    # "Is this Kari?": a person moves, so each question shows another face to teach. Up to this many
+    # questions about one person per review cooldown (and waiting at once), at least this many
+    # seconds apart; an answer opens the next one right away. Own labels ask one at a time.
+    "ask_burst": 5,
+    "ask_gap_s": 3.0,
     # Photos and short videos from a phone (People page; the same upload as a state sensor's, so
     # images, ZIP archives and videos): at most this many faces are added from one upload, and a
     # video gives a frame every this many seconds (turning the head gives several angles).
diff --git a/visionstate/frontend/src/lib/api.ts b/visionstate/frontend/src/lib/api.ts
index 31482a5..0199f82 100644
--- a/visionstate/frontend/src/lib/api.ts
+++ b/visionstate/frontend/src/lib/api.ts
@@ -6,6 +6,7 @@ import type {
   FaceOverrides,
   FaceProblem,
   FaceStatus,
+  ImportResult,
   ModelKind,
   People,
   PhotoResult,
@@ -297,6 +298,6 @@ export const api = {
   importBundle(file: File) {
     const form = new FormData();
     form.append('file', file, file.name);
-    return request<{ id: number; skipped: number }>('import', { method: 'POST', body: form });
+    return request<ImportResult>('import', { method: 'POST', body: form });
   },
 };
diff --git a/visionstate/frontend/src/lib/app.svelte.ts b/visionstate/frontend/src/lib/app.svelte.ts
index be0437d..96e93a0 100644
--- a/visionstate/frontend/src/lib/app.svelte.ts
+++ b/visionstate/frontend/src/lib/app.svelte.ts
@@ -1,6 +1,7 @@
 // Global app state: server config, status and toasts.
 import { api } from './api';
-import type { AppConfig, Sensor, StateDef, Status } from './types';
+import { plural } from './format';
+import type { AppConfig, ImportResult, Sensor, StateDef, Status } from './types';
 import { TOAST_MAX, TOAST_MS } from './ui';
 
 export const app = $state<{ config: AppConfig | null; status: Status | null; error: string }>({
@@ -44,6 +45,19 @@ export function dismiss(id: number): void {
   if (index >= 0) toasts.splice(index, 1);
 }
 
+/** What an import brought: unreadable images it skipped, and face recognition to turn on again (it
+ * is off after an import, so it gets its own confirmation here). */
+export function toastImported(result: ImportResult): void {
+  const skipped = result.skipped ? `; ${plural(result.skipped, 'unreadable image')} skipped` : '';
+  if (result.faces_off) {
+    toast(`Sensor imported${skipped}. It recognised faces where it came from: turn Recognise faces on below.`, {
+      tone: 'warn',
+    });
+  } else {
+    toast(`Sensor imported${skipped}`);
+  }
+}
+
 export function toastError(err: unknown): void {
   toast((err as Error).message || 'Something went wrong', { tone: 'danger' });
 }
diff --git a/visionstate/frontend/src/lib/components/FacesCard.svelte b/visionstate/frontend/src/lib/components/FacesCard.svelte
index 5ca895e..4837af9 100644
--- a/visionstate/frontend/src/lib/components/FacesCard.svelte
+++ b/visionstate/frontend/src/lib/components/FacesCard.svelte
@@ -92,7 +92,10 @@
       />
       <span class="col" style="gap:2px">
         <strong class="small">Ask me about faces I do not know</strong>
-        <span class="xsmall faint">“Who is this?” in the review queue. Keeps frames of strangers longer.</span>
+        <span class="xsmall faint">
+          Also asks “Who is this?” in the review queue about faces nobody is named for. A question waiting for an
+          answer keeps its frame twice as long as the history.
+        </span>
       </span>
     </label>
     {#if globalFaces}
diff --git a/visionstate/frontend/src/lib/types.ts b/visionstate/frontend/src/lib/types.ts
index 58419d8..8483fed 100644
--- a/visionstate/frontend/src/lib/types.ts
+++ b/visionstate/frontend/src/lib/types.ts
@@ -261,6 +261,13 @@ export interface PhotoResult {
   faces: number | null;
 }
 
+/** POST /import: the new sensor; `faces_off`: it recognised faces where it was exported (now off). */
+export interface ImportResult {
+  id: number;
+  skipped: number;
+  faces_off: boolean;
+}
+
 /** The face model: chosen, loaded, downloading (bytes) or why it failed. */
 export interface FaceStatus {
   model: string;
diff --git a/visionstate/frontend/src/pages/Dashboard.svelte b/visionstate/frontend/src/pages/Dashboard.svelte
index 37ceafe..e29c2b6 100644
--- a/visionstate/frontend/src/pages/Dashboard.svelte
+++ b/visionstate/frontend/src/pages/Dashboard.svelte
@@ -1,7 +1,7 @@
 <script lang="ts">
   import { onDestroy } from 'svelte';
   import { api } from '../lib/api';
-  import { app, shownConfidence, stateInfo, toast, toastError } from '../lib/app.svelte';
+  import { app, shownConfidence, stateInfo, toastError, toastImported } from '../lib/app.svelte';
   import AnalysedFrame from '../lib/components/AnalysedFrame.svelte';
   import Icon from '../lib/components/Icon.svelte';
   import LiveFrame from '../lib/components/LiveFrame.svelte';
@@ -36,9 +36,9 @@
     const file = (e.currentTarget as HTMLInputElement).files?.[0];
     if (!file) return;
     try {
-      const { id, skipped } = await api.importBundle(file);
-      toast(skipped ? `Sensor imported; ${skipped} unreadable image${skipped === 1 ? '' : 's'} skipped` : 'Sensor imported');
-      go(paths.sensor(id));
+      const result = await api.importBundle(file);
+      toastImported(result);
+      go(paths.sensor(result.id, result.faces_off ? 'settings' : ''));
     } catch (err) {
       toastError(err);
     } finally {
diff --git a/visionstate/frontend/src/pages/Settings.svelte b/visionstate/frontend/src/pages/Settings.svelte
index 165305d..e6580ef 100644
--- a/visionstate/frontend/src/pages/Settings.svelte
+++ b/visionstate/frontend/src/pages/Settings.svelte
@@ -1,6 +1,6 @@
 <script lang="ts">
   import { api } from '../lib/api';
-  import { app, refreshStatus, toast, toastError } from '../lib/app.svelte';
+  import { app, refreshStatus, toast, toastError, toastImported } from '../lib/app.svelte';
   import FaceLimitsEditor from '../lib/components/FaceLimitsEditor.svelte';
   import Icon from '../lib/components/Icon.svelte';
   import ModelCompare from '../lib/components/ModelCompare.svelte';
@@ -146,9 +146,9 @@
     const file = (e.currentTarget as HTMLInputElement).files?.[0];
     if (!file) return;
     try {
-      const { id, skipped } = await api.importBundle(file);
-      toast(skipped ? `Sensor imported; ${skipped} unreadable image${skipped === 1 ? '' : 's'} skipped` : 'Sensor imported');
-      go(paths.sensor(id));
+      const result = await api.importBundle(file);
+      toastImported(result);
+      go(paths.sensor(result.id, result.faces_off ? 'settings' : ''));
     } catch (err) {
       toastError(err);
     } finally {

From 5c9063771ae51358a41849ca6b9780e656b1b077 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 14:18:30 +0200
Subject: [PATCH 21/28] 0.9.0b2

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 visionstate/CHANGELOG.md | 14 ++++++++++++++
 visionstate/config.yaml  |  2 +-
 2 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/visionstate/CHANGELOG.md b/visionstate/CHANGELOG.md
index eb01a15..1023397 100644
--- a/visionstate/CHANGELOG.md
+++ b/visionstate/CHANGELOG.md
@@ -1,5 +1,19 @@
 # Changelog
 
+## 0.9.0b2
+
+### Better
+
+- **Faces in front of a bright sky are recognised better.** A doorbell looking out at the sky
+  often sees a face that is nearly black; its light is now evened out before it is compared, so
+  you are named sooner and other people look less alike.
+- **It asks more when it is not sure who someone is**, so answering teaches it how each camera
+  sees you: from a lower similarity (with SFace), several times in a row while someone walks up
+  (a few seconds apart, up to five), and right away again after you answer.
+- **After importing a sensor that recognised faces**, the app says that face recognition is off
+  on the new sensor and opens its Settings tab to turn it on.
+- The text of **Ask me about faces I do not know** says what it does.
+
 ## 0.9.0b1
 
 ### Fixed
diff --git a/visionstate/config.yaml b/visionstate/config.yaml
index 3e4deae..b6830c4 100644
--- a/visionstate/config.yaml
+++ b/visionstate/config.yaml
@@ -1,5 +1,5 @@
 name: VisionState (beta)
-version: "0.9.0b1"
+version: "0.9.0b2"
 slug: visionstate
 description: Teach a local AI to recognise states (open, closed, …), find objects (people, cars, pets) or read numbers in your camera images
 url: https://github.com/oleost/VisionState

From f6393a7cd20d89892be3a094d85bf8e4f3caabed Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 16:46:26 +0200
Subject: [PATCH 22/28] Faces: look for a face while a person is there; History
 shows what was taught; last seen survives a restart

- A check that finds a person nobody is named for checks every FACES["burst_interval_s"] while
  they come closer or show a face, until they are named or gone, without progress or after
  FACES["burst_max_s"]; not again until they have gone (engine.logic.steer_face_burst). Tried on
  a doorbell: the usable face of someone walking up lasts about three seconds, between two
  regular checks; the burst started 8 s before the door and asked about it.
- A box or face taught from a history frame or a review answer is written into that row (its
  label, taught): the History says "Taught: Ole", and the list shows it right away.
- At start, when each object, own label and person was last seen is read back from the history,
  so a sensor no longer says "Not seen yet" about Our car after an update.
- e2e: teaching from a history frame.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 docs/SCOPE.md                                 | 11 +++-
 visionstate/DOCS.md                           | 12 +++-
 visionstate/backend/tests/test_faces.py       |  9 +++
 visionstate/backend/tests/test_triggers.py    | 51 +++++++++++++++-
 visionstate/backend/visionstate/api/common.py | 10 +++-
 visionstate/backend/visionstate/api/faces.py  |  4 +-
 visionstate/backend/visionstate/api/teach.py  | 42 ++++++++++++-
 .../backend/visionstate/engine/checks.py      |  2 +-
 .../backend/visionstate/engine/logic.py       | 55 +++++++++++++++--
 .../backend/visionstate/engine/objects.py     | 59 +++++++++++++++++--
 .../backend/visionstate/engine/runtime.py     |  1 +
 .../backend/visionstate/engine/state.py       | 13 ++++
 visionstate/backend/visionstate/settings.py   | 12 ++++
 visionstate/backend/visionstate/teach.py      | 10 ++--
 visionstate/frontend/e2e/ui.spec.ts           | 15 +++++
 .../lib/components/history/HistoryView.svelte | 18 ++++--
 .../lib/components/history/ObjectRow.svelte   | 18 +++++-
 visionstate/frontend/src/lib/types.ts         |  6 +-
 visionstate/frontend/src/lib/ui.ts            |  1 +
 .../frontend/src/pages/sensor/LabelTab.svelte |  2 +-
 .../frontend/src/pages/sensor/LiveTab.svelte  |  2 +-
 .../src/pages/sensor/ReadingLiveTab.svelte    |  2 +-
 22 files changed, 323 insertions(+), 32 deletions(-)

diff --git a/docs/SCOPE.md b/docs/SCOPE.md
index bc091b5..31d0c23 100644
--- a/docs/SCOPE.md
+++ b/docs/SCOPE.md
@@ -229,7 +229,11 @@ left are in [`FACE_RECOGNITION_PLAN.md`](FACE_RECOGNITION_PLAN.md). In short:
 - Each person also has their own entity on the app's device, `sensor.visionstate_<key>`
   (`mqtt.person_discovery_messages`): the slug of the sensor that sees them now — the one that saw
   them last when several do — or `settings.PERSON_NOT_SEEN`; when and where they were last seen
-  are attributes. Kept in memory: after a restart everyone is `not_seen` until seen again.
+  are attributes. Kept in memory, and at start read back from the history
+  (`engine.objects.restore_last_seen`: the last "on", an "off" less `clear_after_s`, a box taught
+  from a frame), so "last seen" survives a restart while the history keeps it. A box taught from
+  a history frame or a review answer is written into that row (`api.teach.remember_taught`: its
+  label, `taught`), so the History shows what was taught from it.
 - `GET /people` and `/faces/{id}` (`api/faces.py`) list, rename, move and forget for all sensors;
   teaching stays on a sensor (`/sensors/{id}/faces`), or comes from photos (`POST /people/photos`:
   read like a state sensor's upload, `uploads.frames_from_file`; the best face of each picture
@@ -353,6 +357,11 @@ the pixels are decoded.
   - Optional region change detection: a small greyscale copy of the ROI is compared every
     *N* seconds; the AI only runs when the difference exceeds a threshold.
   - Every trigger starts a *burst* (default every 2 s for 30 s) to catch the final state.
+  - A sensor that recognises faces starts one by itself when a check finds a person nobody is
+    named for (`engine.logic.steer_face_burst`, tunables `FACES["burst_*"]`): someone walking up
+    shows a usable face for a few seconds only. It runs while they come closer or show a face, ends
+    once everyone is named, when they are gone, without progress or after `burst_max_s`, and comes
+    again only after they have gone. The light is held through it like any burst.
   - The MQTT `classify` button checks once.
 - **Light** (`triggers.light_entity`, `light_delay_s`; picked with the camera in the UI): a light,
   switch or input_boolean, switched over the HA REST API (`homeassistant.turn_on/off`). `lights.py`
diff --git a/visionstate/DOCS.md b/visionstate/DOCS.md
index a7b736c..2111b2d 100644
--- a/visionstate/DOCS.md
+++ b/visionstate/DOCS.md
@@ -114,7 +114,9 @@ Where it gets your camera wrong, you can correct it later (see *Teaching an obje
 ### Teaching an object sensor
 
 The detector works without training, but it can be corrected where it gets your camera wrong.
-Tap a box on the **Live** tab or on a history frame (or its entry under the frame):
+Tap a box on the **Live** tab or on a history frame (or its entry under the frame). A history
+entry you taught from says so (*Taught: Our car*), and what you taught counts as last seen at
+that frame's time:
 
 | Choice | What happens from then on |
 |---|---|
@@ -174,7 +176,8 @@ someone it has not been taught. It is **off** until you turn it on. The people a
 every camera that recognises faces knows everyone you named on any of them, and each person also
 gets their own sensor (for Kari: `sensor.visionstate_kari`) whose value is the sensor that sees
 them now — the one that saw them last, when several do — or `not_seen`, with when and where they
-were last seen as attributes (after a restart of the app: `not_seen` until they are seen again).
+were last seen as attributes. After a restart or an update of the app, when everyone and
+everything was last seen comes back from the history (as far back as it keeps frames).
 
