* Rate events over at least one second
EventsPerSecond.eps() divided the event count by the time since start(),
which can be a few milliseconds right after a restart. Frames buffered
during an ffmpeg restart then report as 100+ fps, and the same happens to
the detector fps. Use a window of at least one second.
* Keep sub-second windows consistent
Floor the divisor at the window length when the window is shorter than a
second, so a caller with a sub-second window still gets its true rate.
2026-09-24 06:28:00 -06:00
A. AhmetGitHubgreptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* feat(deepx): add DEEPX NPU detector and runtime integration.
* feat(deepx): enforce model_format requirement when ppu is enabled and add integrity checks for driver installation
* Update frigate/detectors/plugins/deepx.py
Public method lacks docstring
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Refactor DEEPX detector tests, support SSD and DAMO-YOLO
* feat(deepx): add anchor-free output decoding and corresponding tests
* Add tests and updates for DEEPX detector and refactor DEEPX accelerator code structure.
* fix: enhance model type validation and update documentation for DEEPX detector
* fix: add support for customizable score and NMS thresholds
* refactor: infer YOLO layout from the model, drop per-detector options and the dxrtd placeholder
* fix: keep only the anchor-free PPU verdict, re-read anchor-based each frame
* Update latency data for DEEPX NPU
* Expanding PPU support for DEEPX and set yolo-generic as default.
* enhance scale count resolution logic
* Extend PPU layout handling and YOLOX support to DEEPX detector
* fix: assume the largest PPU anchor table when the .dxnn has no layout
* Improve PPU decoding and introduce strides handling
* Improve PPU scope and fix box format mismatch
* Fix unnamed node issue that breaks traversal
* fix: update object detection model type description to remove outdated architecture
---------
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* don't display audio transcription provider message as health notice
* show remote provider for audio transcription in health pane
* adjust trigger and notifications messages to be consistent with the rest of the settings UI
* disable save buttons when there are no changes in config editor
* fix audio manager crash when a camera is added at runtime
The audio processor and the camera maintainer both poll the same `add` config update on their own one second timers, and the maintainer is what creates `camera_metrics[name]`. When the audio processor got there first it looked the new camera up before that entry existed, and the `KeyError` took down the whole `frigate.audio_manager` process. Whether it happens depends purely on which poll fires first, so cloning a camera from the UI fails or succeeds at random. `spawn_if_needed` now skips a camera whose metrics aren't there yet and picks it up on the next poll, the same way it already waits on a late ffmpeg update.
`AudioEventMaintainer` holds the `CameraMetrics` object now instead of indexing the manager dict on every audio chunk, which drops the IPC round trips and means a removed camera can't `KeyError` out of `detect_audio` after the maintainer pops the entry. The audio process is also registered with the watchdog, since a crash there previously left audio detection dead for every camera until a full restart, and it now receives the shared `DataProcessorMetrics` so `AudioTranscriptionRealTimeProcessor` gets the same type as the other real time processors.
* fix stationary max_frames dropping other tracked objects
When `max_frames` was set for a label, deregistering one object rebuilt norfair's list with a filter that kept an object only if it was both not the target and already on its way out, so every other healthy object of that label was dropped along with it. Any car leaving the frame took the rest of the cars with it and they came back as new tracked objects a few frames later. The filter now removes only the target, and objects that are expiring are still reaped by norfair on the next update.
* fix test
* fix skip_motion_threshold permanently disabling motion detection
The skip check returned before the two `accumulateWeighted` calls at the end of `detect`, so a skipped frame never made it into the background and setting `calibrating` there only picked a faster alpha for calls that never ran. `avg_frame` starts as an all zero image and a normally lit scene differs from black across nearly the whole frame, so the cameras I tested measure 0.84 to 0.98 against it. Any `skip_motion_threshold` below that number skips the first frame, leaves the background black, and skips every frame after it. Motion detection is dead for that camera until the setting is removed or Frigate restarts, with no motion boxes, no motion recordings, and no regions for the tracker since the detector stays calibrating.
Startup isn't the only way in. `update_mask` zeroes the background on any motion config change, and once a camera has calibrated the first IR switch or PTZ move freezes the background on the old scene, so it can't transition to the new one, which is the case the option exists for. The frame is now blended in before the early return at the same 0.2 alpha the calibrating path uses elsewhere, so a large scene change is still suppressed while the background catches up, about a second on a 5 fps camera, and then motion comes back.
* dump ffmpeg logs on every restart
The record watchdog restarted ffmpeg without flushing its `LogPipe`, so a camera whose recording segments went stale never showed a single line of ffmpeg output. The dump now happens in `start_or_restart_ffmpeg` right after the stop, which covers the stale record path, the record crash path, and the audio restart. `reset_capture_thread` and the audio `log_and_restart` fallback keep their own dumps since both pass `ffmpeg_process=None`.
* dump ffmpeg logs once per restart
The audio restart path dumped the log pipe itself before calling the helper, so the restart dump printed a second "last 100 lines" heading over an already drained deque and split the tail that `stop_ffmpeg` flushed into its own section. The heading is now only printed when there's something under it, and the audio path leaves the dump to the restart so each failure produces one section.
* keep all logpipe dumps consistent
* check for a valid frame before using its shape
With the camera offline, no preview frame, and `camera-error.jpg` missing, `latest_frame` read `frame.shape` before its `frame is None` check, so it raised `AttributeError` and answered 500 with a traceback instead of the intended "Unable to get valid frame". The check now runs first.
* fix the has_clip self-heal for events with no recordings
`vod_event` looked for a `(body, 404)` tuple, but `vod_ts` returns a `JSONResponse`, so the check never matched and an old event whose recordings are gone kept offering a clip that can't play. It now checks the response status code.
* return 403 for a snapshot or thumbnail on another camera
The broad `except Exception` handlers in `event_snapshot` and `event_thumbnail` caught the `HTTPException` from `require_camera_access`, so a restricted user asking for another camera's snapshot got a 404 instead of a 403, and for an object still being tracked the snapshot was rendered before the check ran. Both endpoints now look up the event and check access in their own block, the way the other endpoints do, so a denial propagates.
* find DST transitions to the second
`get_dst_transitions` probed the offset once every 24 hours from the start time and reported a change at the first probe after it, up to a day late, so events, review items and recordings near a transition were grouped into days with the old offset. A transition after the last daily probe wasn't found at all. The end of the range is probed too now, and a probe that sees the offset change bisects the interval to the second of the transition.
* don't run page shortcuts for keys a dialog already handled
Radix dismisses a dialog on Escape from a capture-phase keydown listener and calls `preventDefault()` without stopping propagation, so `useKeyboardListener` still ran the page's Escape shortcut: cancelling the delete dialog in the face library or a classification model also cleared the whole selection. Keys another shortcut hook handled still get through, since their listener order changes with every render.
* fix train image filtering for a class with a dash
The backend writes a class with a `-` as `_` in train file names, since it splits those names on `-`, while a dataset folder keeps the dash. Filtering the Train grid by `half-open` compared it with `half_open` and hid every attempt. Both sides are normalized the same way now.
* don't edit a chat message while a reply streams
The edit button stayed active while a reply streamed. `submitConversation` returns early while loading, but the message bubble still closed its editor, so the edit was silently lost. The edit button is hidden while a reply streams, and an editor that's already open keeps its draft with send disabled until the reply ends.
* fix restart failing under non-root
restart_frigate() called psutil.Process(1).terminate() to signal s6-svscan, but s6-svscan runs as root while frigate runs as uid 1000, so the call raised AccessDenied. That exception escaped every caller: the UI restart button dropped its websocket client, MQTT restart and Save & Restart just logged and did nothing, and the watchdog crashed its own monitoring thread on a dead detector. This catches AccessDenied and falls through to the existing SIGINT branch, which exits the process for s6 to restart it.
* show runtime overrides in the settings form
The settings form read a camera section's saved config value, but its dependent warnings (audio transcription requiring audio detection, snapshots requiring detect, etc.) read the live config instead. A runtime toggle from the live view, MQTT, or an active profile can turn a section off without touching yaml, and that override persists across restarts, so the Enable switch showed on while the warning said the feature wasn't enabled. This adds an "Overridden (Live)" badge to any field whose live value differs from what's saved, and swaps the affected warnings to runtime-specific wording when a runtime override is the actual cause instead of the config.
* fix mobile overflowing icons in system due to new health pane
* fix genai settings keeping a stale model and dropping roles after save
Switching a GenAI entry's provider left the previous provider's model selected, so saving wrote a model the new provider doesn't serve. llama.cpp can't find that model in `/v1/models`, so the backend reported every capability as false for the entry, and once the save refetched `genai/models` the roles widget stripped `transcribe` from the form on its own. The section showed unsaved changes right after saving, and saving again would have dropped the role. Switching provider now clears the model, and the roles widget only strips a role for a model or provider picked in the form, since the entry-level capability flags only describe the saved model. A selected role stays visible when the provider can't confirm it, so it can still be switched off. The llama.cpp model list also no longer repeats a model whose alias matches its id, which is what `--alias` produces.
* close onvif sessions on shutdown
`OnvifController.close()` only stopped its event loop, so the aiohttp sessions each `ONVIFCamera` holds and the `_poll_config_updates` task were left to be garbage collected during interpreter shutdown, when their warnings can no longer be logged. Every restart ended with a run of `Unclosed client session` and `Task was destroyed but it is pending!` logging errors, which only became visible once restart started exiting the process itself under non-root. `close()` now closes each camera's client and cancels the tasks on the loop before stopping it.
* fixes
* fixes
Validate config exists 0 regardless of if the config is valid or not.
This makes it not very useful for CI
Tiny fix to bail non-zero if the config is invalid
* Add support for running transcription with GenAI
* Improve audio joining
* Fix GenAI model capability reporting
* Support language correctly
* Migrate existing users to keep english selected
* Fix models
* Fix tests
* Fix accepted null model
* Handle slwo providers
* Implement annotated frames mode for GenAI reviews to improve models with lacking temporal understanding
* Updates
* Improve debug sharing
* Do not number objects
* Fix assumptions
* Remove unhelpful content
* Improve object data sent as part of prompt
* Cleanup ollama dumbness
* Bind db
* Fixes
* Cleanup
* improve zone renaming
Zone rename now saves as one JSON body via config/set, moving required_zones and profile overrides instead of leaving stale references
* fixes
* allow users to select live streaming technology
* fix webrtc being downgraded to mse on load
`useUserPersistence` seeds state with the default and loads asynchronously, so the first render always used `mse` instead of the saved choice, and `useWebRTCGloballyAvailable` reports `checking` until the probe settles and re-enters that state on every consumer mount, so a saved `webrtc` was rewritten to `mse` even after the probe had already passed. On Safari the MSE player then timed out and latched the jsmpeg fallback. A pending probe now counts as available, the player waits on `autoLive` until the stored preferences load, and `handleError` gates on the mode in use since the fallback flag no longer implies webrtc is untried. A rejected IndexedDB read also resolves `loaded` now, so a blocked store can't leave the player waiting forever.
* add support for configurable ICE servers in WebRTC player
* add mic error state, fix dialog overwriting saved choice and dashboard ignoring stream
* tweaks
fix duplicated styles, move fonts to src/assets/fonts for vite to bundle (nginx already rewrites correctly), and fix fast refresh for PreviewController, auth context/provider, and statusbar context
* update date-fns, i18next, react-dropzone, react-markdown and @types/node
react-i18next 17 peers `i18next >= 26.2.0`, so the two move together. react-day-picker already depends on date-fns 4, so date-fns now dedupes to a single copy. react-dropzone 20 declares `node >=22` in `engines`, but npm only warns on Node 20 and nothing in the build needs Node 22.
* update js-yaml, @hookform/resolvers and prettier
js-yaml 5 has no default export, so `DictAsYamlField` imports `dump`, `load` and `YAMLException` by name. @hookform/resolvers 5 types `zodResolver` with the schema's input and output types separately, and fields with defaults are optional on input, so the zone and classification model forms pass both types to `useForm`. zod's range now starts at 3.25, which resolvers 5 requires.
* format with prettier 3.9
* remove unused web test deps
Nothing runs vitest. The CI step that called `npm run test` is commented out, `web/__test__/` was deleted in https://github.com/blakeblackshear/frigate/pull/8983 so `setupFiles` points at a missing file, and there are no unit tests, so `npx vitest run` only picks up the Playwright specs and fails. jsdom, `@testing-library/jest-dom`, msw and fake-indexeddb were only there for vitest.