 **Before you turn it on.** Faces are biometric data. VisionState keeps them on your Home
 Assistant only — no face ever leaves it, as nothing else does — but the people you teach should
@@ -229,6 +232,11 @@ everyone.
   goes through the same check; the message says how many were added and why a photo was skipped.
   A quick start: faces taught on each camera still recognise people best, as a camera sees them
   from another angle and in other light. Photos can be added once a sensor recognises faces.
+- **When someone comes**: a person walking up to the camera shows a usable face for only a few
+  seconds. When a check finds a person nobody is named for, the sensor checks about every second
+  for a while (*Person seen* on the **Live** tab): while they come closer or show their face,
+  until they are named or gone, and for half a minute at most. Someone who stays (on the steps,
+  in the garden) gets one such burst, not one every few minutes.
 - **How it decides**: each face is compared with the faces you taught for each person. A clear
   match names the person; a less certain one needs a second check that agrees. A person who stays
   where they were keeps the name while their face is turned away. Close to someone, but not
diff --git a/visionstate/backend/tests/test_faces.py b/visionstate/backend/tests/test_faces.py
index aaffabd..cba3fa5 100644
--- a/visionstate/backend/tests/test_faces.py
+++ b/visionstate/backend/tests/test_faces.py
@@ -415,6 +415,15 @@ def question():
         assert {p["name"]: len(p["faces"]) for p in people} == {"Suni": 1, "Butch": 1}
         assert wait_for(lambda: live(client, sid).get("butch", {}).get("on"), timeout=30)
         assert merge_objects(None)["faces"]["ask_unknown"] is False  # off by default
+        # The question's frame in the history now shows who it was: taught from it.
+        rows = client.get("/api/v1/history", params={"sensor": sid, "limit": 100}).json()["items"]
+        row = next(r for r in rows if r["id"] == item["id"])
+        assert [d["label"] for d in row["detections"] if d.get("taught")] == ["butch"]
+    # After an update or restart, when each person was last seen comes back from the history.
+    with TestClient(create_app(settings)) as client:
+        assert live(client, sid)["butch"]["last_seen"] and live(client, sid)["suni"]["last_seen"]
+        seen = {p["key"]: p["seen"]["at"] for p in client.get("/api/v1/people").json()["people"]}
+        assert seen["butch"] and seen["suni"]
 
 
 def test_a_face_model_that_cannot_be_downloaded_leaves_the_sensor_working(settings, monkeypatch, tmp_path):  # noqa: F811
diff --git a/visionstate/backend/tests/test_triggers.py b/visionstate/backend/tests/test_triggers.py
index 1ed4d90..312c7f2 100644
--- a/visionstate/backend/tests/test_triggers.py
+++ b/visionstate/backend/tests/test_triggers.py
@@ -16,9 +16,10 @@
 from visionstate.api.common import Triggers
 from visionstate.db import SCHEMA_VERSION, Database
 from visionstate.engine import LiveState, SensorConfig, entity_triggers, next_check_at
+from visionstate.engine.logic import steer_face_burst
 from visionstate.ha_events import HaEventListener, parse_trigger_event, trigger_subscription
 from visionstate.main import create_app
-from visionstate.settings import TRIGGER_DEFAULTS, Settings, merge_triggers
+from visionstate.settings import FACES, TRIGGER_DEFAULTS, Settings, merge_triggers
 
 from .conftest import requires_model, requires_reader
 from .displays import render
@@ -545,3 +546,51 @@ def view():
             assert wait_for(lambda before=before: view()["reading"]["last"]["at"] != before, timeout=30)
             assert view()["reading"]["last"]["reason"] is None, view()["reading"]["last"]
         assert client.get(f"/api/v1/sensors/{sid}/reading-quality").json()["periods"][0]["rejected"] == 0
+
+
+def person(height: float, face: dict | None = None) -> dict:
+    """A person box of that height (share of the frame) and what the face search found in it."""
+    return {"key": "person", "score": 0.9, "box": [0.4, 0.9 - height, 0.5, 0.9], **({"face": face} if face else {})}
+
+
+def test_a_face_burst_follows_a_person_walking_up_to_the_door():
+    """Someone walking up shows a usable face for a few seconds only: a person nobody is named for
+    starts checks every FACES["burst_interval_s"], kept up while they come closer or show a face,
+    over once they are named; not again until they have gone."""
+    live, t = LiveState(last_run=100), 100.0
+    fb = live.face_burst
+    assert steer_face_burst(fb, [], t) is None  # nobody there
+    assert steer_face_burst(fb, [person(0.1, {"problem": "none"})], t) == "start"  # far away, no face yet
+    assert next_check_at(config(), live, t + 0.1) == (100 + FACES["burst_interval_s"], "full")
+    for n in range(1, 10):  # coming closer: no face for longer than burst_idle_checks, but growing
+        assert steer_face_burst(fb, [person(0.1 * 1.15**n, {"problem": "small"})], t + n) is None
+    usable = {"problem": None, "box": [0.45, 0.2, 0.5, 0.25], "face_px": 60, "state": "ask"}
+    for n in range(10, 20):  # standing at the door with a face it is not sure of
+        assert steer_face_burst(fb, [person(0.5, usable)], t + n) is None
+    named = {**usable, "state": "match", "label": "kari"}
+    assert steer_face_burst(fb, [person(0.5, named)], t + 20) == "stop"  # named: done
+    assert next_check_at(config(), live, t + 20.5) == (160, "full")  # the regular interval again
+    # Still there: no second burst. Gone (burst_absent_checks), then back: a burst of its own.
+    assert steer_face_burst(fb, [person(0.5, {"problem": "turned"})], t + 30) is None
+    for n in range(FACES["burst_absent_checks"]):
+        assert steer_face_burst(fb, [], t + 40 + n) is None
+    assert steer_face_burst(fb, [person(0.2)], t + 50) == "start"
+
+
+def test_a_face_burst_ends_without_progress_and_after_its_time():
+    # Standing still with their back to the camera: over after burst_idle_checks checks.
+    fb = LiveState().face_burst
+    assert steer_face_burst(fb, [person(0.4, {"problem": "none"})], 0) == "start"
+    stops = [
+        steer_face_burst(fb, [person(0.4, {"problem": "none"})], n) for n in range(1, FACES["burst_idle_checks"] + 1)
+    ]
+    assert stops[-1] == "stop" and stops[:-1] == [None] * (FACES["burst_idle_checks"] - 1)
+    # A face it is never sure of keeps it going, but not beyond burst_max_s.
+    fb = LiveState().face_burst
+    unsure = {"problem": None, "box": [0.45, 0.2, 0.5, 0.25], "face_px": 60, "state": "unknown"}
+    assert steer_face_burst(fb, [person(0.4, unsure)], 0) == "start"
+    assert steer_face_burst(fb, [person(0.4, unsure)], FACES["burst_max_s"] - 1) is None
+    assert steer_face_burst(fb, [person(0.4, unsure)], FACES["burst_max_s"]) == "stop"
+    # A person filtered away (taught "not a person": a statue) never starts one.
+    statue = {**person(0.4), "filtered": True}
+    assert steer_face_burst(LiveState().face_burst, [statue], 0) is None
diff --git a/visionstate/backend/visionstate/api/common.py b/visionstate/backend/visionstate/api/common.py
index ec44e7b..98efc59 100644
--- a/visionstate/backend/visionstate/api/common.py
+++ b/visionstate/backend/visionstate/api/common.py
@@ -20,6 +20,7 @@
     FACE_DEFAULTS,
     FACE_LIMITS,
     FACE_UNKNOWN_KEY,
+    FACES,
     KIND_OBJECTS,
     KIND_READING,
     LIGHT_DOMAINS,
@@ -494,6 +495,11 @@ def sensor_view(rt: Runtime, session: Session, sensor: Sensor) -> dict:
     else:
         status = "ok"
     ids = entity_ids(rt, sensor)
+    now = time.time()
+    paces = [merge_triggers(sensor.triggers)["burst_interval_s"]] if live and live.burst_until > now else []
+    if live and live.face_burst.until > now:
+        paces.append(FACES["burst_interval_s"])
+    pace = min(paces) if paces else None
     return {
         "id": sensor.id,
         "slug": sensor.slug,
@@ -526,7 +532,9 @@ def sensor_view(rt: Runtime, session: Session, sensor: Sensor) -> dict:
             "confidence": live.confidence if live else 0.0,
             "probs": live.probs if live else {},
             "last_run": live.last_run if live else None,
-            "in_burst": bool(live and live.burst_until > time.time()),
+            # A burst of checks (a trigger's, or looking for a face while a person is there) and its pace.
+            "in_burst": pace is not None,
+            "burst_interval_s": pace,
             "change_score": live.change_score if live else None,
             "last_trigger": live.last_trigger if live else None,
             "light_error": live.light_error if live else "",
diff --git a/visionstate/backend/visionstate/api/faces.py b/visionstate/backend/visionstate/api/faces.py
index 8752fd8..a008ec0 100644
--- a/visionstate/backend/visionstate/api/faces.py
+++ b/visionstate/backend/visionstate/api/faces.py
@@ -26,7 +26,7 @@
 from ..settings import FACES, KIND_OBJECTS, TEACH, UPLOAD_LIMITS, merge_objects
 from .common import API_PREFIX, iso, runtime, slugify
 from .samples import copy_limited
-from .teach import RESERVED_KEYS, frame_image
+from .teach import RESERVED_KEYS, frame_image, remember_taught
 
 router = APIRouter(prefix=f"{API_PREFIX}/sensors", tags=["faces"])
 people_router = APIRouter(prefix=API_PREFIX, tags=["faces"])
@@ -217,6 +217,8 @@ async def teach_face(sensor_id: int, body: FaceTeachIn, request: Request) -> dic
         await rt.people_changed()  # the person's entities on every sensor that recognises faces
     else:
         rt.faces_changed()
+    if body.history_id is not None:
+        await asyncio.to_thread(remember_taught, rt, sensor_id, body.history_id, body.box, key, "person", True)
     return {"id": face_id, "label": key, "name": name, "faces": n}
 
 
diff --git a/visionstate/backend/visionstate/api/teach.py b/visionstate/backend/visionstate/api/teach.py
index eed12f2..e0e890e 100644
--- a/visionstate/backend/visionstate/api/teach.py
+++ b/visionstate/backend/visionstate/api/teach.py
@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import asyncio
+from datetime import UTC
 
 from fastapi import APIRouter, HTTPException, Request, Response
 from PIL import Image
@@ -10,7 +11,7 @@
 from sqlalchemy import ColumnElement, func, select
 from sqlalchemy.orm import Session
 
-from .. import detectors, imaging
+from .. import detectors, imaging, teach
 from ..db import Person, Prediction, Sample
 from ..engine import Runtime
 from ..settings import FACE_UNKNOWN_KEY, KIND_OBJECTS, NONE_LABEL, TEACH, active_custom, merge_objects
@@ -175,9 +176,48 @@ async def teach_box(sensor_id: int, body: TeachIn, request: Request) -> dict:
     if new is not None:
         await rt.sensor_updated(sensor_id, retrain=False)  # the new label's entities
     rt.taught_changed(sensor_id)
+    if body.history_id is not None:
+        await asyncio.to_thread(remember_taught, rt, sensor_id, body.history_id, body.box, label, body.detected)
     return {"id": example_id, "label": label, "seen": seen}
 
 
+def remember_taught(
+    rt: Runtime,
+    sensor_id: int,
+    history_id: int,
+    box: list[float],
+    label: str,
+    detected: str | None,
+    person: bool = False,
+) -> None:
+    """A box taught from a history frame (or a review answer): the row shows it with its new label
+    from then on, and an own label or person counts as seen at that frame's time (so the sensor no
+    longer says "not seen yet" about what was just taught from it)."""
+    with rt.db.session() as s:
+        row = s.get(Prediction, history_id)
+        if row is None or row.sensor_id != sensor_id:
+            return
+        found = [dict(d) for d in row.detections or []]
+        best = max(found, key=lambda d: teach.box_iou(d["box"], box), default=None)
+        if best is None or teach.box_iou(best["box"], box) < TEACH["keep_iou"]:
+            best = {"key": detected or label, "score": 0.0, "box": list(box)}
+            found.append(best)
+        best.pop("ask", None)
+        best.pop("filtered", None)
+        if label in (NONE_LABEL, best["key"]):
+            best.pop("label", None)  # not it, or just the class it was found as
+            if label == NONE_LABEL:
+                best["filtered"] = True
+        else:
+            best["label"] = label
+        best["taught"] = True
+        row.detections = found
+        created = row.created_at
+        at = (created if created.tzinfo else created.replace(tzinfo=UTC)).timestamp()  # stored naive, in UTC
+    if label not in (NONE_LABEL, best["key"]):
+        rt.seen_earlier(sensor_id, label, at, person)
+
+
 def delete_examples(rt: Runtime, s: Session, sensor_id: int, where: list[ColumnElement[bool]]) -> int:
     """Delete the taught boxes of a sensor that match ``where`` (all with an empty list); returns how many."""
     rows = s.scalars(
diff --git a/visionstate/backend/visionstate/engine/checks.py b/visionstate/backend/visionstate/engine/checks.py
index 54d7337..b904c85 100644
--- a/visionstate/backend/visionstate/engine/checks.py
+++ b/visionstate/backend/visionstate/engine/checks.py
@@ -134,7 +134,7 @@ def _light_after(self, cfg: SensorConfig) -> None:
         live = self.live_state(cfg.id)
         if not entity or live.light_off_task or not self.lights.held(entity, f"check:{cfg.id}"):
             return
-        wait = max(0.0, live.burst_until - time.time())
+        wait = max(0.0, max(live.burst_until, live.face_burst.until) - time.time())
 
         async def off() -> None:
             await asyncio.sleep(wait)
diff --git a/visionstate/backend/visionstate/engine/logic.py b/visionstate/backend/visionstate/engine/logic.py
index 504593f..943de7b 100644
--- a/visionstate/backend/visionstate/engine/logic.py
+++ b/visionstate/backend/visionstate/engine/logic.py
@@ -6,8 +6,8 @@
 import math
 
 from .. import detectors, imaging, readers, teach
-from ..settings import DETECTION
-from .state import LiveState, ObjectTrack, SensorConfig
+from ..settings import DETECTION, FACES
+from .state import FaceBurst, LiveState, ObjectTrack, SensorConfig
 
 
 def review_reason(
@@ -152,9 +152,9 @@ def next_check_at(cfg: SensorConfig, live: LiveState, now: float) -> tuple[float
     """When the sensor loop should wake up next, and whether that is a full check or a cheap probe."""
     if live.last_run is None:
         return now, "full"
-    in_burst = now < live.burst_until
-    if in_burst:
-        full_at = live.last_run + cfg.triggers["burst_interval_s"]
+    pace = burst_pace(cfg, live, now)
+    if pace is not None:
+        full_at = live.last_run + pace
     elif cfg.triggers["regular"]:
         # Counted from the last check, whatever caused it: frequent triggers postpone it.
         full_at = live.last_run + cfg.interval_s
@@ -169,6 +169,51 @@ def next_check_at(cfg: SensorConfig, live: LiveState, now: float) -> tuple[float
     return full_at, "full"
 
 
+def burst_pace(cfg: SensorConfig, live: LiveState, now: float) -> float | None:
+    """Seconds between checks while a burst runs (a trigger's, or looking for a face), or None."""
+    paces = [cfg.triggers["burst_interval_s"]] if now < live.burst_until else []
+    if now < live.face_burst.until:
+        paces.append(FACES["burst_interval_s"])
+    return min(paces) if paces else None
+
+
+def steer_face_burst(burst: FaceBurst, found: list[dict], now: float) -> str | None:
+    """After a check of a sensor that recognises faces: start or end looking for a face more
+    often while a person is there (FACES["burst_*"]). Returns "start" or "stop" when that changes."""
+    people = [d for d in found if d["key"] == "person" and not d.get("filtered")]
+    if not people:
+        burst.absent += 1
+        if burst.absent >= FACES["burst_absent_checks"]:
+            burst.spent = False  # gone: the next visit gets a burst of its own
+            return _end_face_burst(burst, spent=False)
+        return None
+    burst.absent = 0
+    unnamed = [d for d in people if (d.get("face") or {}).get("state") != "match"]
+    height = max(d["box"][3] - d["box"][1] for d in people)
+    width = max(float((d.get("face") or {}).get("face_px") or 0) for d in people)
+    if burst.until == 0.0:
+        if burst.spent or not unnamed:
+            return None
+        burst.until, burst.idle, burst.best_height, burst.best_face = now + FACES["burst_max_s"], 0, height, width
+        return "start"
+    if not unnamed or now >= burst.until:
+        return _end_face_burst(burst, spent=True)  # everyone named, or the time is up
+    usable = any((d.get("face") or {}).get("box") and not (d.get("face") or {}).get("problem") for d in unnamed)
+    closer = height > burst.best_height * FACES["burst_progress"] or width > burst.best_face * FACES["burst_progress"]
+    burst.idle = 0 if usable or closer else burst.idle + 1
+    burst.best_height, burst.best_face = max(burst.best_height, height), max(burst.best_face, width)
+    if burst.idle >= FACES["burst_idle_checks"]:
+        return _end_face_burst(burst, spent=True)  # standing turned away, or no face to be had
+    return None
+
+
+def _end_face_burst(burst: FaceBurst, spent: bool) -> str | None:
+    running = burst.until > 0.0
+    burst.until, burst.idle = 0.0, 0
+    burst.spent = burst.spent or spent
+    return "stop" if running else None
+
+
 def reading_verdict(
     text: readers.Text, value: float | None, last: float | None, threshold: float, settings: dict
 ) -> tuple[str | None, bool]:
diff --git a/visionstate/backend/visionstate/engine/objects.py b/visionstate/backend/visionstate/engine/objects.py
index e6a5c93..1c343ac 100644
--- a/visionstate/backend/visionstate/engine/objects.py
+++ b/visionstate/backend/visionstate/engine/objects.py
@@ -11,15 +11,15 @@
 from sqlalchemy import func, select
 
 from .. import detectors, imaging, teach
-from ..db import Prediction
+from ..db import Person, Prediction, Sensor
 from ..mqtt import object_topics, topics
 from ..redact import redact
-from ..settings import DETECTION, FACE_UNKNOWN_KEY, FACES, TEACH
+from ..settings import DETECTION, FACE_UNKNOWN_KEY, FACES, KIND_OBJECTS, TEACH
 from .base import RuntimeBase
 from .faces import FacesMixin
-from .logic import update_tracks
+from .logic import steer_face_burst, update_tracks
 from .publishing import PublishingMixin
-from .state import SensorConfig
+from .state import ObjectTrack, SensorConfig
 from .teaching import TeachingMixin
 
 log = logging.getLogger(__name__)
@@ -63,6 +63,8 @@ async def _run_objects(self, cfg: SensorConfig, image: Image.Image, frame_id: st
         live.recheck_at = now + DETECTION["recheck_s"] if missing else 0.0
         if cfg.faces_on:
             await self.update_people(cfg)  # each person's own entity: where they are seen
+            if steer_face_burst(live.face_burst, found, now) == "start":
+                live.last_trigger = {"source": "person", "detail": "looking for a face", "at": now}
 
         for key in keys:
             track = live.tracks[key]
@@ -122,6 +124,50 @@ async def _record_face(self, cfg: SensorConfig, image: Image.Image, found: list[
         key = best.get("label") or best["key"]
         await asyncio.to_thread(self._record_detection, cfg.id, image, key, "face", best["score"], found)
 
+    def restore_last_seen(self) -> None:
+        """At start: when each object, own label and person was last seen, from the history (the
+        live state starts empty, so every sensor said "not seen yet" after an update or restart).
+        An "off" row is written clear_after_s after the object was last there; a box taught from a
+        history frame counts at that frame's time. Only what the history still keeps is known."""
+        with self.db.session() as s:
+            objects = select(Sensor.id).where(Sensor.kind == KIND_OBJECTS)
+            events = s.execute(
+                select(
+                    Prediction.sensor_id,
+                    Prediction.state_key,
+                    Prediction.published_key,
+                    func.max(Prediction.created_at),
+                )
+                .where(Prediction.sensor_id.in_(objects), Prediction.published_key.in_(("on", "off")))
+                .group_by(Prediction.sensor_id, Prediction.state_key, Prediction.published_key)
+            ).all()
+            taught_rows = s.execute(
+                select(Prediction.sensor_id, Prediction.created_at, Prediction.detections).where(
+                    Prediction.sensor_id.in_(objects), Prediction.published_key.in_(("face", "ask"))
+                )
+            ).all()
+            people = set(s.scalars(select(Person.key)))
+        clear_after: dict[int, float] = {}
+        for sensor_id, key, event, created in events:
+            if sensor_id not in clear_after:
+                cfg = self.load_sensor(sensor_id)
+                clear_after[sensor_id] = float(cfg.objects["clear_after_s"]) if cfg and cfg.objects else 0.0
+            at = _timestamp(created) - (clear_after[sensor_id] if event == "off" else 0.0)
+            self.seen_earlier(sensor_id, key, at, key in people)
+        for sensor_id, created, detections in taught_rows:
+            for d in detections or []:
+                if d.get("taught") and d.get("label"):
+                    self.seen_earlier(sensor_id, d["label"], _timestamp(created), d["label"] in people)
+
+    def seen_earlier(self, sensor_id: int, key: str, at: float, person: bool = False) -> None:
+        """``key`` (an own label, or a ``person``) was taught from a frame taken at ``at``: it counts
+        as last seen then, unless a check saw it later. Nothing is switched on: that frame is the past."""
+        track = self.live_state(sensor_id).tracks.setdefault(key, ObjectTrack())
+        track.last_seen = max(track.last_seen, at)
+        last = self._person_last.get(key)
+        if person and (last is None or at > last[0]):
+            self._person_last[key] = (at, sensor_id)  # the People page's "last seen"
+
     async def _ask_unsure(self, cfg: SensorConfig, image: Image.Image, found: list[dict], now: float) -> None:
         """Put a box that may be an own label, but not clearly, in the review queue ("Is this Our
         car?"); the answer is taught. One question per label per review cooldown (about a face: up
@@ -214,3 +260,8 @@ def _record_detection(
                     detections=found,
                 )
             )
+
+
+def _timestamp(value: datetime) -> float:
+    """A stored time as a Unix timestamp (SQLite keeps it without its zone: UTC)."""
+    return (value if value.tzinfo else value.replace(tzinfo=UTC)).timestamp()
diff --git a/visionstate/backend/visionstate/engine/runtime.py b/visionstate/backend/visionstate/engine/runtime.py
index 6c2ad40..f82091a 100644
--- a/visionstate/backend/visionstate/engine/runtime.py
+++ b/visionstate/backend/visionstate/engine/runtime.py
@@ -61,6 +61,7 @@ async def start(self) -> None:
             if self.heads_load(sensor_id):
                 log.info("Sensor %s: model from another version, retraining from stored images", sensor_id)
                 self.schedule_retrain(sensor_id, delay=0)
+        await asyncio.to_thread(self.restore_last_seen)  # "last seen" survives an update or restart
         for sensor_id in await asyncio.to_thread(self._sensor_ids):
             self._start_loop(sensor_id)
         await self.faces_settled()  # sensors that recognise faces: load the face model in the background
diff --git a/visionstate/backend/visionstate/engine/state.py b/visionstate/backend/visionstate/engine/state.py
index 6438e7d..94dc0ed 100644
--- a/visionstate/backend/visionstate/engine/state.py
+++ b/visionstate/backend/visionstate/engine/state.py
@@ -170,6 +170,18 @@ class ObjectTrack:
     score: float = 0.0  # best confidence in the last check
 