* update contributing docs
* remove unused deps
konva 10.5 removed the private `Node._lastPos`, so `PolygonCanvas` now reads the dragged point from `getAbsolutePosition()`, which returns the position konva just applied. monaco-yaml 5.5 takes formatter options instead of a boolean for `format`. The radix packages move together so every shared primitive stays a single copy under the `react-slot` and `compose-refs` overrides.
* fix recordings unavailable endpoint when no params are provided
we already import the datetime class directly, so those attribute lookups raised AttributeError and the request returned 500
* reject JWTs whose role is no longer in the config
`/auth` trusted the role inside the JWT and re-signed it on refresh without checking the config, so a user whose restricted role was deleted kept a session carrying a role that isn't in `auth.roles`. The media, clip, recording, export and go2rtc checks treat a missing role the same as a role with no camera list, so that session could open every camera. `/auth` now returns 401 for a token whose role isn't configured, which sends the user back through login, and login already falls back to `viewer` for a role that's gone.
* delete the deleted camera group's layout, not the open one's
Deleting a camera group removed the layout of the group being viewed, because the dialog's delete was bound to `${activeGroup}-draggable-layout`. It also cleared the saved group, and both ran before the `config/set` request whether or not it succeeded, so deleting one group while viewing another lost the open group's layout and left the deleted group's layout behind. The deleted group's own layout is now removed after a successful save, and the saved group is only cleared when it was the open group.
* don't block API when querying PTZ info
camera_ptz_info is async but waited on the ONVIF controller's future with future.result(), blocking the API event loop for as long as a slow or unreachable ONVIF camera took to answer (including reconnect attempts), so every other async request stalled with it. Await the future with asyncio.wrap_future instead. The coroutine runs on the controller's own loop and thread, so this cannot deadlock.
* for custom exports, only allow admin users to add to existing cases
follows the existing convention where attaching an export to an existing case is admin-only on `POST /export/{camera}/...` and `POST /exports/batch`
* drop pending edits for a camera or profile that no longer exists
* match cached preview frames to their camera exactly
https://github.com/blakeblackshear/frigate/pull/22594 added a trailing `-` to the `preview_{camera}` prefix so `camera` stopped matching `camera2`'s frames, but camera names can contain `-`, so `front` still matched `front-door`'s. After a restart, `front`'s preview recorder deleted this hour's frames of a matching camera that sorted before it and added the timestamps of one that sorted after it, so ffmpeg was asked for files that don't exist and that hour's preview was lost. The offline fallback for `latest.jpg` could also return `front-door`'s frame for `front`, even to a user without access to `front-door`, and a short export could take its fallback thumbnail from the other camera. These now compare the full camera name taken from the file name.
* migrate web to eslint 10 flat config
ESLint 10 dropped `.eslintrc` support, so `.eslintrc.cjs` is replaced with `eslint.config.js` and the lint scripts no longer pass `--ext` or `--ignore-path`. typescript-eslint moves to 8, react-hooks to 7, and react-refresh to 0.5, and the unused jest and vitest-globals plugins are removed. Lint behaves as it did before: catch variables aren't checked, unused disable directives aren't reported, and rules newly added to the recommended sets are off until the code passes them. typescript-eslint 8 flags constants used only in `typeof`, so those are now exported, or replaced with a union type where the export would trip react-refresh.
* fix lint findings from the eslint 10 recommended rules
Remove the rule overrides from the flat config migration and fix what they were hiding. Unused catch bindings are dropped, 20 disable directives that suppressed nothing are removed (react-hooks 5.2 and 7.1.1 report identical exhaustive-deps findings with inline config ignored), dead initial values are dropped, short-circuit calls become if statements or optional calls, rethrown errors pass `cause`, and the disabled "No recordings" tooltip in `ReviewTimeline` is removed along with its memo and the `getRecordingAvailability` prop. The 3 react-refresh warnings for files that export contexts or classes are left for a later refactor.
* update docusaurus to 3.10.2
* bump @types/node to 25.9.6 and ES2022
target ES2022, which already includes ES2020 and ES2021.String, so the lib list was also trimmed
* bump vite to 8.3.0 and vitest to 4.1.11
Swap `@vitejs/plugin-react-swc` for `@vitejs/plugin-react` and add `esbuild` as a devDependency, since `vite-plugin-monaco-editor` requires it and Vite 8 no longer ships it. `keepNames` moves to `build.rolldownOptions.output` because Vite 8 ignores the `esbuild` block. Rolldown's minifier now writes the preload helper's base path as a template literal instead of a double-quoted string, so the nginx `sub_filter` for `return"/BASE_PATH/"` stopped matching and lazy-loaded chunks and their CSS were requested from a literal `/BASE_PATH/` under Home Assistant ingress. The rule now matches the backtick form.
* bump apexcharts to 7.3.0 and react-apexcharts to 2.1.1
Under Vite 8, a default import from a CommonJS package resolves to its whole `module.exports` when `package.json` has `"type": "module"`, so react-apexcharts 1.4.1 handed React an object and every chart crashed. 2.x ships an ESM build. apexcharts 7 no longer sets `window.ApexCharts`, so the chart components now import it for `ApexCharts.exec`, and `ApexAxisChartSeries` is derived in `types/graph.ts` because it's no longer a global type.
* remove unused immer dep
* remove unused cython pin from tensorrt requirements
The pin was added alongside tensorrt 8.5.3 and cuda-python 11.8, which needed Cython to build, and both have since been dropped from this file. Nothing imports Cython at runtime, and `pip3 wheel` runs with build isolation, so any source build gets its own build dependencies. The pin only installed an unused Cython wheel into the TensorRT image.
* require node 20.19 for docs
types-peewee 4.0 types model fields precisely, so 17 `type: ignore` comments and 2 `cast(str, ...)` calls are no longer needed. Its stubs type `.namedtuples()` and `.dicts()` queries as returning model instances, so the review cleanup reads namedtuple fields by name and the storage usage query casts its dict rows. `start_time` is declared `DateTimeField` but stores unix timestamps, so two reads cast it like `debug_replay.py` already does. `Export` gets an annotation for the `export_case_id` attribute peewee adds at runtime.
* add error boundary
on frontend errors, show a page-level recovery panel and a separate compact strip for the sidebar/status/bottombar so failures there don't take down the rest of the page
* tweaks
* fix test
* Rename face model to face recognizer
* Refactor face detection into own module
* Return face and face landmarks
* Align faces with 5 points instead of just the eyes
* Add landmark validation to throw out images which do not fit a landmark
* Fix circular import and lock face detector for concurrent runs across threads
* Fix mypy
* Support multi resolution exports
* Fix decoder text
* Add dropdown and ability to select export stream selection
* Fix for review comments
* Fix mypy
* Cleanup wording
* rename migration
* use ASC composite index for event camera and start_time
the planner still picks for newest-first single-camera queries but doesn't slow down full per-camera scans on a cold cache
* pause playback while the timeline range handles are up
* reduce multi camera seeded export range to 30m
allows both handlebars to fit within most desktop windows
* Add additional indicies on event and review tables. Every events or timeline endpoint filters on event start time and camera, this should speed things up by avoiding a range scan on the table.
* Rewrite to use a CTE to leverage speedups by using sqllite internal optimization to do a single query instead of a starter query to get distinct labels and a subsequent loop of querys per distinct event labels.
Frigate is currently shipping sqlite 3.46.1, which is above the minimum version 3.25 needed for CTEs.
* Collapse a few sequential queries into a single one.
* Use peewee instead of rw sql for the CTE query.
* Slightly simplify review logic and avoid duplicating the json response for empty review IDs.
* Rerun ruff formatting.
* Remove 2x unnecessary index on reviewsegment, remove reference to prior code implementation in comment in event.py
* Editor fail, re-ruff format.
* Remove the CTE and restore the generator with sub-queries, which is more performance (thanks Nick and Blake for testing against your larger DB!)
* Update peewee index migration description
* Add on_conflict_ignore, replacing the try/catch/pass on IntegrityError
* Add a testcase for validating that on_conflict_ignore bypasses what was formerly an IntegrityError
* Change testcase to clarify that it covers the peewee behavior of on_conflict_ignore
---------
Co-authored-by: Greg <{ID}+{username}@users.noreply.github.com>
* add a notice registry and System Health tab
Problems Frigate detects on its own (ffmpeg crash loops, stuck detectors, failed model downloads, recordings deleted before their retention period) only ever existed as log lines. This adds a `NoticeRegistry` in the main process backed by two tables, an `update_notice` IPC topic so producers in other processes can reach it through the dispatcher, an admin-only API and websocket topic, and a Health tab that lists them. Kinds declare their own mode, severity, and category in one place: state notices are resolved by their producer, event notices are dismissed by the user.
* treat a prerelease as behind its final release
* fix notices clearing early
* rename menu items and update docs
* don't resolve the update notice on a failed version lookup
isLoading and isBuffering only clear on playback progress, and scrubbing holds the player paused, so a source rebuild during a timeline selection left them set with nothing able to clear them. Cancelling made them visible as a spinner over an already-loaded frame, which stayed until the next manual seek. Leaving a scrub now clears them when the source is loaded and the element holds a frame.
* Refactor hardware stats to have consolidated ffmpeg, detector, and enrichments running.
* Cleanup hardware access that is not passed into the container
* Remove network stats from hardware refactor
`after` and `before` defaulted to `datetime.now()` in the function signature, so they were evaluated once at import. Requests that omitted them got a window ending at process start. They now resolve in the handler.
* Support read-only rootfs with self-signed certs in /config/tls
* Support read-only rootfs in s6 and pre-compile bytecode
* Assert read-only rootfs support in CI
* Document hardened read-only deployment
* keep certsync's cert selection identical to nginx's
* note the uid trade-off in user: mode
* fail fast when EXTRA_GROUPS or a missing media volume meets read_only
* keep nosuid and nodev on the /run tmpfs
* support read_only in the default mode
* don't take go2rtc down when the homekit file isn't writable
* lead with the hardware consequence of switching to user:
* refuse to write TLS material through a symlink as root
* note that memryx writes models to the root filesystem
* certsync watches whichever cert path nginx loaded
* Run the frigate service as the frigate user
* Run go2rtc as its own restricted user
* Run nginx as the frigate user with writable state in /tmp/nginx
* Disable bandwidth stats gracefully when not running as root
* Hand TensorRT model cache ownership to the runtime user
* Document non-root operation and per-hardware device access
* Create /media/frigate after the ownership sweep
* Assert non-root services, JWT migration, and escape hatch in CI
* only write the sweep sentinel when a media volume is mounted
* tolerate homekit config chown failures in the go2rtc run script
* chown the s6 log pipe so non-root nginx can reopen /dev/stdout
* set HOME to /config for non-root services
* run smoke nginx -t and the write probe as the runtime user
* re-own the nginx shm cache on service restart
* discard stdout for the unprivileged smoke nginx -t
* unwrap hard-wrapped prose in the installation docs
* report progress during the ownership sweep
* document EXTRA_GROUPS as the only device access path for dropped services
* expand the non-root device access docs with diagnosis steps and udev rules
* document network storage ownership and the remaining detector hardware
* skip lost+found during the ownership sweep
* hand /tmp/cache to the runtime user before services start
* make bundled models readable by the runtime user
* reload nginx by signaling the master instead of parsing its config as root
* harden root writes into unprivileged-owned paths
Restrict the sweep sentinel to a mount at or below /media/frigate so a
parent /media mount cannot bless a later-shadowed volume. Rebuild
/tmp/nginx root-owned each start so root's cp and tempio writes cannot
follow a symlink an unprivileged nginx planted in the previous run.
* collapse the duplicated sentinel comment
* add a service-runs-as-root helper for granular root services
* validate FRIGATE_ROOT_SERVICES and fail fast on unknown names
* let services listed in FRIGATE_ROOT_SERVICES skip the privilege drop
* record the root-services mode in the sentinel and sweep small trees each boot
* cache the runtime ids in the ownership helper
* chown recordings, previews, and exports to the runtime user at create
* chown the database files after init
* recommend FRIGATE_ROOT_SERVICES in the bandwidth stats warning
* assert granular root services in CI
* document FRIGATE_ROOT_SERVICES
* own every directory level created for a recording segment
* clear the cached runtime ids when ownership tests finish
* skip missing media paths in the per-boot ownership sweep
* clarify granular root services docs
* clean up
* install acl for device access grants
* grant runtime users access to mapped device nodes at boot
* assert device access grants in CI
* document automatic device access grants
* stop telling users device access needs host side setup
* clarify the non-root docs
* link the migration script to the repo
* group the manual device setup under one section
* harden against symlink attacks
/config is owned by the unprivileged runtime user after the ownership sweep, so root operations on files there could be redirected by a planted symlink.