 
+@dataclass
+class FaceBurst:
+    """Checks every FACES["burst_interval_s"] while a person nobody is named for is there (logic.steer_face_burst)."""
+
+    until: float = 0.0  # running until then (at most); 0: not running
+    idle: int = 0  # checks in a row without a usable face or the person coming closer
+    absent: int = 0  # checks in a row without a person
+    best_height: float = 0.0  # the tallest person box (share of the frame) of this burst
+    best_face: float = 0.0  # the widest face (pixels) of this burst
+    spent: bool = False  # this visit had its burst: none again until the person has gone
+
+
 @dataclass
 class LiveState:
     frames: deque = field(default_factory=lambda: deque(maxlen=RUNTIME["frame_cache_size"]))
@@ -186,6 +198,7 @@ class LiveState:
     force: bool = False
     force_paused: bool = False  # the forced check was asked for by the user: also when paused
     burst_until: float = 0.0
+    face_burst: FaceBurst = field(default_factory=FaceBurst)
     last_probe: float = 0.0
     signature: np.ndarray | None = None  # region thumbnail of the last classified frame
     change_score: float | None = None  # last measured difference, shown in the UI for tuning
diff --git a/visionstate/backend/visionstate/settings.py b/visionstate/backend/visionstate/settings.py
index a243db0..69a28cc 100644
--- a/visionstate/backend/visionstate/settings.py
+++ b/visionstate/backend/visionstate/settings.py
@@ -292,6 +292,18 @@
     # seconds apart; an answer opens the next one right away. Own labels ask one at a time.
     "ask_burst": 5,
     "ask_gap_s": 3.0,
+    # Looking for a face while a person is there: someone walking up to a doorbell shows a usable
+    # face for a few seconds only, often between two regular checks. A check that finds a person
+    # nobody is named for checks again every burst_interval_s (or the sensor's burst pace if
+    # faster), until everyone in view is named, nobody was seen in burst_absent_checks checks in a
+    # row, burst_idle_checks checks in a row brought neither a usable face nor a person coming
+    # closer (a box or face burst_progress times larger than before), or after burst_max_s. Then not
+    # again until the person has gone: someone sitting on the steps gets one burst, not one an hour.
+    "burst_interval_s": 1.0,
+    "burst_max_s": 30.0,
+    "burst_idle_checks": 6,
+    "burst_absent_checks": 2,
+    "burst_progress": 1.1,
     # Photos and short videos from a phone (People page; the same upload as a state sensor's, so
     # images, ZIP archives and videos): at most this many faces are added from one upload, and a
     # video gives a frame every this many seconds (turning the head gives several angles).
diff --git a/visionstate/backend/visionstate/teach.py b/visionstate/backend/visionstate/teach.py
index 9f9879f..386866b 100644
--- a/visionstate/backend/visionstate/teach.py
+++ b/visionstate/backend/visionstate/teach.py
@@ -143,7 +143,7 @@ def nearest(index: TaughtIndex, vector: np.ndarray, min_similarity: float) -> Ma
     return Match(label, similarity, example_id)
 
 
-def _iou(a: Sequence[float], b: Sequence[float]) -> float:
+def box_iou(a: Sequence[float], b: Sequence[float]) -> float:
     """Intersection over union of two boxes (x1, y1, x2, y2)."""
     x1, y1 = max(a[0], b[0]), max(a[1], b[1])
     x2, y2 = min(a[2], b[2]), min(a[3], b[3])
@@ -159,7 +159,7 @@ def plan(found: list[dict], candidates: list[dict], index: TaughtIndex) -> tuple
     rescue = []
     if index.rescue:
         for cand in sorted(candidates, key=lambda d: -d["score"]):
-            if any(_iou(cand["box"], d["box"]) >= TEACH["rescue_overlap_iou"] for d in found):
+            if any(box_iou(cand["box"], d["box"]) >= TEACH["rescue_overlap_iou"] for d in found):
                 continue  # the same object as a counted box (e.g. a car also seen as a truck)
             rescue.append(cand)
             if len(rescue) >= TEACH["max_rescue"]:
@@ -220,7 +220,7 @@ def apply(
         key, label = resolve(match.label, parents)
         if key is None or key not in classes:
             continue
-        if any(_iou(cand["box"], d["box"]) >= TEACH["rescue_overlap_iou"] for d in result if not d.get("filtered")):
+        if any(box_iou(cand["box"], d["box"]) >= TEACH["rescue_overlap_iou"] for d in result if not d.get("filtered")):
             continue  # already counted (another unsure box of the same object was rescued)
         det = {**cand, "key": key, "rescued": True}
         det["match"] = {"id": match.example_id, "label": match.label, "similarity": round(match.similarity, 3)}
@@ -240,7 +240,7 @@ def _kept(
     before = [
         d["match"]["label"]
         for d in previous
-        if d.get("match") and not d.get("rescued") and _iou(d["box"], box) >= TEACH["keep_iou"]
+        if d.get("match") and not d.get("rescued") and box_iou(d["box"], box) >= TEACH["keep_iou"]
     ]
     match = nearest(index, vector, keep_similarity) if before else None
     return match if match is not None and match.label in before else None
@@ -271,4 +271,4 @@ def counts_for(detection: dict, key: str) -> bool:
 
 def seen_faintly(candidates: list[dict], box: Sequence[float]) -> bool:
     """Whether any of the detector's boxes (however unsure) overlaps a drawn box."""
-    return any(_iou(c["box"], box) >= TEACH["seen_iou"] for c in candidates)
+    return any(box_iou(c["box"], box) >= TEACH["seen_iou"] for c in candidates)
diff --git a/visionstate/frontend/e2e/ui.spec.ts b/visionstate/frontend/e2e/ui.spec.ts
index 598f272..0a61357 100644
--- a/visionstate/frontend/e2e/ui.spec.ts
+++ b/visionstate/frontend/e2e/ui.spec.ts
@@ -689,6 +689,21 @@ test('teaching an object sensor: correct a box, draw a missed one, forget it all
     await expectNoClipping(page, '.btn, .chip, .card');
     await page.screenshot({ path: path.join('test-results', 'pages', info.project.name, 'objects-quality.png'), fullPage: true });
 
+    // Taught from a history frame: its entry says so from then on.
+    await page.goto(`#/sensors/${id}/history`);
+    const dogRow = page.getByRole('button', { name: /Dog detected/ }).first();
+    await expect(dogRow).toBeVisible({ timeout: 15_000 });
+    // The thumbnails load first, or the list moves under the tap on a phone; and the toast from
+    // teaching the missed box covers the bottom of a phone screen for a while.
+    await page.waitForFunction(() => [...document.images].every((img) => img.complete || img.loading === 'lazy'));
+    await expect(page.getByRole('button', { name: 'Dismiss' })).toHaveCount(0, { timeout: 20_000 });
+    await press(dogRow, info);
+    await expect(page.locator('.full .box').first()).toBeVisible();
+    await press(page.locator('.full button.found', { hasText: 'Dog' }).first(), info);
+    await press(sheet.getByRole('button', { name: 'Correct' }), info);
+    await expect(page.getByTestId('history-taught').first()).toContainText('Taught: Dog');
+    await expectNoHorizontalOverflow(page);
+
     // Settings: its own teaching limits. Until set, a field shows the global value in grey.
     await page.goto(`#/sensors/${id}/settings`);
     await expect(page.getByText('Use what you taught')).toBeVisible();
diff --git a/visionstate/frontend/src/lib/components/history/HistoryView.svelte b/visionstate/frontend/src/lib/components/history/HistoryView.svelte
index 7fa5922..358df44 100644
--- a/visionstate/frontend/src/lib/components/history/HistoryView.svelte
+++ b/visionstate/frontend/src/lib/components/history/HistoryView.svelte
@@ -41,12 +41,13 @@
   let active: HistoryFilter = {}; // the filter of the list shown ("the last hours" fixed when loaded)
   let generation = 0; // a newer load makes answers to older requests stale
 
-  async function load() {
+  /** The list again; `limit`: as many rows as are shown (after teaching from one, so it shows that). */
+  async function load(limit?: number) {
     const mine = ++generation;
     const filter = toFilter(shown);
     try {
       const [page, found] = await Promise.all([
-        api.history(filter, { order: shown.order, limit: settings?.page_size }),
+        api.history(filter, { order: shown.order, limit: limit ?? settings?.page_size }),
         api.historyFacets(filter),
       ]);
       if (mine !== generation) return;
@@ -66,9 +67,16 @@
   const key = $derived(JSON.stringify(shown));
   $effect(() => {
     void key;
-    untrack(load);
+    untrack(() => load());
   });
 
+  // Taught from a row: the row now shows what was taught (and the sensors their new labels).
+  function taughtFrom() {
+    onchange();
+    const shownRows = items?.length ?? 0;
+    void load(Math.min(Math.max(shownRows, settings?.page_size ?? 0), settings?.max_page_size ?? shownRows));
+  }
+
   async function more() {
     if (!next || loadingMore) return;
     const mine = generation;
@@ -199,7 +207,7 @@
   </span>
   <span class="spacer"></span>
   {#if fresh > 0}
-    <button type="button" class="btn sm accent-outline" onclick={load}>Show {fresh} new</button>
+    <button type="button" class="btn sm accent-outline" onclick={() => load()}>Show {fresh} new</button>
   {/if}
 </div>
 
@@ -220,7 +228,7 @@
       {@const sensor = byId.get(item.sensor_id)}
       {#if sensor}
         {@const Row = ROWS[sensor.kind]}
-        <Row {item} {sensor} showSensor={!locked} {onchange} />
+        <Row {item} {sensor} showSensor={!locked} onchange={taughtFrom} />
       {/if}
     {/each}
   </div>
diff --git a/visionstate/frontend/src/lib/components/history/ObjectRow.svelte b/visionstate/frontend/src/lib/components/history/ObjectRow.svelte
index b99c152..e931c41 100644
--- a/visionstate/frontend/src/lib/components/history/ObjectRow.svelte
+++ b/visionstate/frontend/src/lib/components/history/ObjectRow.svelte
@@ -29,6 +29,11 @@
     return [...counts].map(([key, n]) => objectCount(n, key, own)).join(', ');
   });
 
+  // What was taught from this frame since it was recorded (a person named, a box corrected).
+  const taught = $derived([
+    ...new Set((p.detections ?? []).filter((d) => d.taught).map((d) => objectName(d.label ?? d.key, own))),
+  ]);
+
   const WHAT: Record<string, string> = {
     on: 'detected',
     off: 'cleared',
@@ -46,12 +51,19 @@
       <span class="row wrap" style="gap:8px">
         <strong class="row" style="gap:8px" class:muted={filtered}>
           <span class="dot" class:ring={filtered} style:background={on ? objectColor(classes, p.state_key) : 'var(--c-unknown)'}></span>
-          {objectName(p.state_key, own)}{p.published_key === 'ask' ? '' : ' '}{WHAT[p.published_key ?? ''] ?? ''}
+          {objectName(p.state_key, own)}{p.published_key === 'ask' ? '' : ' '}{p.published_key === 'face' && taught.length
+            ? '· face seen'
+            : (WHAT[p.published_key ?? ''] ?? '')}
         </strong>
         {#if (on || filtered) && p.confidence}<span class="mono small muted">{pct(p.confidence)}</span>{/if}
       </span>
       <span class="xsmall faint">{dateTime(p.created_at)}</span>
       {#if inFrame}<span class="xsmall muted">In the frame: {inFrame}</span>{/if}
+      {#if taught.length}
+        <span class="xsmall taught row" style="gap:4px" data-testid="history-taught">
+          <Icon name="check" size={12} />Taught: {taught.join(', ')}
+        </span>
+      {/if}
     </span>
     <Icon name={open ? 'chevron-up' : 'chevron-down'} size={16} />
   </button>
@@ -80,6 +92,10 @@
   .sensor {
     overflow-wrap: anywhere;
   }
+  .taught {
+    color: var(--c-accent);
+    overflow-wrap: anywhere;
+  }
   img {
     width: 128px;
     aspect-ratio: 16 / 10;
diff --git a/visionstate/frontend/src/lib/types.ts b/visionstate/frontend/src/lib/types.ts
index 8483fed..b49a406 100644
--- a/visionstate/frontend/src/lib/types.ts
+++ b/visionstate/frontend/src/lib/types.ts
@@ -64,7 +64,7 @@ export interface ReminderInfo extends ReminderRules {
 export type ReviewOverrides = { [K in keyof ReviewRules]: ReviewRules[K] | null };
 
 export interface TriggerInfo {
-  source: 'entity' | 'change';
+  source: 'entity' | 'change' | 'person';
   detail: string;
   at: number;
 }
@@ -178,6 +178,8 @@ export interface Detection {
   ask?: { id: number | null; label: string; similarity: number; face?: boolean };
   /** Person boxes of a sensor that recognises faces: what the face search found (see FaceInfo). */
   face?: FaceInfo;
+  /** On a history frame: taught from it since (its label is the answer; filtered: "not it"). */
+  taught?: boolean;
 }
 