- go2rtc HomeKit setup: replace the root yq/jq normalization and chown with an O_NOFOLLOW helper (prepare_homekit.py), so a symlink at go2rtc_homekit.yml can't redirect a root write or chown onto another file
- go2rtc binary override: ignore /config/go2rtc whenever the service runs as root, so a planted binary can't exec as root under FRIGATE_ROOT_SERVICES
- sweep sentinel: read and write it through safe-sentinel, which trusts only a root-owned regular file and never follows a symlink, so it can't be forged to skip the migration or symlinked to clobber a root file
- ownership sweep: chown with -execdir so a parent directory swapped for a symlink mid-walk can't redirect the chown out of the volume
- validate inputs: restrict DEVICE_ACL_PATHS to /dev, require nonzero numeric EXTRA_GROUPS, and reject PUID/PGID that collide with the go2rtc ids
- docs: correct the TLS key ownership note to match what actually happens
* tweak docs
* stop the ownership sweep chasing entries other mechanisms own
* keep custom binaries out of root services only under granular root
The `v1.x` line is the same muxed fMP4 code as Kaltura's 1.31 with fixes backported, so the mapping JSON, manifest routes, and ffmpeg consumers are unchanged. The `MAX_CLIPS` patch applies as-is and the HEVC workaround is rewritten for the fork's reformatted source.
`vod_hls_version 6` is now explicit because the fork replaced Kaltura's automatic version calculation with a directive that defaults to 4 and only warns when fmp4 needs 6, so playlists were being stamped `EXT-X-VERSION:4` while carrying `EXT-X-MAP`. The `error_page 502 =404` hack is gone: https://github.com/kaltura/nginx-vod-module/issues/468 is a `vod_mode remote` bug and we're `mapped`, so those 502s were really `_vod_response` returning 404 upstream. The fork maps that through now, and the hack was also turning real 5xx into "no recordings".
* use the camera's model for the lpr vehicle check
`FrigateConfig.model` became `models[]` in the detector refactor, so this didn't compile on 0.19. Each camera has its own detector/model pair now, so the check resolves the camera's scene with `getModelForCamera` instead of looking at every model.
* use camera config model
* fix export test
* add onboarding wizard for new users
* resolve hwaccel per camera and clarify recording retention
The hwaccel step listed every preset Frigate ships, so an Intel box was offered Raspberry Pi and Rockchip decoding, and the codec specific presets (`preset-intel-qsv-h264` vs `-h265`) were offered as global values that break as soon as two cameras use different codecs. `/hardware/hwaccel` now returns the decoding families the probed hardware can actually use, each carrying a preset per codec, and the wizard resolves the family against the detect stream codec the camera wizard already probed: one global `ffmpeg.hwaccel_args` when every camera agrees, per-camera `cameras.<name>.ffmpeg.hwaccel_args` when they don't. The global stays on `auto` in that case so cameras added later still resolve at startup. A gen13+ Intel machine keeps its QuickSync recommendation with mixed h264 and h265 cameras instead of dropping to vaapi.
The recording step's "Days to retain recordings" only wrote alert and detection retention, and the storage estimate under it assumed continuous recording. It now asks what to record in plain language, writes `record.continuous.days` to match, shows the estimate only for continuous, and drops the spinner arrows on the number input.
* clean up
* add light/dark mode icon switcher
* use yml as default config file extension when not found
* i18n tweaks
* gate the setup wizard on cameras instead of a config key
* render setup wizard steps by key
* share the setup wizard e2e helpers and mock users
* add an account step to the setup wizard
* add setup wizard account step e2e coverage
* cover the account step's restart behavior
* button consistency
* fix test
* docs
* fixes
* fix inconsistent export download filenames
Zip entries in a case download were named from `Export.name`, the friendly display name, while an individual download uses the file name on disk. The two have always been formatted differently, so one export came out as `front_door_20260823_020615-20260823_020734_abc123.mp4` on its own and `front door 2026-08-23 020615 2026-08-23 020734.mp4` inside a zip. Zip entries now use the on-disk file name, and renaming an export renames its file, so there's only one name to download under. The rename is blocked while ffmpeg still holds the file.
* cap filename length and catch duplicate names
* fix export rename and stop blocking the event loop
* move the rename rollback off the event loop
* no awaits
* pin genai review frames to the main stream
* retain previews as long as either stream has recordings
* watch sub stream recording health separately from main
* reject record_sub on the same input as record and document the role
* derive recording paths from the cache segment timestamp
Recording paths carry one second of resolution, but since sub stream recording start times are resolved to fractional wall clock, anchored to the cache file mtime and chained to the previous segment's end. A stream cutting segments faster than once a second resolves consecutive segments into the same second, so two rows collide on the unique path index and the batch insert fails. The cache segment name is unique per camera stream and second by construction because ffmpeg names segments with strftime, so the recording path is now built from that timestamp while the row keeps the resolved start time. This also restores the path semantics from before sub stream recording, when start times came straight from the cache filename.
Nothing derives times from recording paths: playback offsets, stream switching, and export all use the row's start time, which is unchanged, and the recordings sync matches files by exact path string.
* keep the rest of a recording batch when one row conflicts
* only publish record_sub status when a sub stream is configured
* don't shadow camera_cfg when publishing empty cache streams
* back off restarts when a recording stream goes stale
* give the shared sub stream grace on any capture thread reset
* include segment details in recording discard warnings
* Create frigate and go2rtc runtime users in the image
* Add single fix-ownership helper for volume permission migration
* Add init-usermod oneshot for PUID and PGID remapping
* Chown newly created runtime directories to the frigate user
* Run sentinel-guarded ownership sweep during prepare
* Add host-side volume permission migration script
* Guard log directory ownership for user-mode startup
* Fall back to plain s6-log when running without root
* Assert PUID remapping and sweep sentinel in CI smoke test
* Skip the ownership sweep in the devcontainer
* Pin FRIGATE_RUN_AS_ROOT in ownership tests
* Do not record the sweep as complete when a chown failed
* Validate PUID and PGID in the migration script
* Treat a failed ownership scan as an incomplete sweep
* Reject PUID and PGID of 0 during remapping
* Handle symlinks, dry runs, and sentinel write failures in the sweep
* Treat an absent sweep root as an incomplete sweep
* Verify s6-overlay downloads against pinned checksums
* Verify go2rtc download against pinned checksums
The v1.9.14 release publishes no checksums file, just the bare per-platform binaries, so these digests come from a one-time fetch rather than upstream. That pins the artifact against later substitution, which is the realistic threat for a version we stay on for months, but it does not verify the original download. The stage moves from `ADD --link` to a script because `ADD --checksum` can't express an architecture-dependent URL.
* Verify main image downloads against pinned checksums
Covers everything the main image downloads on the default path: tempio, the hailort runtime tarball and wheel, the six ffmpeg builds, the libedgetpu deb, and the thirteen Intel driver debs. The hailort tarball was streamed straight into `tar`, which can't be verified before extraction, so it downloads to `/tmp` first. The three ffmpeg blocks per arch collapse into one `install_ffmpeg` helper since they only differed by URL and install dir, and the Intel debs go through a `fetch_intel_deb` helper for the same reason.
The Intel debs are the ones that mattered most here. They're installed as root with `dpkg` on the default amd64 path and had no verification at all. compute-runtime publishes a `ww<week>.sum` asset with every release and npu-driver published `checksum.sha256` on v1.19.0, so those eight digests came from upstream rather than from us. intel-graphics-compiler and level-zero publish none, so those five and everything else here come from a one-time fetch, which pins the artifact against later substitution but doesn't verify the original download. The comment above the map says which is which and how to refresh them, since npu-driver has stopped publishing sums since v1.19.0 and that provenance won't survive the next bump.
Still unpinned: `get-pip.py`, which is a rolling URL where a digest would just break the build on pypa's next edit, and the per-variant artifacts for Axera, Synaptics, and Jetson. apt repositories are out of scope since apt already verifies signatures.
* Restrict generated TLS key permissions
OpenSSL 3.x already writes the key at 600 on its own, so this pins the guarantee rather than fixing an observed leak: the mode no longer depends on the openssl version or the umask the service happens to start with. Only the generated pair is touched. User-mounted certs take the other branch and are never chmod'd, which matters when they're mounted read-only.
* Add security headers and server_tokens off
Adds `X-Content-Type-Options: nosniff` and `Referrer-Policy: strict-origin-when-cross-origin`, and turns off nginx version disclosure.
No `X-Frame-Options` and no CSP `frame-ancestors`. HA's Webpage card and iframe panels frame Frigate's own address cross-origin, and either header would break them silently with nothing in Frigate's logs to explain it. Ingress is same-origin and would survive `SAMEORIGIN`, but Frigate can't tell the two apart from inside the container. `security_headers.conf` is a plain file in the image rather than a generated one, so anyone who does want framing restrictions can bind-mount it.
`add_header` doesn't inherit into a block that declares its own, so the include goes in per block, all nine of them, including the four nested static-asset locations that serve the JS bundles. Those are the ones nosniff actually matters for.
The run script now reads `get_nginx_settings.py` once into a variable instead of shelling out per template. That script imports the frigate config machinery, which is noticeable on an SBC.
Not fixed here: `listen.conf` is included at server level and carries `Strict-Transport-Security`, so those same nine blocks already drop HSTS under TLS today. Folding it into this file would change existing TLS behavior on nine paths, so it needs its own PR.
* Restrict go2rtc config file permissions
* Log failed login attempts with source address
Failed logins returned a bare 401 and left nothing behind, so credential stuffing was invisible unless you were already watching nginx access logs. Both failure branches now log a warning with the attempted username and the client address.
The address comes from `get_remote_addr()`, the same helper the login rate limiter keys on, so the two agree on who the client is and the trusted-proxy handling is consistent. Logging a raw `x-forwarded-for` instead would let an attacker forge the source address in the very log line meant to catch them.
The response is unchanged and identical either way. Which factor failed is only visible in the log, never to the client, and the password is never logged.
* Recommend least-privilege container options in install docs
The compose generator pushed `privileged: true` into every file it produced, no matter what hardware you picked, and it's the default tab on the install page so it's what most people copy. It now emits `security_opt: no-new-privileges:true` instead, and only adds `privileged: true` for hardware that actually needs it, with the reason inline. MemryX is the only one today, since it needs to reach the max-manager. Rockchip and Synaptics only want privileged during initial setup and their documented end state is device mappings, so neither gets it.
`no-new-privileges` merges into the same `security_opt` block as any device-specific entries, so Rockchip still gets its `apparmor=unconfined` and `systempaths=unconfined` without a duplicate key.
The static example now has `privileged` commented out, and there's a short section on the options worth adding, with a note that `cap_drop: ALL` breaks `telemetry.stats.network_bandwidth` since nethogs needs NET_ADMIN/NET_RAW.
* Add amd64 container smoke test to CI
Boots the built amd64 image against a minimal config and asserts the two security headers, that the Server header no longer carries a version, that no frame-ancestors is present, that nginx accepts its own config, and the two file modes. This is also the harness the rest of the hardening work extends.
The two negative assertions are written as `if grep; then exit 1; fi` rather than `! grep`. Bash exempts a negated command from `set -e`, so the `!` form would have passed even with the version and frame-ancestors both present, which is the opposite of what a regression net is for.
* improve keyframes messages
* don't pad the labelmap with unknown
`load_labels()` prefilled 91 `unknown` entries before reading the label file, so any model with fewer than 91 classes kept that padding in `merged_labelmap` and `unknown` showed up as a selectable object type in the objects settings UI. The padding only existed so `RemoteObjectDetector.detect` could index the labelmap without a KeyError, and it didn't even cover the empty-file case or Frigate+, which never had a prefill. Both lookups now skip class ids the labelmap doesn't name and warn once per id.
* add secrets.yaml and merge substitution sources by precedence
FRIGATE_ENV_VARS was built once at import from container env and /run/secrets, and the environment_vars validator overwrote it unconditionally, so the block beat the deployment and nothing could be re-read. Sources are now separate dicts merged lowest to highest (environment_vars, secrets.yaml, container env, credentials directory), re-read at the top of every parse, and a collision warns once naming the winner. An undefined {FRIGATE_*} raises a ValueError subclass so pydantic reports the field instead of a KeyError traceback.
* use the shared substitution namespace in go2rtc config
The generator rebuilt the namespace itself from os.environ and a hardcoded /run/secrets, so it never saw environment_vars or CREDENTIALS_DIRECTORY, and str.format made any stray brace fatal. It now installs the FRIGATE_ names from environment_vars and substitutes streams the same way every other field does.
* read the exec override from an import time snapshot
environment_vars is exported into os.environ, and is_go2rtc_arbitrary_exec_allowed read os.environ live, so the config file could enable exec sources. Snapshot the variable at import, which runs before any config is loaded.
* docs
* clarify docs
ffmpeg's stderr was piped but never read, so recording segments that generate more than 64 KB of ffmpeg warnings blocked ffmpeg mid-write, stranding the streaming thread and its anyio threadpool token for good. Enough of those and every sync endpoint stops responding until restart. The trigger is how noisy the segments are, not how long the clip is.
Send stderr to a temp file instead, and guarantee ffmpeg teardown and playlist cleanup on every exit path, including client disconnect.
Also fixes two bugs the deadlock hid: the failure branch was unreachable because returncode is None mid-loop, so the playlist file leaked and ffmpeg's logs were never reported. Playlist files now get a unique name so concurrent requests for one range cannot delete each other's input.
Extracts the terminate helper motion search already had into frigate/util/ffmpeg.py, now shared by both streaming call sites.
* add import/export for camera group layouts and streaming settings
Camera group layouts and per-camera streaming settings are stored in the browser's IndexedDB, so they are tied to a single browser on a single device. Users with more than one device have to rebuild every group layout and re-pick every camera's stream settings by hand, and clearing browser data loses the work.
Add a Backup & Restore card to Settings > UI Settings that exports these settings to a JSON file and imports that file on another device. Import shows a confirmation dialog with per-section counts, switches for layouts, streaming settings, and UI preferences, and warnings about camera groups or cameras in the file that are not on this server.
Server-side storage is deliberately avoided. These are per-device presentation settings: a layout arranged for a desktop is wrong on a tablet, and continuous full-resolution streams that are free on a wired LAN are not on a phone. An explicit file moves settings only when the user chooses to move them.
Implementation notes:
- web/src/utils/uiSettingsTransfer.ts owns a registry of transferable IndexedDB keys. Each entry records whether the key is user-namespaced, matching which persistence hook wrote it, plus a zod schema for its value.
- Only registry-known keys are ever written, and only when their value passes that schema. The file format deliberately lets unknown keys survive parsing, so this filter is what prevents a hand-edited file from writing arbitrary storage keys or out-of-range values.
- Export falls back to the legacy un-namespaced key, because the username migration runs lazily on first mount of each owning hook.
- Streaming settings merge per group rather than replacing the whole map, so groups configured only on the receiving device survive.
- Import writes storage and then reloads, because useUserPersistence reads a key only on mount and StreamingSettingsProvider would otherwise write its stale in-memory state back over the import.
- playbackBandwidthEstimate, frigate-search-history, and live-layout are excluded: the first two are measurements and user data rather than preferences, and live-layout's default is derived from the device.
* merge imported streaming settings per camera instead of per group
Allow audio classes to be grouped under a shared configured label.
Keep audio overrides separate from object labels and retain only the highest-scoring grouped detection.
Refs #23967
* refactor mqtt so that Frigate owns the transport lifecycle instead of delegating it to paho
* release the shutdown barrier on worker crash and replay retained publishes the broker never acked
* collapse in-flight retained values by topic and release the shutdown barrier from a finally
* replay the outage buffer before the publish queue so newer values are not reverted
* serve a segment startup ladder so seeks begin playing sooner
nginx-vod was handed one 10s segment per recording file, so every playlist start had to download and decode a full segment before the first frame. Declare real keyframe data per clip and let nginx cut short leading segments from it.
- add vod_bootstrap_segment_durations 1000/2000/4000 so each playlist starts with 1s/2s/4s segments before settling at 10s
- emit real clip-relative keyFrameDurations (plus firstKeyFrameOffset when nonzero) from the recording keyframe index; rows without an index keep the whole-clip declaration, the only safe cut without keyframe knowledge
- drop the manifest's segment_duration field, which was always inert: nginx-vod parses only camelCase segmentDuration
- rebuild the player source at the seek target, quantized to a 10s grid, so the ladder applies to every seek and seek URLs stay repeatable for nginx's mapping and response caches
- route the seek model, in-range checks, and the stale-report guard through the source window rather than the chunk range
- bridge repositioning seeks (>2s from the last played timestamp) through the preview player and hold the release anchor one commit, so neither path paints a stale frame
- clear a pending loading timer before replacing it; an orphaned timer escaped onPlaying's clearTimeout and flashed loading mid-playback
* keep recordings queries on their indexes
Several recordings queries degraded into full scans or large sorts on big databases: the planner ignored index order, or the query shape gave it nothing tight to seek on. Reshape them into bounded seeks and add the composite index the per-stream lookups need.
- index recordings on (camera, stream_type, start_time DESC) and drop the (camera, stream_type) index it supersedes
- walk the recordings summary day by day with EXISTS probes and per-camera MIN/MAX seeks, skipping ahead over empty gaps instead of bucketing every row for the requested cameras
- run the summary endpoint on the event loop rather than the threadpool
- bound the unavailable-recordings query by start_time per camera and merge the results in Python
- bound the expire query's start_time so it seeks the retention window instead of scanning a camera's whole history
- enumerate deleted cameras with one index seek each rather than a camera NOT IN (...) scan
- compute bandwidth with segment_size filtered in a CASE projection; as a WHERE predicate it baited the planner into the (camera, segment_size) index plus a full sort of the camera's history
- fall back to a 1000-segment window when the recent 100 are all zero-size, so an ingest glitch doesn't report zero bandwidth
- limit the needs_refresh count instead of counting every segment
- cover sub-only and sparse calendar days, midnight-spanning day attribution, multi-camera gap merging, deleted-camera expiry, and zero-size segment runs
* fix mypy
* add ptz controls to camera via wizard when onvif has already been probed
* i18n
* add e2e test
* backend add and remove subscriber
* tweaks
* turn on switch by default if pan and/or tilt capability is available
* fix test
* add sub stream recording with adaptive quality playback
Optionally record a second, lower bitrate stream alongside the main
recording stream via a `record_sub` input role and `record.sub` config block, with its own retention windows.
Recordings rows now carry the stream type plus the media details needed to serve both streams from one manifest: video codec, audio presence, audio codec and rate, and a record-time keyframe index.
Playback resolves coverage across both streams and merges them into a single VOD sequence, falling back to a discontinuity manifest with per-clip init segments when the media signatures differ. The player exposes a quality selector, and an auto governor picks the stream from stall time, bandwidth, codec support, and the save-data hint.
* fix tests and i18n
* Guard object processor queue handlers against unknown cameras
* Skip embeddings post processing for removed cameras
* End review segments for removed cameras
* Drop queued autotracker moves for removed cameras
* Release tracked event thumbnails when skipping a removed camera
* Add locked accessors for camera states
* Read camera states through the processor accessors
* Guard output and recording paths against cameras not yet known
* Resolve camera state once in ONVIF, notification, and transcription paths
* Add combined motion and object Birdseye mode
Add a motion_objects mode that keeps Birdseye active when motion is detected or a confirmed tracked object is present, including stationary objects.
Wire the mode through configuration, runtime commands, API schemas, documentation, and UI labels. Exclude false-positive trackers and add regression coverage for Birdseye activation and MQTT validation.
* Refactor Birdseye activity types as booleans
Replace combination-specific Birdseye modes with composable boolean activity types for motion, active objects, stationary objects, and continuous display.
Preserve legacy single-mode configuration and MQTT inputs, support canonical comma-separated MQTT combinations, and allow scalar YAML values to be replaced by nested settings through the config API.
* Preserve OpenVINO config translations
Regenerate the configuration translations with the OpenVINO detector schema available so the unrelated production detector labels remain intact.
* Preserve partial Birdseye mode overrides
Allow an empty activity selection with a canonical NONE MQTT state so partial camera and profile overrides can disable inherited flags without failing validation.
Add regression coverage for camera and profile inheritance, document the NONE contract, and keep the generated schema fixture scoped to Birdseye.
* Address Birdseye activity review feedback
Move scalar mode compatibility into the 0.18-1 config migration and reject empty activity selections instead of publishing a NONE state.
Pass activity signals through a frozen dataclass, preserve existing active-object tracker behavior, and require confirmed stationary objects. Revert the generic YAML mutation and cover migration, inheritance, MQTT, and activation regressions.
* Move Birdseye migration to 0.19
Use the 0.19-0 configuration revision for converting scalar Birdseye modes to composable activity flags, and update the migration regression coverage accordingly.
* Remove Birdseye migration test
Drop the dedicated config migration test as requested during review while retaining the 0.19-0 migration implementation.
* Docs: fix Synaptics default model path in object detector docs
The model is installed at /synaptics/mobilenet.synap by docker/synaptics/Dockerfile,
matching the config examples in the same section.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* Docs: warn about kernel Oops with v4l2m2m hwaccel on ASUS GT-BE19000AI
Enabling the recommended h264_v4l2m2m hwaccel args on the GT-BE19000AI AI
board (SL1680, firmware kernel 5.15.140) triggers a LIST_POISON dereference
Oops in the vendor vpu driver during decoder teardown, requiring a reboot.
Observed and captured on real hardware. Also fixes a Synaptics typo.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* Docs: drop GT-BE19000AI hwaccel warning per review
Remove the device-specific v4l2m2m kernel Oops warning as requested by
maintainer review; upstream/vendor gotchas are not documented here. The
Synaptics typo fix and model path correction remain.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
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Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: windasd <me@windasd.tw>
Co-authored-by: 蘭蘭露 <flandrescarlettw@gmail.com>
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Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/audio
Translation: Frigate NVR/common
Translation: Frigate NVR/components-auth
Translation: Frigate NVR/components-camera
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/components-player
Translation: Frigate NVR/objects
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Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-motionSearch
Translation: Frigate NVR/views-replay
Translation: Frigate NVR/views-search
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
* fix jumping timeline handles in debug replay range selection
The timeline mirrored the export range into local state that was never reset when the range cleared, so starting a second selection after cancelling one left the stale mirror disagreeing with the new range. The sync effect and the draggable position effect then rewrote each other's values every commit until React hit its update limit. The handles now write the range directly.
* add mobile test
* fix incorrect backchannel docs
`#backchannel=0` is a native rtsp source param, not an ffmpeg one, and the troubleshooting warning had it listed as ffmpeg-only. Any `#` modifier on a bare `rtsp://` source turns the backchannel off unless the URL explicitly contains `#backchannel=1`, so a dedicated two-way talk stream can't carry `#video=h264` or anything else.
* add rotation faq
* run lpr on more vehicle types by default
before, a config change to attribute_map was required
* logging tweaks
* remove arg
* use attributes for frontend check
* docs
* only check thumbnail attributes the object can have
send_alert()'s short circuit for skipping a no-op "update" push only
compared object and zone counts between before/after, never severity.
Both underlying collections are cumulative and deduplicated (objects
is a set of labels, zones only appends a zone not already present),
so a segment being promoted from detection to alert can leave both
counts unchanged, silently dropping the single most notable
transition in a review's life. Add a severity comparison to the same
check so a detection -> alert promotion always notifies.
The CUDA execution provider returns an identical vector for every image when jina-clip-v2 is built below ORT_ENABLE_EXTENDED, so every thumbnail embedding written on a GPU was the same normalized garbage and semantic search returned the same results for any query. Reproduced on two different NVIDIA cards, across onnxruntime 1.22 and 1.24, and on both the 0.17 and 0.18 CUDA stacks, so it isn't specific to any of those. ORT_ENABLE_ALL isn't an option because it fails to build on CPU with a SimplifiedLayerNormFusion error, leaving EXTENDED as the only level that works on both providers. jinav1 is unaffected and stays on BASIC.
description:Visible on the System Metrics page in the Web UI. Please include the full version including the build identifier (eg. 0.18.0-beta1, 0.18.0-8b72c7a, etc.)
placeholder:"0.18.0-beta1"
description:Visible on the System Metrics page in the Web UI. Please include the full version including the build identifier (eg. 0.19.0-beta1, 0.19.0-8b72c7a, etc.)