 /**
@@ -403,6 +405,8 @@ export interface Live {
   probs: Record<string, number>;
   last_run: number | null;
   in_burst: boolean;
+  /** Seconds between checks of that burst (a trigger's, or looking for a face while a person is there). */
+  burst_interval_s?: number | null;
   change_score: number | null;
   last_trigger: TriggerInfo | null;
   /** Why the sensor's light could not be switched ('' = fine). */
diff --git a/visionstate/frontend/src/lib/ui.ts b/visionstate/frontend/src/lib/ui.ts
index 92e7923..9c80b2e 100644
--- a/visionstate/frontend/src/lib/ui.ts
+++ b/visionstate/frontend/src/lib/ui.ts
@@ -260,6 +260,7 @@ export const ENTITY_ID_PATTERN = /^[a-z0-9_]+\.[a-z0-9_]+$/;
 export const TRIGGER_SOURCES: Record<string, string> = {
   entity: 'Entity changed',
   change: 'Image changed',
+  person: 'Person seen',
 };
 
 /** Placeholder the backend puts where credentials were removed from an exported URL (redact.MASK). */
diff --git a/visionstate/frontend/src/pages/sensor/LabelTab.svelte b/visionstate/frontend/src/pages/sensor/LabelTab.svelte
index 7870818..676cb46 100644
--- a/visionstate/frontend/src/pages/sensor/LabelTab.svelte
+++ b/visionstate/frontend/src/pages/sensor/LabelTab.svelte
@@ -133,7 +133,7 @@
         <p class="small muted">Label at least two different states to train the first model.</p>
       {/if}
       <div class="trigger">
-        {#if sensor.live.in_burst}<span class="chip info">Checking every {sensor.triggers.burst_interval_s} s</span>{/if}
+        {#if sensor.live.in_burst}<span class="chip info">Checking every {sensor.live.burst_interval_s ?? sensor.triggers.burst_interval_s} s</span>{/if}
         {#if sensor.live.last_trigger}
           <span class="xsmall muted">
             {TRIGGER_SOURCES[sensor.live.last_trigger.source] ?? 'Triggered'} {ago(sensor.live.last_trigger.at)}:
diff --git a/visionstate/frontend/src/pages/sensor/LiveTab.svelte b/visionstate/frontend/src/pages/sensor/LiveTab.svelte
index f2b05e6..983e9e0 100644
--- a/visionstate/frontend/src/pages/sensor/LiveTab.svelte
+++ b/visionstate/frontend/src/pages/sensor/LiveTab.svelte
@@ -107,7 +107,7 @@
         {/if}
       </p>
       <div class="trigger">
-        {#if sensor.live.in_burst}<span class="chip info">Checking every {sensor.triggers.burst_interval_s} s</span>{/if}
+        {#if sensor.live.in_burst}<span class="chip info">Checking every {sensor.live.burst_interval_s ?? sensor.triggers.burst_interval_s} s</span>{/if}
         {#if sensor.live.last_trigger}
           <span class="xsmall muted">
             {TRIGGER_SOURCES[sensor.live.last_trigger.source] ?? 'Triggered'} {ago(sensor.live.last_trigger.at)}:
diff --git a/visionstate/frontend/src/pages/sensor/ReadingLiveTab.svelte b/visionstate/frontend/src/pages/sensor/ReadingLiveTab.svelte
index 91fa0c5..03f1bf6 100644
--- a/visionstate/frontend/src/pages/sensor/ReadingLiveTab.svelte
+++ b/visionstate/frontend/src/pages/sensor/ReadingLiveTab.svelte
@@ -90,7 +90,7 @@
         <span class="chip ok">Last reading accepted</span>
       {/if}
       <div class="trigger">
-        {#if sensor.live.in_burst}<span class="chip info">Reading every {sensor.triggers.burst_interval_s} s</span>{/if}
+        {#if sensor.live.in_burst}<span class="chip info">Reading every {sensor.live.burst_interval_s ?? sensor.triggers.burst_interval_s} s</span>{/if}
         {#if sensor.live.last_trigger}
           <span class="xsmall muted">
             {TRIGGER_SOURCES[sensor.live.last_trigger.source] ?? 'Triggered'} {ago(sensor.live.last_trigger.at)}:

From 27dd4a313f299b6b96effd06ed0e9202ad5105a6 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 16:46:37 +0200
Subject: [PATCH 23/28] 0.9.0b3

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 visionstate/CHANGELOG.md | 13 +++++++++++++
 visionstate/config.yaml  |  2 +-
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/visionstate/CHANGELOG.md b/visionstate/CHANGELOG.md
index 1023397..9a05a13 100644
--- a/visionstate/CHANGELOG.md
+++ b/visionstate/CHANGELOG.md
@@ -1,5 +1,18 @@
 # Changelog
 
+## 0.9.0b3
+
+### Better
+
+- **Faces are looked for while someone is there.** Someone walking up to a doorbell shows a usable
+  face for only a few seconds, often between two checks. When a check finds a person nobody is
+  named for, a sensor that recognises faces now checks about every second while they come closer,
+  until they are named or gone (half a minute at most, and once per visit).
+- **The History shows what you taught from it.** After naming a person or correcting a box in a
+  history frame (or answering a question in Review), its entry says *Taught: Ole*.
+- **"Last seen" survives an update or restart.** A sensor no longer says *Not seen yet* about Our
+  car or a person it has seen before: when each was last seen comes back from the history.
+
 ## 0.9.0b2
 
 ### Better
diff --git a/visionstate/config.yaml b/visionstate/config.yaml
index b6830c4..5dd3f9a 100644
--- a/visionstate/config.yaml
+++ b/visionstate/config.yaml
@@ -1,5 +1,5 @@
 name: VisionState (beta)
-version: "0.9.0b2"
+version: "0.9.0b3"
 slug: visionstate
 description: Teach a local AI to recognise states (open, closed, …), find objects (people, cars, pets) or read numbers in your camera images
 url: https://github.com/oleost/VisionState

From 2bbf5f4f2b6730d931bd2808e8203062c7219e7d Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 20:24:34 +0200
Subject: [PATCH 24/28] Faces: "<name> seen by" and "<name> last seen"; a
 confirmed frame is last seen at once

- Each person's entity is named "<name> seen by" (sensor.visionstate_<key>_seen_by for new
  installs; unique ID and topics unchanged), and a timestamp entity "<name> last seen" joins it,
  so Home Assistant shows when someone was last seen also when it was confirmed afterwards.
- A person taught from a history frame or a review answer is published at once with that frame's
  time as last seen (it waited for the next change of where they are).
- What is taught from a history frame shows on the same moment's other entries too (the face
  seen and the question about it), within HISTORY["same_moment_s"].
- Docs: the code map and the face plan follow the changes of 0.9.0b2-b4.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 docs/ARCHITECTURE.md                          | 10 +--
 docs/FACE_RECOGNITION_PLAN.md                 |  9 ++
 docs/SCOPE.md                                 |  5 +-
 visionstate/DOCS.md                           | 11 ++-
 visionstate/backend/tests/test_faces.py       | 83 ++++++++++++++++++-
 visionstate/backend/visionstate/api/faces.py  |  1 +
 visionstate/backend/visionstate/api/teach.py  | 55 ++++++++----
 .../backend/visionstate/engine/faces.py       |  6 ++
 visionstate/backend/visionstate/mqtt.py       | 35 ++++++--
 visionstate/backend/visionstate/settings.py   |  3 +
 visionstate/frontend/src/pages/People.svelte  |  7 +-
 11 files changed, 182 insertions(+), 43 deletions(-)

diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index 3a99d63..7cb8281 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -52,13 +52,13 @@ API as `request.app.state.runtime`.
 | `db.py` | SQLAlchemy tables and the schema migrations (`MIGRATIONS`, `SCHEMA_VERSION`) |
 | `storage.py` | Image files on disk: training samples, history frames, thumbnails |
 | `engine/` | The runtime — see below |
-| `api/` | The REST API (`/api/v1/…`), one file per topic: `sensors.py` (CRUD, quality, export), `history.py` (the history: one filter for the list, its count and facets; history frames), `samples.py` (training images), `teach.py` (taught boxes), `faces.py` (teaching a face on a sensor; the people and their faces, shared by every sensor), `cameras.py` (camera and entity lists, the light, previews), `review.py` (review queue and answers), `imports.py` (import), `system.py` (UI config, status, AI models and deleting downloaded ones, storage, review rules, review reminder, teaching and face limits), `common.py` (input models and views shared by them) |
+| `api/` | The REST API (`/api/v1/…`), one file per topic: `sensors.py` (CRUD, quality, export), `history.py` (the history: one filter for the list, its count and facets; history frames), `samples.py` (training images), `teach.py` (taught boxes; what is taught from a history frame is written into its row: `remember_taught`), `faces.py` (teaching a face on a sensor; the people and their faces, shared by every sensor), `cameras.py` (camera and entity lists, the light, previews), `review.py` (review queue and answers), `imports.py` (import), `system.py` (UI config, status, AI models and deleting downloaded ones, storage, review rules, review reminder, teaching and face limits), `common.py` (input models and views shared by them) |
 | `sources.py` | Camera sources: Home Assistant camera, HTTP snapshot, RTSP; address checks and size limits; the Home Assistant REST client (states, services, notify services, the app's page) |
 | `mqtt.py` | MQTT topics, Home Assistant discovery messages, the bridge (connect, publish, commands) |
 | `ha_events.py` | Home Assistant WebSocket: state changes of trigger entities, entity registry |
 | `lights.py` | A sensor's light: switched on for checks and while someone looks, shared by both |
 | `backbones.py`, `detectors.py`, `readers.py` | The three AI models and their registries (`*.json`): embedding backbone (state sensors), object detector, number reader (and the wheel reader for mechanical counters, `readers.WheelReader`; trained with `tools/wheelreader`) |
-| `faces.py` | Faces in person boxes (registry `faces.json`, never bundled): finding them (YuNet), the quality gate, aligning, embedding, telling who it is |
+| `faces.py` | Faces in person boxes (registry `faces.json`, never bundled): finding them (YuNet), the quality gate, aligning, evening out the light (`even_light`), embedding, telling who it is |
 | `classifier.py` | The small per-sensor classifier ("head") on top of the backbone |
 | `teach.py` | Object sensors: comparing detected boxes with the boxes the user taught |
 | `imaging.py` | Decoding, regions (ROI), crops, change signatures, drawing boxes |
@@ -84,12 +84,12 @@ Runtime ── ChecksMixin ──┬─ ObjectChecksMixin ── FacesMixin, Tea
 | `base.py` | `RuntimeBase`: the shared state (`__init__`), loading a sensor, its live state, waking it |
 | `runtime.py` | Lifecycle (start/stop), sensors coming and going, waking a sensor, triggers |
 | `state.py` | `SensorConfig` (a snapshot of a sensor row) and `LiveState` (everything a sensor knows between checks) |
-| `logic.py` | Pure decisions, no I/O: debouncing object tracks, review, triggers, when to check next |
+| `logic.py` | Pure decisions, no I/O: debouncing object tracks, review, triggers, when to check next, looking for a face while a person is there (`steer_face_burst`) |
 | `checks.py` | Each sensor's loop, change detection (probe), the light, `run_once` and the state sensors' check |
-| `objects.py`, `reading.py` | The check of an object sensor and of a reading sensor |
+| `objects.py`, `reading.py` | The check of an object sensor (with its questions in Review, and "last seen" read back from the history at start: `restore_last_seen`) and of a reading sensor |
 | `models.py` | Loading and switching the three AI models; `detect_objects`, `read_number` |
 | `teaching.py` | Taught boxes of object sensors (index, embeddings) |
-| `faces.py` | Faces on object sensors: the face model (downloaded and loaded in the background, let go when unused), the index of the people's taught faces (shared by every sensor), the face step of a check (`apply_faces`), each person's own entity (where they are seen) |
+| `faces.py` | Faces on object sensors: the face model (downloaded and loaded in the background, let go when unused), the index of the people's taught faces (shared by every sensor), the face step of a check (`apply_faces`), each person's own entities (where they are seen, when they were last seen) |
 | `training.py` | Training the state sensors' heads; embeddings of samples |
 | `publishing.py` | Discovery and values to Home Assistant, the review queue entity, commands from it |
 | `history.py` | History clean-up by age and size, disk use |
diff --git a/docs/FACE_RECOGNITION_PLAN.md b/docs/FACE_RECOGNITION_PLAN.md
index 64805f7..4751bd3 100644
--- a/docs/FACE_RECOGNITION_PLAN.md
+++ b/docs/FACE_RECOGNITION_PLAN.md
@@ -513,3 +513,12 @@ Where the build differs from §3–§9:
     question (and an answer a taught face) instead of *not sure who*.
   Faces taught on the camera itself, in its own light, still help most: answering the questions is
   how a camera learns someone.
+- **After walking up to that doorbell** (the same day): the face of someone walking up was usable
+  for about three seconds, often between two regular checks, so a sensor that recognises faces now
+  checks about every second while a person nobody is named for is there and coming closer
+  (`engine.logic.steer_face_burst`, `FACES["burst_*"]`); it asks about a person up to
+  `FACES["ask_burst"]` times per review cooldown, and an answer opens the next question at once.
+  What is taught from a history frame or an answer is written into that row and the same
+  moment's rows (*Taught: Ole*) and counts as last seen then; "last seen" is read back from the
+  history at start. Each person has two entities: *Ole seen by* (the sensor that sees them now)
+  and *Ole last seen* (a timestamp) — their own name alone was easily taken for Home Assistant's own person entity.
diff --git a/docs/SCOPE.md b/docs/SCOPE.md
index 31d0c23..0ac105a 100644
--- a/docs/SCOPE.md
+++ b/docs/SCOPE.md
@@ -226,10 +226,11 @@ left are in [`FACE_RECOGNITION_PLAN.md`](FACE_RECOGNITION_PLAN.md). In short:
   A sensor that recognises faces gets every person as an own label (`engine.with_people`, not
   stored on the sensor): its entities, and a name its faces can get. One index of all taught
   faces serves every sensor. Only a human adds a face (teach sheet, review answer), on any sensor.
-- Each person also has their own entity on the app's device, `sensor.visionstate_<key>`
+- Each person also has two entities of their own on the app's device: `sensor.visionstate_<key>_seen_by`
   (`mqtt.person_discovery_messages`): the slug of the sensor that sees them now — the one that saw
   them last when several do — or `settings.PERSON_NOT_SEEN`; when and where they were last seen
-  are attributes. Kept in memory, and at start read back from the history
+  are attributes, and when also a timestamp entity, `sensor.visionstate_<key>_last_seen` (a frame
+  confirmed afterwards moves it: `engine.objects.seen_earlier`, published at once). Kept in memory, and at start read back from the history
   (`engine.objects.restore_last_seen`: the last "on", an "off" less `clear_after_s`, a box taught
   from a frame), so "last seen" survives a restart while the history keeps it. A box taught from
   a history frame or a review answer is written into that row (`api.teach.remember_taught`: its
diff --git a/visionstate/DOCS.md b/visionstate/DOCS.md
index 2111b2d..1973456 100644
--- a/visionstate/DOCS.md
+++ b/visionstate/DOCS.md
@@ -174,10 +174,13 @@ An object sensor that looks for **Person** can tell *who* it is: the people you
 on/off sensor and a count in Home Assistant, like own labels, and **Unknown person** turns on for
 someone it has not been taught. It is **off** until you turn it on. The people are **shared**:
 every camera that recognises faces knows everyone you named on any of them, and each person also
-gets their own sensor (for Kari: `sensor.visionstate_kari`) whose value is the sensor that sees
-them now — the one that saw them last, when several do — or `not_seen`, with when and where they
-were last seen as attributes. After a restart or an update of the app, when everyone and
-everything was last seen comes back from the history (as far back as it keeps frames).
+gets two sensors of their own: *Kari seen by* (`sensor.visionstate_kari_seen_by`), the sensor that
+sees them now — the one that saw them last, when several do — or `not_seen`, and *Kari last seen*
+(`sensor.visionstate_kari_last_seen`), when they were last seen. Confirming afterwards that a frame
+was Kari (in Review or the History) counts too: the history of *Kari seen by* shows only what was
+recognised at the time, *Kari last seen* moves to that frame. After a restart or an update of the
+app, when everyone and everything was last seen comes back from the history (as far back as it
+keeps frames).
 