Use this form to submit a reproducible bug in Frigate or Frigate's UI.
**⚠️ If you are running a beta version (0.18.0-beta or similar), please use the [Beta Support template](https://github.com/blakeblackshear/frigate/discussions/new?category=beta-support) instead.**
If you are running on Proxmox, please see the [Proxmox FAQ](https://github.com/blakeblackshear/frigate/discussions/23916) and reproduce the issue on a standard Docker install first (bare metal, or a VM running plain Debian/Ubuntu) before submitting here.
**⚠️ If you are running a beta version (0.19.0-beta or similar), please use the [Beta Support template](https://github.com/blakeblackshear/frigate/discussions/new?category=beta-support) instead.**
Before submitting your bug report, please ask the AI with the "Ask AI" button on the [official documentation site][ai] about your issue, [search the discussions][discussions], look at recent open and closed [pull requests][prs], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your bug has already been fixed by the developers or reported by the community.
echo "[WARNING] Failed to convert cleaned config to YAML, creating minimal config"
: > "${config_path}"
}
fi
# Clean up temp files
rm -f "${temp_json}"
}
set_libva_version
if [[ -f "/dev/shm/go2rtc.yaml" ]]; then
@@ -106,13 +84,23 @@ else
echo "[WARNING] Unable to remove existing go2rtc config. Changes made to your frigate config file may not be recognized. Please remove the /dev/shm/go2rtc.yaml from your docker host manually."
fi
# HomeKit configuration persistence setup
# HomeKit persistence. The helper is symlink-safe; hand off to go2rtc only when dropping.
# the sweep hands /config to uid 1000, so a root service must not exec from it
if [[ "$granular_root" -eq 1 && -x "${config_path}/go2rtc" ]]; then
echo "[WARN] Ignoring '${config_path}/go2rtc' because FRIGATE_ROOT_SERVICES runs this service as root and /config is owned by the runtime user; using the embedded binary"
echo "[WARN] Use FRIGATE_RUN_AS_ROOT=true instead if you need both a custom go2rtc build and root"
# Required: Label name modifications. These are merged into the standard labelmap.
labelmap:
2:vehicle
# Optional: Map of object labels to their attribute labels (default: depends on model)
attributes_map:
person:
- amazon
- face
car:
- amazon
- fedex
- license_plate
- ups
# Optional: Audio Events Configuration
# NOTE: Can be overridden at the camera level
@@ -217,6 +218,8 @@ audio:
- fire_alarm
- speech
- yell
# Optional: Audio label name modifications. These are merged into the standard audio labelmap.
labelmap:{}
# Optional: Filters to configure detection.
filters:
# Label that matches label in listen config.
@@ -251,11 +254,15 @@ birdseye:
# Optional: Encoding quality of the mpeg1 feed (default: shown below)
# 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
quality:8
# Optional: Mode of the view. Available options are: objects, motion, and continuous
# objects - cameras are included if they have had a tracked object within the last 30 seconds
# motion - cameras are included if motion was detected in the last 30 seconds
# continuous - all cameras are included always
mode:objects
# Optional: Activity types that include cameras in Birdseye (default: shown below)
# Multiple activity types can be listed at the same time.
# continuous: all cameras are included always
# motion: included if motion was detected within the inactivity threshold
# all_objects: included if a tracked object was present within the inactivity threshold
# alerts: included while an alert review item is in progress
# detections: included while a detection review item is in progress
modes:
- all_objects
# Optional: Threshold for camera activity to stop showing camera (default: shown below)
inactivity_threshold:30
# Optional: Configure the birdseye layout
@@ -287,6 +294,8 @@ ffmpeg:
detect:-threads 2 -f rawvideo -pix_fmt yuv420p
# Optional: output args for record streams (default: shown below)
record:preset-record-generic
# Optional: output args for sub stream record streams (default: the record output args above)
# record_sub: preset-record-generic
# Optional: Time in seconds to wait before ffmpeg retries connecting to the camera. (default: shown below)
# If set too low, frigate will retry a connection to the camera's stream too frequently, using up the limited streams some cameras can allow at once
# If set too high, then if a ffmpeg crash or camera stream timeout occurs, you could potentially lose up to a maximum of retry_interval second(s) of footage
@@ -306,6 +315,10 @@ detect:
width:1280
# Optional: height of the frame for the input with the detect role (default: use native stream resolution)
height:720
# Optional: the environment this camera looks at, which picks the model it runs on
# (default: the model with a scene of all)
# Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
scene:outdoor
# Optional: desired fps for your camera for the input with the detect role (default: shown below)
# NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
fps:5
@@ -483,6 +496,11 @@ review:
- Animals in the garden
# Optional: Preferred response language (default: English)
This section can be used to set environment variables for those unable to modify the environment of the container, like within Home Assistant OS. Docker users should set environment variables in their `docker run` command (`-e FRIGATE_MQTT_PASSWORD=secret`) or `docker-compose.yml` file (`environment:` section) instead. Note that values set here are stored in plain text in your config file, so if the goal is to keep credentials out of your configuration, use Docker environment variables or Docker secrets instead.
This section sets environment variables in the Frigate process for those unable to modify the environment of the container, like within Home Assistant OS. It's meant for process settings such as `LIBVA_DRIVER_NAME` or the TensorFlow thread counts below. Docker users should set environment variables in their `docker run` command (`-e LIBVA_DRIVER_NAME=i965`) or `docker-compose.yml` file (`environment:` section) instead. Values set here are stored in plain text in your config file, so credentials belong in `secrets.yaml`, Docker environment variables, or Docker secrets instead.
Variables prefixed with `FRIGATE_`can be referenced in config fields that support environment variable substitution (such as MQTT host and credentials, camera stream URLs, and ONVIF host and credentials) using the `{FRIGATE_VARIABLE_NAME}` syntax.
:::note
The `go2rtc` section is an exception. go2rtc runs as a separate process, so its stream definitions can only be substituted with variables that exist in the container's environment (set via Docker `-e`, the `environment:` section of `docker-compose.yml`, or Docker secrets). Variables defined in the `environment_vars` block above are not available to go2rtc streams. Home Assistant app users, who cannot set container environment variables, must instead put credentials directly in their go2rtc stream URLs.
:::
Names prefixed with `FRIGATE_`set here also take part in `{FRIGATE_VARIABLE_NAME}` substitution (see [below](#substitution-sources-and-precedence)), but `secrets.yaml` is the better home for them.
<ConfigTabs>
<TabItem value="ui">
@@ -80,23 +74,17 @@ Navigate to <NavPath path="Settings > System > Environment variables" /> to add
| **Variable name** | The environment variable name (e.g., `FRIGATE_MQTT_USER`) |
| **Variable name** | The environment variable name (e.g., `LIBVA_DRIVER_NAME`) |
| **Value** | The value for the variable |
Variables defined here can be referenced elsewhere in your configuration using the `{FRIGATE_VARIABLE_NAME}` syntax.
Names prefixed with `FRIGATE_` can also be referenced elsewhere in your configuration using the `{FRIGATE_VARIABLE_NAME}` syntax.
</TabItem>
<TabItem value="yaml">
```yaml
environment_vars:
FRIGATE_MQTT_USER:my_mqtt_user
FRIGATE_MQTT_PASSWORD:my_mqtt_password
mqtt:
host:"{FRIGATE_MQTT_HOST}"
user:"{FRIGATE_MQTT_USER}"
password:"{FRIGATE_MQTT_PASSWORD}"
LIBVA_DRIVER_NAME:i965
```
</TabItem>
@@ -130,6 +118,51 @@ environment_vars:
</TabItem>
</ConfigTabs>
### `secrets.yaml`
A `secrets.yaml` file next to your `config.yml` is an additional source of `FRIGATE_` variables, for installs that can't set container environment variables or mount Docker secrets. It's a flat map of names to values, and it is never read or written by the Frigate UI:
```yaml
FRIGATE_CAM_USER:viewer
FRIGATE_CAM_PASS:"p@ss w0rd"
FRIGATE_MQTT_HOST:mqtt.internal.example
```
For Docker this is `/config/secrets.yaml` inside the container, so it lives in whatever host directory you mounted at `/config`. For the Home Assistant App it's `/addon_configs/<addon_directory>/secrets.yaml`, in the same folder as your `config.yml`; see [the App config directory](../config.md#accessing-app-config-dir) for the directory name for your variant.
Names must start with `FRIGATE_`, and nesting is not supported. `secrets.yaml` feeds `{FRIGATE_VARIABLE_NAME}` substitution, so the handful of variables Frigate reads straight from the process environment, such as `FRIGATE_JWT_SECRET`, still need a container environment variable or a Docker secret.
### Substitution sources and precedence
The same `{FRIGATE_VARIABLE_NAME}` placeholder resolves from four sources. When a name is defined in more than one, the higher one wins and a warning at startup names which source was used.
| Priority | Source | Where it's set | Who can use it |
| 1 (highest) | Docker secrets | Files in `/run/secrets`, or the directory named by `CREDENTIALS_DIRECTORY` | Docker, systemd |
| 2 | Container environment | `docker run -e`, the `environment:` section of `docker-compose.yml` | Docker |
| 3 | `secrets.yaml` | Next to `config.yml`, see above | Everyone, including the HA App |
| 4 (lowest) | `environment_vars` | The block in `config.yml` described above | Everyone, including the HA App |
For example, with this `secrets.yaml`:
```yaml
FRIGATE_MQTT_PASSWORD:from_secrets
```
and this `config.yml`:
```yaml
environment_vars:
FRIGATE_MQTT_PASSWORD:from_config
mqtt:
password:"{FRIGATE_MQTT_PASSWORD}"
```
the password resolves to `from_secrets`, and the log shows `FRIGATE_MQTT_PASSWORD is defined in more than one place, using the value from secrets.yaml`. Add `-e FRIGATE_MQTT_PASSWORD=from_env` to the container and it resolves to `from_env` instead.
Referencing a name that no source defines is a config validation error naming the field.
### `database`
Tracked object and recording information is managed in a sqlite database at `/config/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
@@ -177,7 +210,7 @@ Custom models may also require different input tensor formats. The colorspace co
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Detectors and model" /> and open the **Custom Model** tab to configure the model path, dimensions, and input format.
Navigate to <NavPath path="Settings > System > Detection models" /> and, on the model you want to change, open the **Custom Model** tab to configure the model path, dimensions, and input format.
@@ -192,12 +225,14 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and open
```yaml
# Optional: model config
model:
path:/path/to/model
width:320
height:320
input_tensor:"nhwc"
input_pixel_format:"bgr"
models:
- devices:
- openvino:GPU
path:/path/to/model
width:320
height:320
input_tensor:"nhwc"
input_pixel_format:"bgr"
```
</TabItem>
@@ -214,15 +249,15 @@ If the labelmap is customized then the labels used for alerts will need to be ad
The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. By default, truck is renamed to car because they are often confused. You cannot add new object types, but you can change the names of existing objects in the model.
```yaml
model:
labelmap:
2:vehicle
3:vehicle
5:vehicle
7:vehicle
15:animal
16:animal
17:animal
models:
- labelmap:
2:vehicle
3:vehicle
5:vehicle
7:vehicle
15:animal
16:animal
17:animal
```
Note that if you rename objects in the labelmap, you will also need to update your `objects -> track` list as well.
@@ -362,6 +397,10 @@ To do this:
2. Update the `ffmpeg.path` in your Frigate config to `/config/custom-ffmpeg`.
3. Restart Frigate and the custom version will be used if the steps above were done correctly.
Both binaries have to be executable by Frigate's unprivileged runtime user, so `chmod 755` them after extracting. The startup ownership sweep runs only once, so anything you add to `/config` later keeps whatever ownership and mode you gave it.
There is one exception, and it only affects [`FRIGATE_ROOT_SERVICES`](/configuration/non_root#keeping-individual-services-root) listing `frigate`. That mode runs Frigate as root while still handing `/config` to the unprivileged runtime user, so anything running as that user could swap the binary and gain root. A build inside any of Frigate's writable volumes (`/config`, `/media/frigate`, the cache and shm dirs) is ignored there and the bundled one is used, with a warning in the log. Keep the build somewhere root-owned (any absolute `ffmpeg.path` works, so a read-only bind mount such as `/opt/custom-ffmpeg` is enough) if you need both. The default mode and `FRIGATE_RUN_AS_ROOT=true` are unaffected and behave exactly as they always have.
### Custom go2rtc version
Frigate currently includes go2rtc v1.9.14, there may be certain cases where you want to run a different version of go2rtc.
@@ -370,9 +409,11 @@ To do this:
1. Download the go2rtc build to the `/config` folder.
2. Rename the build to `go2rtc`.
3. Give `go2rtc` execute permission.
3. Give `go2rtc` execute permission for all users (`chmod 755`). It runs as its own `go2rtc` user, which doesn't own the file, so owner-only execute permission isn't enough.
4. Restart Frigate and the custom version will be used, you can verify by checking go2rtc logs.
The same exception applies, and again only to [`FRIGATE_ROOT_SERVICES`](/configuration/non_root#keeping-individual-services-root) listing `go2rtc`: the binary is ignored there and the embedded one is used, with a warning in the log. Unlike `ffmpeg.path`, the go2rtc binary location is not configurable, so there is no outside-`/config` alternative. Use `FRIGATE_RUN_AS_ROOT=true` instead if you need both a custom go2rtc build and root. The default mode and the escape hatch both honor `/config/go2rtc` exactly as they always have.
## Validating your config.yml file updates
When frigate starts up, it checks whether your config file is valid, and if it is not, the process exits. To minimize interruptions when updating your config, you have three options -- you can edit the config via the WebUI which has built in validation, use the config API, or you can validate on the command line using the frigate docker container.
so each ID is one less than the displayed file line number.
Audio label mappings are separate from the object detector's `model.labelmap`.
### Common Audio Labels
The labelmap includes hundreds of sound types. The labels below are the ones most users may find practical, grouped by what they're typically used for. Use the exact label string from the left column in your `listen` config, or search for the label in the Frigate UI directly.
@@ -180,7 +204,7 @@ Frequently-heard labels like `speech` can generate a lot of events, and each eve
### Audio Transcription
Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI's open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service. Automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI's open-source Whisper models via `faster-whisper`, and can alternatively offload transcription to a [GenAI provider](#genai-provider). The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service. Automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
:::info
@@ -200,6 +224,7 @@ To enable transcription, configure it globally and optionally disable for specif
- Set **Audio transcription model or GenAI provider name** to `whisper` for Frigate's built-in local models, or to the name of a GenAI provider
- Set **Transcription device** to the desired device
- Set **Model size** to the desired size
@@ -211,6 +236,7 @@ To enable transcription, configure it globally and optionally disable for specif
```yaml
audio_transcription:
enabled:True
model:whisper
device:...
model_size:...
```
@@ -239,20 +265,88 @@ The optional config parameters that can be set at the global level include:
- **`enabled`**: Enable or disable the audio transcription feature.
- Default: `False`
- It is recommended to only configure the features at the global level, and enable it at the individual camera level.
- **`model`**: The transcription backend.
- Default: `whisper`
-`whisper` uses Frigate's built-in local models, described by `device` and `model_size` below.
- Any other value must name a key in your `genai` config whose entry has `transcribe` in its `roles`. See [GenAI Provider](#genai-provider).
- **`device`**: Device to use to run transcription and translation models.
- Default: `CPU`
- This can be `CPU` or `GPU`. The `sherpa-onnx` models are lightweight and run on the CPU only. The `whisper` models can run on GPU but are only supported on CUDA hardware.
- Ignored when `model` names a GenAI provider.
- **`model_size`**: The size of the model used for live transcription.
- Default: `small`
- This can be `small` or `large`. The `small` setting uses `sherpa-onnx` models that are fast, lightweight, and always run on the CPU but are not as accurate as the `whisper` model.
- This config option applies to **live transcription only**. Recorded `speech` events will always use a different `whisper` model (and can be accelerated for CUDA hardware if available with `device: GPU`).
- **`language`**: Defines the language used by `whisper` to translate `speech` audio events (and live audio only if using the `large` model).
- Default: `en`
-You must use a valid [language code](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10).
- This config option applies to **live transcription only**. With `model: whisper`, recorded `speech` events always use a different `whisper` model (and can be accelerated for CUDA hardware if available with `device: GPU`).
- Ignored when `model` names a GenAI provider.
- **`language`**: Defines the language used to transcribe and translate `speech` audio events (and live audio only if using the `large` model or a GenAI provider).
-Default: `auto`
-`auto` lets the model detect the language itself, which most models do well. Set an explicit language only if detection is picking the wrong one.
- Otherwise you must use a valid [language code](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10).
- Transcriptions for `speech` events are translated.
- Live audio is translated only if you are using the `large` model. The `small``sherpa-onnx` model is English-only.
The only field that is valid at the camera level is `enabled`.
The only field that is valid at the camera level is `enabled`. In particular `model` is global only: the transcription backend is a process-wide resource shared by every camera.
#### GenAI Provider
Frigate can send audio to a GenAI provider for transcription when that provider has the `transcribe` role. This is useful if you already run a GenAI provider, or if you do not have the CPU/GPU headroom for a local whisper model. Supported providers are **OpenAI**, **Azure OpenAI**, **Gemini**, and **llama.cpp** with an audio-capable model (a dedicated ASR model such as Qwen3-ASR, or a general multimodal model that accepts audio). Ollama is not supported as it has no audio input.
To use a GenAI provider for audio transcription:
1. Configure a GenAI provider with `transcribe` in its `roles`.
2. Set the audio transcription model to that GenAI config key (e.g. `whisper_cloud`).
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Enrichments > Audio transcription" />.
| **Audio transcription model or GenAI provider name** | Set to the GenAI config key (e.g. `whisper_cloud`) to use a configured GenAI provider for transcription |
The GenAI provider must also be configured with the `transcribe` role under <NavPath path="Settings > Enrichments > Generative AI" />.
</TabItem>
<TabItem value="yaml">
```yaml
genai:
whisper_cloud:
provider:openai
api_key:your-api-key
model:gpt-transcribe
roles:
- transcribe
audio_transcription:
enabled:True
model:whisper_cloud
language:en
```
</TabItem>
</ConfigTabs>
:::warning
**Give `transcribe` its own `genai` entry.** A `genai` entry has a single `model` string that is shared by every role it holds, so `roles: [descriptions, transcribe]` would send the same model name to both the chat endpoint and the transcription endpoint. Transcription models and chat models are almost never the same model, so define a dedicated entry as shown above.
:::
:::warning
**Live transcription against a metered provider is billed continuously.** In live mode Frigate uploads an overlapping ~2 second window of audio roughly once per second, per camera, for as long as audio stays above that camera's `audio.min_volume`. Windows below that threshold are never uploaded, which is what keeps a quiet camera near zero requests, but a camera pointed at a busy street will keep sending.
Three things keep this opt-in: `transcribe` is not one of the default roles, live transcription is off by default, and the volume gate suppresses silence. Transcription of recorded `speech` events is unaffected - it remains a manual, one-request-per-event action.
:::
`device` and `model_size` have no effect on this path and no local model is ever downloaded.
`language` defaults to `auto`, which sends no language hint and lets the model detect it. Most audio models detect language well, so leave it on `auto` unless detection is picking the wrong one.
When set explicitly, it is sent as the transcription endpoint's native `language` parameter for OpenAI, Azure, and llama.cpp, and as part of the prompt for Gemini. This matters for dedicated ASR models such as Qwen3-ASR: they read the prompt as contextual biasing rather than as an instruction, so a language named in the prompt is ignored, while the endpoint parameter is honored.
#### Live transcription
@@ -268,6 +362,8 @@ Results can be error-prone due to a number of factors, including:
For speech sources close to the camera with minimal background noise, use the `small` model.
A [GenAI provider](#genai-provider) is generally the most accurate option for live transcription, at the cost of a network round trip per window. That round trip has to stay under about a second to keep up with the audio; if it does not, Frigate drops the oldest buffered audio rather than letting the backlog grow.
If you have CUDA hardware, you can experiment with the `large``whisper` model on GPU. Performance is not quite as fast as the `sherpa-onnx``small` model, but live transcription is far more accurate. Using the `large` model with CPU will likely be too slow for real-time transcription.
#### Transcription and translation of `speech` audio events
@@ -284,7 +380,7 @@ Only one `speech` event may be transcribed at a time. Frigate does not automatic
:::
Recorded `speech` events will always use a `whisper` model, regardless of the `model_size` config setting. Without a supported Nvidia GPU, generating transcriptions for longer `speech` events may take a fair amount of time, so be patient.
With `model: whisper`, recorded `speech` events always use a `whisper` model, regardless of the `model_size` config setting. Without a supported Nvidia GPU, generating transcriptions for longer `speech` events may take a fair amount of time, so be patient. With a [GenAI provider](#genai-provider), the recorded clip is sent to the provider instead and no local model is used.
@@ -22,7 +22,9 @@ The following ports are available to access the Frigate web UI.
## Onboarding
On startup, an admin user and password are generated and printed in the logs. It is recommended to set a new password for the admin account after logging in for the first time under Settings > Users.
On startup, an admin user and password are generated and printed in the logs. It is recommended to set a new password for the admin account after logging in for the first time.
On a new install the [setup wizard](../guides/getting_started.md#configuring-frigate) offers this as its first step, along with creating accounts for anyone else who needs access. You can also do both at any time under <NavPath path="Settings > Users" />.
## Resetting admin password
@@ -214,9 +216,9 @@ A default role can be provided. Any value in the mapped `role` header will overr
Navigate to <NavPath path="Settings > System > Proxy" /> and set the default role.
| **Default role** | Fallback role when no role header is present (e.g., `viewer`), or `None (deny access)` to reject unmapped users |
</TabItem>
<TabItem value="yaml">
@@ -230,6 +232,14 @@ proxy:
</TabItem>
</ConfigTabs>
Setting `default_role` to `none` denies access instead of falling back to a role. Any proxy-authenticated user whose headers do not match an explicit `role_map` entry receives a 403 response. This is useful when the upstream proxy authenticates a broader set of users than should reach Frigate, so that only mapped groups are allowed in.
```yaml
proxy:
...
default_role:none
```
## Role mapping
In some environments, upstream identity providers (OIDC, SAML, LDAP, etc.) do not pass a Frigate-compatible role directly, but instead pass one or more group claims. To handle this, Frigate supports a `role_map` that translates upstream group names into Frigate's internal roles (`admin`, `viewer`, or custom). This is configurable via YAML in the configuration file:
@@ -255,7 +265,7 @@ In this example:
- If the proxy passes a role header containing `sysadmins` or `access-level-security`, the user is assigned the `admin` role.
- If the proxy passes a role header containing `camera-viewer`, the user is assigned the `viewer` role.
- If the proxy passes a role header containing `operators`, the user is assigned the `operator` custom role.
- If no mapping matches, Frigate falls back to `default_role` if configured.
- If no mapping matches, Frigate falls back to `default_role` if configured, or denies access if `default_role` is `none`.
- If `role_map` is not defined, Frigate assumes the role header directly contains `admin`, `viewer`, or a custom role name.
**Note on matching semantics:**
@@ -329,7 +339,7 @@ Frigate supports user roles to control access to certain features in the UI and
- **admin**: Full access to all features, including user management and configuration.
- **viewer**: Read-only access to the UI and API, including viewing cameras, review items, and historical footage. Configuration editor and settings in the UI are inaccessible.
- **Custom Roles**: Arbitrary role names (alphanumeric, dots/underscores) with specific camera permissions. These extend the system for granular access (e.g., "operator" for select cameras).
- **Custom Roles**: Arbitrary role names (alphanumeric, dots/underscores) with specific camera permissions. These extend the system for granular access (e.g., "operator" for select cameras). The names `admin`, `viewer`, and `none` are reserved and cannot be used.
@@ -18,13 +18,17 @@ Each camera tile in Birdseye is composed from the frames of the stream assigned
## Birdseye Behavior
### Birdseye Modes
### Birdseye Activity Types
Birdseye offers different modes to customize which cameras show under which circumstances.
Birdseye offers independent activity types that control when cameras are shown. Multiple activity types can be listed together.
- **continuous:** All cameras are always included
- **motion:** Cameras that have detected motion within the last 30 seconds are included
- **objects:** Cameras that have tracked an active object within the last 30 seconds are included
- **continuous:** The camera is always included
- **motion:** The camera is included when motion was detected within the last 30 seconds
- **all_objects:** The camera is included when a tracked object is present, active or stationary
- **alerts:** The camera is included while an alert review item is in progress
- **detections:** The camera is included while a detection review item is in progress
`alerts` and `detections` follow the review item's own lifetime, so the camera is removed as soon as the review item ends. Which objects qualify for each is set in [review configuration](./review.md).