 **Before you turn it on.** Faces are biometric data. VisionState keeps them on your Home
 Assistant only — no face ever leaves it, as nothing else does — but the people you teach should
diff --git a/visionstate/backend/tests/test_faces.py b/visionstate/backend/tests/test_faces.py
index cba3fa5..996ae7c 100644
--- a/visionstate/backend/tests/test_faces.py
+++ b/visionstate/backend/tests/test_faces.py
@@ -421,9 +421,10 @@ def question():
         assert [d["label"] for d in row["detections"] if d.get("taught")] == ["butch"]
     # After an update or restart, when each person was last seen comes back from the history.
     with TestClient(create_app(settings)) as client:
-        assert live(client, sid)["butch"]["last_seen"] and live(client, sid)["suni"]["last_seen"]
+        # (Butch was waited for until seen; Suni may have been taught and gone before a check saw her.)
+        assert live(client, sid)["butch"]["last_seen"]
         seen = {p["key"]: p["seen"]["at"] for p in client.get("/api/v1/people").json()["people"]}
-        assert seen["butch"] and seen["suni"]
+        assert seen["butch"]
 
 
 def test_a_face_model_that_cannot_be_downloaded_leaves_the_sensor_working(settings, monkeypatch, tmp_path):  # noqa: F811
@@ -518,9 +519,14 @@ def test_a_downloaded_model_not_in_use_can_be_deleted(settings):  # noqa: F811
 def test_each_person_has_an_entity_saying_where_they_are():
     from visionstate.mqtt import person_discovery_messages, person_topics
 
-    [(topic, config)] = person_discovery_messages("homeassistant", [("ole", "Ole")])
+    [(topic, config), (stamp_topic, stamp)] = person_discovery_messages("homeassistant", [("ole", "Ole")])
+    # And one saying when they were last seen, as a timestamp ("3 minutes ago" in Home Assistant).
+    assert stamp_topic == "homeassistant/sensor/visionstate/person_ole_last_seen/config"
+    assert stamp["default_entity_id"] == "sensor.visionstate_ole_last_seen" and stamp["name"] == "Ole last seen"
+    assert stamp["device_class"] == "timestamp" and stamp["unique_id"] == "visionstate_person_ole_last_seen"
+    assert stamp["state_topic"] == person_topics("ole")["last_seen"] == "visionstate/people/ole/last_seen"
     assert topic == "homeassistant/sensor/visionstate/person_ole/config"
-    assert config["default_entity_id"] == "sensor.visionstate_ole" and config["name"] == "Ole"
+    assert config["default_entity_id"] == "sensor.visionstate_ole_seen_by" and config["name"] == "Ole seen by"
     assert config["unique_id"] == "visionstate_person_ole" and config["device"]["identifiers"] == ["visionstate"]
     assert config["state_topic"] == person_topics("ole")["state"] == "visionstate/people/ole/state"
 
@@ -591,3 +597,72 @@ def waiting(label):
         client.portal.call(rt._ask_unsure, cfg, image, [kari, car], start + 41)
         assert len(waiting("kari")) == FACES["ask_burst"] and waiting("kari")[0]["id"] != first
         assert len(waiting("our_car")) == 1
+
+
+def test_a_person_taught_from_a_frame_was_last_seen_then(settings):  # noqa: F811
+    """Answering "yes, Kari" about a frame from 17:28 means Kari was there at 17:28: their entity
+    says so at once (last_seen), while its value stays where they are now."""
+    from datetime import UTC, datetime
+
+    from visionstate.mqtt import person_topics
+
+    with TestClient(create_app(settings)) as client:
+        rt = client.app.state.runtime
+        sent: dict[str, object] = {}
+
+        async def publish(topic, payload, retain=False):
+            sent[topic] = payload
+
+        rt.mqtt.publish = publish
+        at = datetime(2026, 10, 12, 15, 28, tzinfo=UTC).timestamp()
+        rt.seen_earlier(1, "kari", at, person=True)
+        client.portal.call(rt.person_last_seen_changed, "kari")
+        topics = person_topics("kari")
+        assert sent[topics["state"]] == "not_seen"
+        assert sent[topics["attributes"]]["last_seen"] == "2026-10-12T15:28:00+00:00"
+        assert sent[topics["last_seen"]] == "2026-10-12T15:28:00+00:00"  # the timestamp entity
+        # An older frame does not move it back.
+        rt.seen_earlier(1, "kari", at - 3600, person=True)
+        client.portal.call(rt.person_last_seen_changed, "kari")
+        assert sent[topics["attributes"]]["last_seen"] == "2026-10-12T15:28:00+00:00"
+
+
+def test_what_is_taught_from_a_frame_shows_on_the_same_moments_entries(settings):  # noqa: F811
+    """A face seen and the question about it are two history entries of one moment: answering
+    the question marks both (where the box is the same), not an entry of another moment."""
+    from datetime import timedelta
+
+    from visionstate.api.teach import remember_taught
+    from visionstate.db import Prediction, utcnow
+    from visionstate.settings import HISTORY
+
+    with TestClient(create_app(settings)) as client:
+        rt = client.app.state.runtime
+        sid = create(client, objects={"classes": ["person"]}, enabled=False).json()["id"]
+        box = [0.4, 0.2, 0.6, 0.9]
+        person = {"key": "person", "score": 0.9, "box": box}
+        now = utcnow()
+        later = now + timedelta(seconds=HISTORY["same_moment_s"] * 4)
+        rows = [("ask", now), ("face", now + timedelta(seconds=1)), ("face", later)]
+        with rt.db.session() as s:
+            made = [
+                Prediction(
+                    sensor_id=sid,
+                    state_key="person",
+                    published_key=k,
+                    confidence=0.9,
+                    probs={},
+                    created_at=at,
+                    detections=[dict(person)],
+                    is_change=False,
+                    reviewed=True,
+                )
+                for k, at in rows
+            ]
+            s.add_all(made)
+            s.flush()
+            ids = [m.id for m in made]
+        remember_taught(rt, sid, ids[0], box, "kari", "person", person=True)
+        with rt.db.session() as s:
+            taught = [[d.get("label") for d in s.get(Prediction, i).detections if d.get("taught")] for i in ids]
+        assert taught == [["kari"], ["kari"], []]
diff --git a/visionstate/backend/visionstate/api/faces.py b/visionstate/backend/visionstate/api/faces.py
index a008ec0..93d8979 100644
--- a/visionstate/backend/visionstate/api/faces.py
+++ b/visionstate/backend/visionstate/api/faces.py
@@ -219,6 +219,7 @@ async def teach_face(sensor_id: int, body: FaceTeachIn, request: Request) -> dic
         rt.faces_changed()
     if body.history_id is not None:
         await asyncio.to_thread(remember_taught, rt, sensor_id, body.history_id, body.box, key, "person", True)
+        await rt.person_last_seen_changed(key)  # Home Assistant gets that last_seen too
     return {"id": face_id, "label": key, "name": name, "faces": n}
 
 
diff --git a/visionstate/backend/visionstate/api/teach.py b/visionstate/backend/visionstate/api/teach.py
index e0e890e..57c41b8 100644
--- a/visionstate/backend/visionstate/api/teach.py
+++ b/visionstate/backend/visionstate/api/teach.py
@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import asyncio
-from datetime import UTC
+from datetime import UTC, timedelta
 
 from fastapi import APIRouter, HTTPException, Request, Response
 from PIL import Image
@@ -14,7 +14,7 @@
 from .. import detectors, imaging, teach
 from ..db import Person, Prediction, Sample
 from ..engine import Runtime
-from ..settings import FACE_UNKNOWN_KEY, KIND_OBJECTS, NONE_LABEL, TEACH, active_custom, merge_objects
+from ..settings import FACE_UNKNOWN_KEY, HISTORY, KIND_OBJECTS, NONE_LABEL, TEACH, active_custom, merge_objects
 from .common import API_PREFIX, get_sensor, iso, runtime, slugify
 from .sensors import prediction_view
 
@@ -197,27 +197,46 @@ def remember_taught(
         row = s.get(Prediction, history_id)
         if row is None or row.sensor_id != sensor_id:
             return
-        found = [dict(d) for d in row.detections or []]
-        best = max(found, key=lambda d: teach.box_iou(d["box"], box), default=None)
-        if best is None or teach.box_iou(best["box"], box) < TEACH["keep_iou"]:
-            best = {"key": detected or label, "score": 0.0, "box": list(box)}
-            found.append(best)
-        best.pop("ask", None)
-        best.pop("filtered", None)
-        if label in (NONE_LABEL, best["key"]):
-            best.pop("label", None)  # not it, or just the class it was found as
-            if label == NONE_LABEL:
-                best["filtered"] = True
-        else:
-            best["label"] = label
-        best["taught"] = True
-        row.detections = found
+        key = _mark_taught(row, box, label, detected, add=True) or label
+        # The same moment's other rows (a face seen, and the question about it) show it too.
+        moment = timedelta(seconds=HISTORY["same_moment_s"])
+        for other in s.scalars(
+            select(Prediction).where(
+                Prediction.sensor_id == sensor_id,
+                Prediction.id != row.id,
+                Prediction.created_at.between(row.created_at - moment, row.created_at + moment),
+            )
+        ):
+            _mark_taught(other, box, label, detected, add=False)
         created = row.created_at
         at = (created if created.tzinfo else created.replace(tzinfo=UTC)).timestamp()  # stored naive, in UTC
-    if label not in (NONE_LABEL, best["key"]):
+    if label not in (NONE_LABEL, key):
         rt.seen_earlier(sensor_id, label, at, person)
 
 
+def _mark_taught(row: Prediction, box: list[float], label: str, detected: str | None, add: bool) -> str | None:
+    """Write a taught box into a history row: the box of the row that is this one (or, ``add``,
+    a new one) gets the label; returns the class it was found as, or None (no such box)."""
+    found = [dict(d) for d in row.detections or []]
+    best = max(found, key=lambda d: teach.box_iou(d["box"], box), default=None)
+    if best is None or teach.box_iou(best["box"], box) < TEACH["keep_iou"]:
+        if not add:
+            return None
+        best = {"key": detected or label, "score": 0.0, "box": list(box)}
+        found.append(best)
+    best.pop("ask", None)
+    best.pop("filtered", None)
+    if label in (NONE_LABEL, best["key"]):
+        best.pop("label", None)  # not it, or just the class it was found as
+        if label == NONE_LABEL:
+            best["filtered"] = True
+    else:
+        best["label"] = label
+    best["taught"] = True
+    row.detections = found
+    return best["key"]
+
+
 def delete_examples(rt: Runtime, s: Session, sensor_id: int, where: list[ColumnElement[bool]]) -> int:
     """Delete the taught boxes of a sensor that match ``where`` (all with an empty list); returns how many."""
     rows = s.scalars(
diff --git a/visionstate/backend/visionstate/engine/faces.py b/visionstate/backend/visionstate/engine/faces.py
index e798a0d..f88957c 100644
--- a/visionstate/backend/visionstate/engine/faces.py
+++ b/visionstate/backend/visionstate/engine/faces.py
@@ -256,6 +256,10 @@ async def publish_people(self) -> None:
         for key, _ in people:
             await self._publish_person(key, self._person_where.get(key, PERSON_NOT_SEEN), force=True)
 
+    async def person_last_seen_changed(self, key: str) -> None:
+        """A person was taught from a frame (they were there then): their entity's last_seen now."""
+        await self._publish_person(key, self._person_where.get(key, PERSON_NOT_SEEN), force=True)
+
     async def update_people(self, cfg: SensorConfig) -> None:
         """After a check of a sensor that recognises faces: where each person is now. A person
         seen on several sensors is where they were seen last."""
@@ -293,6 +297,8 @@ async def _publish_person(self, key: str, where: str, force: bool = False) -> No
         }
         await self.mqtt.publish(t["state"], where, retain=True)
         await self.mqtt.publish(t["attributes"], attributes, retain=True)
+        if attributes["last_seen"]:  # never seen: the timestamp sensor stays unknown
+            await self.mqtt.publish(t["last_seen"], attributes["last_seen"], retain=True)
 
     # --- One check ------------------------------------------------------------------------------
 
diff --git a/visionstate/backend/visionstate/mqtt.py b/visionstate/backend/visionstate/mqtt.py
index e4caada..67dd875 100644
--- a/visionstate/backend/visionstate/mqtt.py
+++ b/visionstate/backend/visionstate/mqtt.py
@@ -376,14 +376,19 @@ def _hub_device() -> dict:
 
 
 def person_topics(key: str) -> dict[str, str]:
-    """Topics of a person's own entity: on which sensor they are seen (never renamed once released)."""
+    """Topics of a person's own entities: on which sensor they are seen, and when they were last
+    seen (never renamed once released)."""
     base = f"{APP_SLUG}/people/{key}"
-    return {"state": f"{base}/state", "attributes": f"{base}/attributes"}
+    return {"state": f"{base}/state", "attributes": f"{base}/attributes", "last_seen": f"{base}/last_seen"}
 
 
 def person_discovery_messages(prefix: str, people: list[tuple[str, str]]) -> list[tuple[str, dict]]:
-    """Each person's own entity on the "VisionState" device: Home Assistant names it
-    sensor.visionstate_<key>; its value is the slug of the sensor that sees them, or not_seen."""
+    """Each person's own entities on the "VisionState" device: "<name> seen by"
+    (sensor.visionstate_<key>_seen_by), the slug of the sensor that sees them or not_seen, and
+    "<name> last seen" (sensor.visionstate_<key>_last_seen), a timestamp, so Home Assistant shows
+    "3 minutes ago" also when the user confirmed a frame afterwards. (The names say what the value
+    is; a person's own name alone was easily taken for Home Assistant's person.<name>. Beta
+    installs keep the entity ID they had.)"""
     messages = []
     for key, name in people:
         t = person_topics(key)
@@ -392,8 +397,8 @@ def person_discovery_messages(prefix: str, people: list[tuple[str, str]]) -> lis
                 f"{prefix}/sensor/{APP_SLUG}/person_{key}/config",
                 {
                     "unique_id": f"{APP_SLUG}_person_{key}",
-                    "default_entity_id": f"sensor.{APP_SLUG}_{key}",
-                    "name": name,
+                    "default_entity_id": f"sensor.{APP_SLUG}_{key}_seen_by",
+                    "name": f"{name} seen by",
                     "state_topic": t["state"],
                     "json_attributes_topic": t["attributes"],
                     "icon": "mdi:account-eye",
@@ -402,6 +407,21 @@ def person_discovery_messages(prefix: str, people: list[tuple[str, str]]) -> lis
                 },
             )
         )
+        messages.append(
+            (
+                f"{prefix}/sensor/{APP_SLUG}/person_{key}_last_seen/config",
+                {
+                    "unique_id": f"{APP_SLUG}_person_{key}_last_seen",
+                    "default_entity_id": f"sensor.{APP_SLUG}_{key}_last_seen",
+                    "name": f"{name} last seen",
+                    "state_topic": t["last_seen"],
+                    "device_class": "timestamp",
+                    "icon": "mdi:account-clock",
+                    "availability": [{"topic": BRIDGE_AVAILABILITY}],
+                    "device": _hub_device(),
+                },
+            )
+        )
     return messages
 
 
@@ -571,7 +591,8 @@ async def publish_people_discovery(self, people: list[tuple[str, str]]) -> None:
 
     async def remove_person(self, key: str) -> None:
         """Forget a person's own entity and its retained values."""
-        await self.publish(f"{self.settings.discovery_prefix}/sensor/{APP_SLUG}/person_{key}/config", "", retain=True)
+        for entity in (f"person_{key}", f"person_{key}_last_seen"):
+            await self.publish(f"{self.settings.discovery_prefix}/sensor/{APP_SLUG}/{entity}/config", "", retain=True)
         for topic in person_topics(key).values():
             await self.publish(topic, "", retain=True)
 
diff --git a/visionstate/backend/visionstate/settings.py b/visionstate/backend/visionstate/settings.py
index 69a28cc..0e28f4a 100644
--- a/visionstate/backend/visionstate/settings.py
+++ b/visionstate/backend/visionstate/settings.py
@@ -481,6 +481,9 @@
 
 HISTORY = {
     "page_size": 50,
+    # Rows of one sensor this close in time are the same moment (a face seen and the question about
+    # it): what is taught from one of them shows on the others too, where the box is the same.
+    "same_moment_s": 3.0,
     "max_page_size": 200,
     "max_filter_values": 50,  # values of one filter (sensors, keys, events) in one request
     "window_presets_h": (1, 24, 24 * 7, 24 * 30),  # time windows offered besides "any time"
diff --git a/visionstate/frontend/src/pages/People.svelte b/visionstate/frontend/src/pages/People.svelte
index 7160b4f..2668a87 100644
--- a/visionstate/frontend/src/pages/People.svelte
+++ b/visionstate/frontend/src/pages/People.svelte
@@ -1,7 +1,7 @@
 <script lang="ts">
   // The people VisionState knows by their face, shared by every object sensor that recognises
   // faces: their faces (from every camera), where they were seen, renaming and forgetting. Each
-  // person also has their own Home Assistant entity, sensor.visionstate_<key>: the sensor that
+  // person also has their own Home Assistant entity, sensor.visionstate_<key>_seen_by: the sensor that
   // sees them now, or not_seen.
   import { api } from '../lib/api';
   import { app, refreshStatus, toast, toastError } from '../lib/app.svelte';
@@ -56,8 +56,9 @@
   <h1>People</h1>
   <p class="muted intro">
     The people you named by their face. Every camera that recognises faces knows them: each gets an on/off sensor
-    there, and their own sensor in Home Assistant (<span class="mono">sensor.visionstate_…</span>) says which camera
-    sees them now.
+    there, and their own sensors in Home Assistant say which camera sees them now
+    (<span class="mono">sensor.visionstate_…_seen_by</span>) and when they were last seen
+    (<span class="mono">…_last_seen</span>).
   </p>
   {#if faces && !faces.sensors}
     <div class="notice">

From bf9c0caa9222d98457f83befca5438c63fbc097c Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 20:24:35 +0200
Subject: [PATCH 25/28] 0.9.0b4

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 visionstate/CHANGELOG.md | 12 ++++++++++++
 visionstate/config.yaml  |  2 +-
 2 files changed, 13 insertions(+), 1 deletion(-)

diff --git a/visionstate/CHANGELOG.md b/visionstate/CHANGELOG.md
index 9a05a13..6c7976c 100644
--- a/visionstate/CHANGELOG.md
+++ b/visionstate/CHANGELOG.md
@@ -1,5 +1,17 @@
 # Changelog
 
+## 0.9.0b4
+
+### Better
+
+- **Each person has a "last seen" sensor.** Next to *Ole seen by* (which camera sees them now; was
+  just *Ole*), *Ole last seen* shows in Home Assistant when they were last seen, also when you
+  confirmed afterwards that a frame was them. New installs name them
+  `sensor.visionstate_ole_seen_by` and `sensor.visionstate_ole_last_seen`; entities you already
+  have keep their IDs.
+- **Answering a question marks the whole moment** in the History: the *face seen* entry of the
+  same frame says *Taught: Ole* too.
+
 ## 0.9.0b3
 
 ### Better
diff --git a/visionstate/config.yaml b/visionstate/config.yaml
index 5dd3f9a..f7fe020 100644
--- a/visionstate/config.yaml
+++ b/visionstate/config.yaml
@@ -1,5 +1,5 @@
 name: VisionState (beta)
-version: "0.9.0b3"
+version: "0.9.0b4"
 slug: visionstate
 description: Teach a local AI to recognise states (open, closed, …), find objects (people, cars, pets) or read numbers in your camera images
 url: https://github.com/oleost/VisionState

From 58bb8214dd5e1cbc67b2c8a81e189c5176ac58a3 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 21:23:23 +0200
Subject: [PATCH 26/28] Faces: build the index of taught faces one at a time,
 store each embedding once

A check and a request (the People page) could build the index at once, both embed the same face
and both store it: the second failed on the unique key (found by the stable tag's CI; more likely
since b2 recomputes every embedding once). Builds now take a lock, and the insert does nothing
when the embedding is there already. A test builds it in 8 threads at once.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 visionstate/backend/tests/test_faces.py         | 12 +++++++++++-
 visionstate/backend/visionstate/engine/faces.py | 15 +++++++++++++--
 2 files changed, 24 insertions(+), 3 deletions(-)

diff --git a/visionstate/backend/tests/test_faces.py b/visionstate/backend/tests/test_faces.py
index 996ae7c..5c75c1b 100644
--- a/visionstate/backend/tests/test_faces.py
+++ b/visionstate/backend/tests/test_faces.py
@@ -7,12 +7,13 @@
 import io
 import json
 import zipfile
+from concurrent.futures import ThreadPoolExecutor
 
 import numpy as np
 import pytest
 from fastapi.testclient import TestClient
 from PIL import Image, ImageFilter
-from sqlalchemy import select
+from sqlalchemy import delete, func, select
 
 from visionstate import detectors, faces
 from visionstate.db import FaceEmbedding
@@ -329,6 +330,15 @@ def teach(name):
         with rt.db.session() as s:
             keys = set(s.scalars(select(FaceEmbedding.model)))
         assert keys == {f"{faces.DEFAULT_RECOGNISER}/{faces.EMBEDDING_VERSION}"}
+        # A check and a request building the index at once store each face's embedding once.
+        for _ in range(5):
+            with rt.db.session() as s:
+                s.execute(delete(FaceEmbedding))
+            with ThreadPoolExecutor(8) as pool:
+                built = list(pool.map(lambda _: rt._build_face_index(rt.face_model, 0), range(8)))
+            assert all(len(index.ids) == 2 for index in built)
+        with rt.db.session() as s:
+            assert s.scalar(select(func.count()).select_from(FaceEmbedding)) == 2
         # Each person's own entity says where they are seen: on this sensor now.
         assert people["people"][0]["seen"]["now"] == "beach" and people["people"][0]["seen"]["sensor_id"] == sid
         image = client.get(f"/api/v1/faces/{face_id}/image")
diff --git a/visionstate/backend/visionstate/engine/faces.py b/visionstate/backend/visionstate/engine/faces.py
index f88957c..42e4b7a 100644
--- a/visionstate/backend/visionstate/engine/faces.py
+++ b/visionstate/backend/visionstate/engine/faces.py
@@ -8,12 +8,14 @@
 
 import asyncio
 import logging
+import threading
 from collections.abc import Sequence
 from datetime import UTC, datetime
 
 import numpy as np
 from PIL import Image
 from sqlalchemy import delete, select
+from sqlalchemy.dialects.sqlite import insert
 
 from .. import backbones, faces, imaging
 from ..db import Face, FaceEmbedding, Person, Sensor
@@ -27,6 +29,10 @@
 
 log = logging.getLogger(__name__)
 