### Custom Birdseye Icon
@@ -39,27 +43,29 @@ To include a camera in Birdseye view only for specific circumstances, or exclude
**Global settings:** Navigate to <NavPath path="Settings > System > Birdseye" /> to configure the default Birdseye behavior for all cameras.
**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the mode or disable Birdseye for a specific camera.
**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the activity types or disable Birdseye for a specific camera.
| **Enable Birdseye** | Whether this camera appears in Birdseye view |
| **Activity types** | Conditions that determine when to show the camera |
</TabItem>
<TabItem value="yaml">
```yaml {8-10,12-14}
```yaml {10-12,15-16}
# Include all cameras by default in Birdseye view
birdseye:
enabled: True
mode: continuous
modes:
- continuous
cameras:
front:
# Only include the "front" camera in Birdseye view when objects are detected
# Only include the "front" camera in Birdseye view when an alert is in progress
birdseye:
mode: objects
modes:
- alerts
back:
# Exclude the "back" camera from Birdseye view
birdseye:
@@ -71,7 +77,7 @@ cameras:
### Birdseye Inactivity
By default birdseye shows all cameras that have had the configured activity in the last 30 seconds. This threshold can be configured.
By default birdseye shows all cameras that have had the configured activity in the last 30 seconds. This threshold can be configured, and applies to the `motion` and `all_objects` activity types only.
<ConfigTabs>
<TabItem value="ui">
@@ -140,7 +146,8 @@ Navigate to <NavPath path="Settings > System > Birdseye" /> and in the **Camera
@@ -9,7 +9,7 @@ import NavPath from "@site/src/components/NavPath";
## Adding a camera with the Add Camera Wizard
The Add Camera Wizard is the recommended way to add a camera. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />. The wizard connects to your camera, tests each stream, and writes the camera's configuration for you, including the [go2rtc](go2rtc.md) restream and the live view stream mapping, so a standard setup needs no hand-written YAML.
The Add Camera Wizard is the recommended way to add a camera. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />, or use it from the [setup wizard](../guides/getting_started.md#configuring-frigate) on a new install. The wizard connects to your camera, tests each stream, and writes the camera's configuration for you, including the [go2rtc](go2rtc.md) restream and the live view stream mapping, so a standard setup needs no hand-written YAML.
### Step 1: Name and connection
@@ -83,11 +83,12 @@ A camera is enabled by default but can be disabled by using `enabled: False`. Ca
Each role can only be assigned to one input per camera. The options for roles are as follows:
@@ -100,7 +100,7 @@ VS Code supports JSON schemas for automatically validating configuration files.
## Environment Variable Substitution
Frigate supports the use of environment variables starting with `FRIGATE_`**only** where specifically indicated in the [reference config](./advanced/reference.md). For example, the following values can be replaced at runtime by using environment variables:
Frigate supports the use of environment variables starting with `FRIGATE_`**only** where specifically indicated in the [reference config](./advanced/reference.md). See [substitution sources and precedence](./advanced/system.md#substitution-sources-and-precedence) for where those values can come from, including `secrets.yaml`. For example, the following values can be replaced at runtime by using environment variables:
```yaml
mqtt:
@@ -154,7 +154,7 @@ Here are some common starter configuration examples. These can be configured thr
1. Navigate to <NavPath path="Settings > System > MQTT" /> and configure the MQTT connection to your Home Assistant Mosquitto broker
2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)`
3. Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type**`EdgeTPU` and **Device**`usb`
3. Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown
4. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
5. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
6. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
@@ -172,10 +172,9 @@ mqtt:
ffmpeg:
hwaccel_args:preset-rpi-64-h264
detectors:
coral:
type:edgetpu
device:usb
models:
- devices:
- edgetpu:usb
record:
enabled:True
@@ -233,7 +232,7 @@ cameras:
1. Navigate to <NavPath path="Settings > System > MQTT" /> and set **Enable MQTT** to off
2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`
3. Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type**`EdgeTPU` and **Device**`usb`
3. Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown
4. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
5. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
6. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
@@ -249,10 +248,9 @@ mqtt:
ffmpeg:
hwaccel_args:preset-vaapi
detectors:
coral:
type:edgetpu
device:usb
models:
- devices:
- edgetpu:usb
record:
enabled:True
@@ -310,8 +308,8 @@ cameras:
1. Navigate to <NavPath path="Settings > System > MQTT" /> and configure the connection to your MQTT broker
2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`
3. Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type**`openvino` and **Device**`AUTO`
4. On the same page, in the **Custom Model** tab, configure the OpenVINO model path and settings
3. Navigate to <NavPath path="Settings > System > Detection models" /> and select **Intel GPU** from the **Hardware** dropdown
4. On the same model, open the **Custom Model** tab and configure the OpenVINO model path and settings
5. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
6. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
7. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
@@ -106,3 +106,5 @@ Output arguments are passed to FFmpeg after your camera source and control how r
| preset-record-mjpeg | Record - MJPEG Cameras | Record an MJPEG stream | Restreaming the MJPEG stream is recommended instead |
| preset-record-jpeg | Record - JPEG Cameras | Record a live JPEG | Restreaming the live JPEG is recommended instead |
| preset-record-ubiquiti | Record - Ubiquiti Cameras | Record a Ubiquiti stream with audio | Handles Ubiquiti's non-standard audio format |
These presets apply to the `record` output args. If [sub stream recording](/configuration/record#sub-stream-recording) is enabled, the same args are used for the `record_sub` role unless `output_args.record_sub` is set, which accepts the same presets and manual args.
The examples on this page all use `my_provider`, but the name is arbitrary and is only used to reference the provider elsewhere in the config (for example, `semantic_search.model`).
Each provider handles one or more **roles**: `chat`, `descriptions`, and`embeddings`. A provider handles all three by default, and each role may be assigned to exactly one provider. Define a single provider if you want it to do everything, or split the roles across several providers using the `roles` option.
Each provider handles one or more **roles**: `chat`, `descriptions`, `embeddings`, and `transcribe`. A provider handles the first three by default; `transcribe` must always be listed explicitly, and is not available on Ollama, which has no audio input. Each role may be assigned to exactly one provider. Define a single provider if you want it to do everything, or split the roles across several providers using the `roles` option.
If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
@@ -63,11 +63,11 @@ Running Generative AI models on CPU is not recommended, as high inference times
You must use a vision-capable model with Frigate. The following models are recommended for local deployment of the `descriptions` and `chat` roles:
| `qwen3-vl` | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object. |
| `qwen3.6`/`qwen3.8` | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
| `gemma4` | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action. |
| Model | Review [frame mode](/configuration/genai/genai_review#frame-mode) | Notes |
| `qwen3-vl` |`frames` | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object. Follows a sequence of frames on its own. |
| `qwen3.6`/`qwen3.8` |`frames` | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
| `gemma4` |`annotated_frames` | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action. Loses track of activity that repeats or reverses, so it benefits from annotated frames. |
#### Embedding models
@@ -77,6 +77,17 @@ The `embeddings` role needs a different kind of model. Text queries are matched
| `qwen3-vl-embedding` | Multimodal embeddings for [Semantic Search](/configuration/semantic_search#genai-provider). Must be served by llama.cpp started with `--embeddings` and `--mmproj`. |
#### Transcription models
The `transcribe` role needs a model that accepts audio input. A text-only or vision-only model cannot serve this role. The following are recommended for local deployment of the `transcribe` role:
| `qwen3-asr` | Dedicated speech recognition model covering 30 languages, and the better choice for transcription quality. It only transcribes, so it cannot be shared with the `descriptions` or `chat` roles. |
| `gemma4` | General multimodal model that accepts audio as well as images, so one served model can cover `transcribe` alongside the other roles. Transcript quality is below `qwen3-asr`, particularly on noisy audio. |
Both must be served by llama.cpp started with the matching audio `--mmproj`. llama.cpp only reports audio support when an audio projector is loaded. Without it Frigate sees the model as text-only and the `transcribe` role is unavailable in the UI. Frigate transcribes through the server's `/v1/audio/transcriptions` route, which llama.cpp serves for any audio-capable model.
:::info
Each model is available in multiple parameter sizes (3b, 4b, 8b, etc.). Larger sizes are more capable of complex tasks and understanding of situations, but requires more memory and computational resources. It is recommended to try multiple models and experiment to see which performs best.
@@ -517,6 +528,6 @@ objects:
7. If descriptions are generated but the results are poor or inconsistent, look at the model and the context window.
- Empty fields, missing `shortSummary` values, or `Failed to parse review description` errors usually mean the model is not following the requested JSON schema. Smaller models struggle with structured output. Try a larger parameter size or one of the [recommended models](#recommended-local-models).
- Frigate calculates how many frames to send from the context size the provider reports. If your server reports a different value than it is actually running with, frames will be truncated or the request will fail. Pin the value by adding `context_size` under <NavPath path="Settings > Enrichments > Generative AI > Provider options" /> (`genai.<provider>.provider_options`), and for Ollama also confirm `options.num_ctx` there matches the context you have configured.
- Check **Review Description Speed** and **Object Description Speed** in <NavPath path="System metrics > Enrichments" />. If inference takes tens of seconds, requests will queue behind each other and descriptions will appear to stop. For Ollama, review `OLLAMA_NUM_PARALLEL`, `OLLAMA_MAX_QUEUE`, and `OLLAMA_MAX_LOADED_MODELS` so that concurrent requests from Frigate are handled the way you expect.
- Check **Review Description Speed** and **Object Description Speed** in <NavPath path="Health and Metrics > Enrichments" />. If inference takes tens of seconds, requests will queue behind each other and descriptions will appear to stop. For Ollama, review `OLLAMA_NUM_PARALLEL`, `OLLAMA_MAX_QUEUE`, and `OLLAMA_MAX_LOADED_MODELS` so that concurrent requests from Frigate are handled the way you expect.
Review items are sent to the model as a sequence of still frames. Some models follow that sequence well on their own; others lose track of activity that repeats or reverses, and describe a single trip when the subject actually made several. The `frame_mode` option controls how those frames are presented.
- `frames` (default): the prompt followed by the frames, exactly as earlier versions of Frigate sent them.
- `annotated_frames`: each frame is preceded by its frame number and elapsed time, along with notes describing what the object tracker recorded at that moment, such as an object being first detected, starting to move, turning around, stopping, or no longer being detected.
The notes come from tracking data rather than from the images, so they describe activity the model may not have picked up on its own. In testing with a person carrying three waste bins to the curb one at a time, `gemma4` described a single trip on every attempt with `frames`, and consistently described multiple trips with `annotated_frames`. Models that already handle these sequences well, such as the `qwen3-vl` family, gain little and should stay on `frames`.
Annotated mode also caps the number of frames, since the notes already establish the order of events and extra near-duplicate frames tend to crowd out the middle of a clip. Longer review items are sampled more sparsely as a result, and typically use fewer tokens than `frames` mode for the same item.
:::note
Annotated mode needs tracking data for the review item. If none is available, Frigate falls back to sending plain frames for that item.
:::
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > Review" />.
- Set **GenAI config > Frame mode** to the desired mode (e.g., `annotated_frames`)
</TabItem>
<TabItem value="yaml">
```yaml {4}
review:
genai:
enabled: true
frame_mode: annotated_frames
```
</TabItem>
</ConfigTabs>
### Response Style
Different models respond to the built-in prompt with very different writing styles: some produce natural narration while others sound short and mechanical. The `response_style` option selects a writing style preset that rewords the prompt's instructions for the user-facing fields (the title, short summary, and scene description). Presets replace those instructions rather than adding extra ones, so the model never receives competing style directions.
Available presets:
- `default`: The built-in prompt, unchanged. This already reads like a neutral security report.
- `natural`: Plain, everyday narration with flowing sentences and sentence-style headline titles. Useful when a model's output sounds robotic.
- `concise`: As brief as possible while still covering each significant action, with terse two-to-four word titles.
- `detailed`: Thorough descriptions and titles that include the most identifying specifics, like colors, clothing, and carried items.
Style presets only adjust how the user-facing text reads; the model's step-by-step observations and threat level scoring guidance are unaffected. Results vary by model, so it is worth comparing presets against saved debug output using `testing-scripts/genai_review_tester.py` in the Frigate repository.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > Review" />.