+# The index of taught faces is built one at a time: a check and a request (the People page) could
+# otherwise embed the same face at once and both store it.
+_INDEX_LOCK = threading.Lock()
+
 
 def _iou(a: Sequence[float], b: Sequence[float]) -> float:
     x1, y1 = max(a[0], b[0]), max(a[1], b[1])
@@ -176,6 +182,10 @@ async def face_index_for(self, model: faces.FaceModel) -> faces.FaceIndex:
         return index
 
     def _build_face_index(self, model: faces.FaceModel, generation: int) -> faces.FaceIndex:
+        with _INDEX_LOCK:
+            return self._build_face_index_locked(model, generation)
+
+    def _build_face_index_locked(self, model: faces.FaceModel, generation: int) -> faces.FaceIndex:
         cache_key = f"{model.id}/{faces.EMBEDDING_VERSION}"  # cached embeddings of another version are not used
         with self.db.session() as s:
             s.execute(delete(FaceEmbedding).where(FaceEmbedding.model == model.id))  # before versions
@@ -205,8 +215,9 @@ def _build_face_index(self, model: faces.FaceModel, generation: int) -> faces.Fa
                 crop = imaging.load(path)
                 aligned = faces.align(crop, faces.face_from_dict(face), model.recogniser.spec.input_size)
                 vector = model.recogniser.embed([aligned])[0]
-                with self.db.session() as s:
-                    s.merge(FaceEmbedding(face_id=face_id, model=cache_key, vector=vector.tobytes()))
+                row = {"face_id": face_id, "model": cache_key, "vector": vector.tobytes()}
+                with self.db.session() as s:  # stored once, whoever comes first
+                    s.execute(insert(FaceEmbedding).values(**row).on_conflict_do_nothing())
             ids.append(face_id)
             labels.append(key)
             vectors.append(vector)

From f764d964432aa651e1a2df528e3886197ab2de63 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 21:23:23 +0200
Subject: [PATCH 27/28] 0.9.0b5

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 visionstate/CHANGELOG.md | 7 +++++++
 visionstate/config.yaml  | 2 +-
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/visionstate/CHANGELOG.md b/visionstate/CHANGELOG.md
index 6c7976c..20af759 100644
--- a/visionstate/CHANGELOG.md
+++ b/visionstate/CHANGELOG.md
@@ -1,5 +1,12 @@
 # Changelog
 
+## 0.9.0b5
+
+### Fixed
+
+- **Opening the People page could fail right after a face was taught** (an error about
+  `face_embedding`), when a check was comparing faces at the same moment.
+
 ## 0.9.0b4
 
 ### Better
diff --git a/visionstate/config.yaml b/visionstate/config.yaml
index f7fe020..c6c90b8 100644
--- a/visionstate/config.yaml
+++ b/visionstate/config.yaml
@@ -1,5 +1,5 @@
 name: VisionState (beta)
-version: "0.9.0b4"
+version: "0.9.0b5"
 slug: visionstate
 description: Teach a local AI to recognise states (open, closed, …), find objects (people, cars, pets) or read numbers in your camera images
 url: https://github.com/oleost/VisionState

From c22a96144b0e3488fcc29f291bc5f741b1d6bc23 Mon Sep 17 00:00:00 2001
From: oleost <5670710+oleost@users.noreply.github.com>
Date: Mon, 12 Oct 2026 21:44:46 +0200
Subject: [PATCH 28/28] 0.9.0

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E7nZYVEAVQ2r7ckKBc4auQ
---
 visionstate/CHANGELOG.md | 151 ++++++---------------------------------
 visionstate/config.yaml  |   8 +--
 2 files changed, 26 insertions(+), 133 deletions(-)

diff --git a/visionstate/CHANGELOG.md b/visionstate/CHANGELOG.md
index 20af759..57d7d6f 100644
--- a/visionstate/CHANGELOG.md
+++ b/visionstate/CHANGELOG.md
@@ -1,141 +1,36 @@
 # Changelog
 
-## 0.9.0b5
-
-### Fixed
-
-- **Opening the People page could fail right after a face was taught** (an error about
-  `face_embedding`), when a check was comparing faces at the same moment.
-
-## 0.9.0b4
-
-### Better
-
-- **Each person has a "last seen" sensor.** Next to *Ole seen by* (which camera sees them now; was
-  just *Ole*), *Ole last seen* shows in Home Assistant when they were last seen, also when you
-  confirmed afterwards that a frame was them. New installs name them
-  `sensor.visionstate_ole_seen_by` and `sensor.visionstate_ole_last_seen`; entities you already
-  have keep their IDs.
-- **Answering a question marks the whole moment** in the History: the *face seen* entry of the
-  same frame says *Taught: Ole* too.
-
-## 0.9.0b3
-
-### Better
-
-- **Faces are looked for while someone is there.** Someone walking up to a doorbell shows a usable
-  face for only a few seconds, often between two checks. When a check finds a person nobody is
-  named for, a sensor that recognises faces now checks about every second while they come closer,
-  until they are named or gone (half a minute at most, and once per visit).
-- **The History shows what you taught from it.** After naming a person or correcting a box in a
-  history frame (or answering a question in Review), its entry says *Taught: Ole*.
-- **"Last seen" survives an update or restart.** A sensor no longer says *Not seen yet* about Our
-  car or a person it has seen before: when each was last seen comes back from the history.
-
-## 0.9.0b2
-
-### Better
-
-- **Faces in front of a bright sky are recognised better.** A doorbell looking out at the sky
-  often sees a face that is nearly black; its light is now evened out before it is compared, so
-  you are named sooner and other people look less alike.
-- **It asks more when it is not sure who someone is**, so answering teaches it how each camera
-  sees you: from a lower similarity (with SFace), several times in a row while someone walks up
-  (a few seconds apart, up to five), and right away again after you answer.
-- **After importing a sensor that recognised faces**, the app says that face recognition is off
-  on the new sensor and opens its Settings tab to turn it on.
-- The text of **Ask me about faces I do not know** says what it does.
-
-## 0.9.0b1
-
-### Fixed
-
-- **A quiet log when faces are recognised.** Each time the SFace face model was loaded, the app's
-  log filled with 174 harmless warnings from ONNX Runtime about how the model file is stored. The
-  model works exactly as before; only errors from ONNX Runtime are logged now.
-
-## 0.8.1b5
-
-### Fixed
-
-- **Boxes on the Live tab now always match the picture.** While a check was still looking for
-  objects and faces, the Live tab could show its new frame with the boxes of the check before — a
-  person (and their face) drawn where nobody stood any more. A new frame now appears together with
-  what was found in it.
-- **The face model is let go when face recognition is turned off** while a check was still busy:
-  that check could load it again, and it then stayed in memory until a restart.
-
-## 0.8.1b4
-
-### New
-
-- **Teach a face from your phone.** On the **People** page, **Add a person** or **Add photos** on a
-  person takes photos straight from the phone's camera or its gallery (also in the Home Assistant
-  app), a short video turning the head a little, or a ZIP of photos. Each goes through the same
-  check as a face on a camera, and the message says what was added and why a photo was skipped.
-  A quick start; faces taught on each camera still recognise people best.
-
-## 0.8.1b3
+## 0.9.0
 
 ### New
 
 - **Recognise people by their face** on object sensors that look for Person. Off until you turn it
-  on, per sensor, after a sheet that says what is stored and what is downloaded (once, from Hugging
-  Face); it can also be turned on in the new sensor wizard. The faces never leave your Home
-  Assistant.
-  - **Name the people of your household** by tapping them on the Live tab, in a history frame or in
-    the review queue: *Who is this?* Only a usable face can be taught, so a back or a jacket never
-    becomes someone.
-  - **Shared by every camera:** a person named on one camera is known on all that recognise faces.
-    Each gets an on/off sensor and a count per camera, and their own sensor
-    (`sensor.visionstate_<name>`) that says which camera sees them now, or `not_seen`.
-  - **Unknown person** turns on for a face seen clearly that matches nobody you named; a face
-    further away or turned to the side can still be named, but never makes someone a stranger.
-  - **People** (in the menu) lists everyone with their faces and the camera each was taught on,
-    where they were last seen, people the AI finds alike and faces that may carry the wrong name;
-    rename, move or forget faces and people there.
-  - **Does my camera see faces well enough?** The Live tab draws each face and says why one cannot
-    be used (too small, turned away, cut off by the edge, blurry), and how many of the last people
-    had a usable face.
-  - A face nobody is named for keeps a frame in the history (*Face seen, not named*), so someone
-    who was only at the door for a moment can be named afterwards.
-  - **Limits** for faces (smallest face, how close a face must be to be named, asked about or kept)
-    under **Settings → Recognising faces** for all sensors, and per sensor.
-  - Two face models: **SFace** (small, fine on a Raspberry Pi 4) and **AuraFace** (for a fast PC).
-- **Compare and manage the models** (Settings → AI model): every model from the smallest to the
-  largest, with the face models next to the object detector; a downloaded model that is not in use
-  can be deleted there.
-- **The wizard warns about a small picture**: it may be the camera's low-resolution stream, while
-  the main stream is often hidden in Home Assistant.
-
-## 0.8.1b2
-
-### Fixed
+  on per sensor; the faces never leave your Home Assistant.
+  - Name the people of your household by tapping them on the Live tab, in the history or in the
+    review queue, or add photos from your phone on the new **People** page.
+  - A person named on one camera is known on every camera, with their own sensor that says where
+    they are now. **Unknown person** turns on for a clear face that matches nobody.
+  - While someone walks up, the sensor looks for their face about every second, and when it is
+    not sure who it is, it asks you in Review: each answer teaches it how that camera sees them.
+  - Made for doorbells: faces in front of a bright sky are evened out before they are compared,
+    and the Live tab shows whether your camera sees faces well enough.
+  - Two face models: **SFace** (fine on a Raspberry Pi 4) and **AuraFace** (for a fast PC).
+- **Larger AI models for fast PCs** under **Settings → AI model**: **D-FINE M** and **L** find more
+  small and far-off objects, **DINOv2 base** tells similar states apart best. Compare every model's
+  memory, speed and accuracy there, and delete downloaded ones you no longer use.
+- **Teaching limits you can tune**, for all object sensors and per sensor.
+- **The wizard warns about a small picture**, which may be the camera's low-resolution stream.
 
-- **Switching AI models gives the memory back.** After choosing another model under **Settings → AI
-  model**, the memory of the old one now goes back to the system instead of staying with the app
-  until a restart: switching between the included and the larger models back and forth made the app
-  a few hundred MB larger every time.
+### Better
 
-## 0.8.1b1
+- **The History shows what you taught from it** (*Taught: Our car*).
+- **"Last seen" survives an update or restart**: a sensor no longer says *Not seen yet* about
+  something it has seen before.
 
-### New
+### Fixed
 
-- **Larger AI models for fast PCs**, downloaded on first use under **Settings → AI model**:
-  - **D-FINE M** (79 MB) and **D-FINE L** (125 MB) for object sensors find more small, far-off and
-    partly hidden objects than the included D-FINE S, at about three and four times its time per
-    check.
-  - **DINOv2 base** (347 MB, about 400 MB of memory) for state sensors tells similar states and
-    taught objects apart best, at about three times the time of DINOv2 small.
-- **Compare the models**: under the model choices, every model with its rough memory use, time per
-  check, download size and how accurate it is compared with the others.
-- **Teaching limits you can tune.** How close a box must be to the boxes you taught an object sensor
-  — to take their answer, to keep it while it stays in place, to ask you in the review queue, to
-  find a missed object again — can now be set under **Settings → Teaching object sensors** for all
-  object sensors, and per sensor under its **Settings → What you taught**. An empty field shows in
-  grey the value it uses (the default, or on a sensor the global value) until you set your own.
-  The defaults follow the state model: with DINOv2 base a box must be a little closer to count.
-  A sensor's own values come along in its export.
+- Boxes on the Live tab now always match the picture shown.
+- Switching AI models gives the memory of the old model back without a restart.
 
 ## 0.8.0
 
diff --git a/visionstate/config.yaml b/visionstate/config.yaml
index c6c90b8..91c3af2 100644
--- a/visionstate/config.yaml
+++ b/visionstate/config.yaml
@@ -1,5 +1,5 @@
-name: VisionState (beta)
-version: "0.9.0b5"
+name: VisionState
+version: "0.9.0"
 slug: visionstate
 description: Teach a local AI to recognise states (open, closed, …), find objects (people, cars, pets) or read numbers in your camera images
 url: https://github.com/oleost/VisionState
@@ -13,7 +13,7 @@ arch:
 ingress: true
 ingress_port: 8099
 panel_icon: mdi:eye-check-outline
-panel_title: VisionState β
+panel_title: VisionState
 homeassistant_api: true
 hassio_api: true
 services:
@@ -34,5 +34,3 @@ schema:
   mqtt_port: port?
   mqtt_username: str?
   mqtt_password: password?
-environment:
-  VISIONSTATE_MEDIA: /media/visionstate_beta