- Set **GenAI config > Response style** to the desired preset (e.g., `natural`)
</TabItem>
<TabItem value="yaml">
```yaml {4}
review:
genai:
enabled: true
response_style: natural
```
</TabItem>
</ConfigTabs>
## Review Reports
Along with individual review item summaries, Generative AI can also produce a single report of review items from all cameras marked "suspicious" over a specified time period (for example, a daily summary of suspicious activity while you're on vacation).
@@ -8,7 +8,7 @@ import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
import FaqItem from "@site/src/components/FaqItem";
Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car` or`motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car`,`motorcycle`, `bus`, `truck`, `school_bus`, or `garbage_truck`, depending on which of those labels your model detects. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.
@@ -24,7 +24,7 @@ When a plate is recognized, the details are:
- Viewable in the Details pane in Review/History.
- Viewable in the Tracked Object Details pane in Explore (sub labels and recognized license plates).
- Filterable through the More Filters menu in Explore.
- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the `car` or `motorcycle` tracked object.
- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the vehicle tracked object.
- Published via the `frigate/tracked_object_update` MQTT topic with `name` (if [known](#matching)) and `plate`.
## Model Requirements
@@ -35,7 +35,7 @@ Users without a model that detects license plates can still run LPR. Frigate use
:::note
In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle` before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a `car` or `motorcycle` will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
In the default mode, Frigate's LPR needs to first detect a vehicle before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a vehicle will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
:::
@@ -86,7 +86,7 @@ cameras:
</TabItem>
</ConfigTabs>
For non-dedicated LPR cameras, ensure that your camera is configured to detect objects of type `car` or `motorcycle`, and that a car or motorcycle is actually being detected by Frigate. Otherwise, LPR will not run.
For non-dedicated LPR cameras, ensure that your camera is configured to detect vehicle objects, and that a vehicle is actually being detected by Frigate. Otherwise, LPR will not run. The object types that can carry a plate are defined by your model's `attributes_map`, so if your model detects other vehicle labels, you can add them there.
Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
- **Known plates**: Assign custom `sub_label` values to `car` and `motorcycle` objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
- **Known plates**: Assign custom `sub_label` values to vehicle objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
- **Match distance**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate. For example, setting to `1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`. This parameter will _not_ operate on known plates that are defined as regular expressions.
</TabItem>
@@ -316,7 +316,7 @@ lpr:
:::note
If a camera is configured to detect `car` or `motorcycle` but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
If a camera is configured to detect vehicles but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
<ConfigTabs>
<TabItem value="ui">
@@ -456,7 +456,7 @@ With this setup:
- Snapshots will have license plate bounding boxes on them.
- The `frigate/events` MQTT topic will publish tracked object updates.
- Debug view will display `license_plate` bounding boxes.
- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the `car` / `motorcycle` and the `license_plate` in the snapshots on the Frigate+ website, even if the car is barely visible.
- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the vehicle and the `license_plate` in the snapshots on the Frigate+ website, even if the vehicle is barely visible.
### Using the Secondary LPR Pipeline (Without Frigate+)
@@ -611,9 +611,9 @@ If you are still having issues detecting plates, start with a basic configuratio
</FaqItem>
<FaqItem id="can-i-run-lpr-without-detecting-car-or-motorcycle-objects" question={<>Can I run LPR without detecting <code>car</code> or <code>motorcycle</code> objects?</>}>
<FaqItem id="can-i-run-lpr-without-detecting-car-or-motorcycle-objects" question={<>Can I run LPR without detecting vehicle objects?</>}>
In normal LPR mode, Frigate requires a `car` or `motorcycle` to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
In normal LPR mode, Frigate requires a vehicle to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
</FaqItem>
@@ -697,9 +697,9 @@ lpr:
- You may need to adjust your `detection_threshold` if your plates are not being detected.
4. Ensure the characters on detected plates are being _recognized_.
- Check the **Plate recognition** inference time in Enrichment metrics (<NavPath path="System metrics > Enrichments" />). High inference times (> 100ms) could lead to poor recognition results, especially for dedicated LPR cameras where the plate crosses the frame quickly.
- Check the **Plate recognition** inference time in Enrichment metrics (<NavPath path="Health and Metrics > Enrichments" />). High inference times (> 100ms) could lead to poor recognition results, especially for dedicated LPR cameras where the plate crosses the frame quickly.
- Enable `debug_save_plates` to save images of detected text on plates to the clips directory (`/media/frigate/clips/lpr`). Ensure these images are readable and the text is clear.
- Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the `car` or `motorcycle` label will change to the recognized plate when LPR is enabled and working.
- Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the vehicle's label will change to the recognized plate when LPR is enabled and working.
- Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
</FaqItem>
@@ -714,13 +714,13 @@ LPR's performance impact depends on your hardware. Ensure you have at least 4GB
The YOLOv9 license plate detector model will run (and the metric will appear) if you've enabled LPR but haven't defined `license_plate` as an object to track, either at the global or camera level.
If you are detecting `car` or `motorcycle` on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
If you are detecting vehicles on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
</FaqItem>
<FaqItem id="it-looks-like-frigate-picked-up-my-cameras-timestamp-or-overlay-text-as-the-license-plate-how-can-i-prevent-this" question="It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?">
This could happen if cars or motorcycles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
This could happen if vehicles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
If you are using a model that natively detects `license_plate`, add an _object mask_ of type `license_plate` and a _motion mask_ over your text.
@@ -28,6 +28,24 @@ WebRTC may use an external STUN server for NAT traversal. MSE and HLS streaming
:::
### Selecting a streaming technology
Frigate [defaults to MSE](#why-does-frigate-prefer-mse-over-webrtc-for-live-view) for restreamed cameras by design. To use WebRTC, select it explicitly from a camera's single-camera Live view settings (the settings menu in the camera's Live view header on desktop, or the settings drawer on mobile). Three related controls work together:
- **Stream**: _what_ to play. This lists the [streams you've configured](#setting-streams-for-live-ui) (for example `Main Stream` and `Sub Stream`).
- **Force low-bandwidth mode**: a switch that always plays Frigate's built-in low-bandwidth feed (the stream assigned the `detect` role, using JSMpeg) instead of the selected stream. It works anywhere without go2rtc and is useful on slow or metered connections. While it is enabled, the stream and streaming technology selectors are disabled; your stream and technology choices are restored when you turn it off.
- **Streaming Technology**: _how_ to play the selected stream, listing **MSE** and **WebRTC**. It is only shown for a restreamed stream.
- The choices are saved **per device, per camera** in your browser's local storage.
- **WebRTC is only selectable when it can actually work for that stream.** When it can't, the option is shown disabled with the reason inline, and a more detailed reason (the failing codecs, or why the connectivity check failed) is logged to your browser's console. Common reasons:
- **Not configured**: no `candidates` or `ice_servers` are set under `go2rtc.webrtc` (see [WebRTC extra configuration](#webrtc-extra-configuration)).
- **Could not connect**: e.g. port `8555` isn't reachable, or a STUN/TURN server is misconfigured. Frigate runs a one-time WebRTC connectivity check when the Live view opens; the option may briefly show as "checking" while it runs.
- **Unsupported video codec**: the stream's video codec can't be played over WebRTC in your browser, most commonly H.265/HEVC in Firefox or Edge.
- **Unsupported audio codec**: WebRTC needs opus or G.711 audio, so a stream whose playback audio is only AAC (without an added opus/G.711 track) can't carry audio over WebRTC. See [Audio Support](#audio-support) for how to add one.
- **Unsupported browser**: the browser doesn't support WebRTC.
When WebRTC isn't available, Frigate automatically uses MSE (or falls back to JSMpeg), so live view keeps working regardless of the selection.
### Camera Settings Recommendations
If you are using go2rtc, you should adjust the following settings in your camera's firmware for the best experience with Live view:
@@ -157,6 +175,17 @@ WebRTC works by creating a TCP or UDP connection on port `8555`. However, it req
- stun:8555
```
- The web UI uses the STUN and TURN servers in `ice_servers` and falls back to Google's public STUN server when none are set:
```yaml title="config.yml"
go2rtc:
webrtc:
ice_servers:
- urls: [turn:turn.example.com:3478]
username: frigate
credential: password
```
- For access through Tailscale, the Frigate system's Tailscale IP must be added as a WebRTC candidate. Tailscale IPs all start with `100.`, and are reserved within the `100.64.0.0/10` CIDR block.
- Note that some browsers may not support H.265 (HEVC). You can check your browser's current version for H.265 compatibility [here](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#codecs-madness).
@@ -206,6 +235,8 @@ For devices that support two way talk, Frigate can be configured to use the feat
- Ensure you access Frigate via https (may require [opening port 8971](/frigate/installation/#ports)).
- For the Home Assistant Frigate card, [follow the docs](http://card.camera/#/usage/2-way-audio) for the correct source.
The two-way talk control in the single-camera Live view is only enabled when WebRTC is available; if WebRTC isn't configured or can't connect, the control is shown disabled.
To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-cameras)
As a starting point to check compatibility for your camera, view the list of cameras supported for two-way talk on the [go2rtc repository](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#two-way-audio). For cameras in the category `ONVIF Profile T`, you can use the [ONVIF Conformant Products Database](https://www.onvif.org/conformant-products/)'s FeatureList to check for the presence of `AudioOutput`. A camera that supports `ONVIF Profile T` _usually_ supports this, but due to inconsistent support, a camera that explicitly lists this feature may still not work. If no entry for your camera exists on the database, it is recommended not to buy it or to consult with the manufacturer's support on the feature availability.
Frigate's services run as an unprivileged user inside the container. The main Frigate process and nginx run as `frigate`, and go2rtc runs as its own more restricted `go2rtc` user. Only the s6 init system and the certsync helper stay root.
The runtime user is uid/gid `1000:1000` by default. You can change it with `PUID`/`PGID`, or bypass Frigate's user handling entirely with Docker's own `user:`.
Most upgrades need nothing. Frigate aligns your volume ownership on the first boot and grants access to your hardware at startup. The sections below cover the cases that need attention: large storage volumes, network storage, and hardware the automatic grant can't reach.
## Run modes
| Mode | How to enable | Ownership of `/config` and `/media/frigate` | `read_only: true` |
| Default | nothing, this is the default | Aligned to `1000:1000` on first boot | Supported |
| `PUID`/`PGID` | `PUID=1001`, `PGID=1001` | Aligned to the values you set, on first boot | Not supported |
| Docker-native user | `user: "1001:1001"` | You own it, Frigate never changes ownership | Supported |
| Root (escape hatch) | `FRIGATE_RUN_AS_ROOT=true` | Never touched | Not supported |
| Granular root | `FRIGATE_ROOT_SERVICES=frigate` | Aligned at boot; recordings and exports also at create | Not supported |
`PUID`/`PGID` remapping runs `usermod` at startup, which writes to `/etc/passwd`, so it can't work with a read-only root filesystem. That combination stops at startup with a message pointing here. `EXTRA_GROUPS` writes to `/etc/group` and stops the same way; use Docker's `group_add:` instead, which needs no writes inside the container. The default mode and Docker's `user:` mode both work with `read_only: true`; see [Hardened deployment](#hardened-deployment).
`FRIGATE_RUN_AS_ROOT` is matched against the exact lowercase string `true`. `True`, `TRUE`, and `1` are all ignored. `FRIGATE_DEVICE_ACLS` works the same way: only the lowercase string `false` turns off the automatic device grants.
### Keeping individual services root
`FRIGATE_ROOT_SERVICES` takes a comma separated list of `frigate`, `go2rtc`, and `nginx`. A listed service keeps running as root, and everything else about non-root operation still applies: `PUID`/`PGID` remapping, the ownership sweep, and ownership of the files those services create.
There are two reasons to use it:
- Your detector hardware won't work as an unprivileged user, even after reading [Hardware device access](#hardware-device-access). `FRIGATE_ROOT_SERVICES=frigate` keeps the main process and its detectors as root while nginx and go2rtc stay unprivileged.
- You want everything to run as root but still want your files owned by `PUID`/`PGID` instead of root. `FRIGATE_ROOT_SERVICES=frigate,go2rtc,nginx` does that.
Try the device grants and `EXTRA_GROUPS` first. The `frigate` service runs the API and every ffmpeg process that decodes your camera streams, so listing it puts those back on root as well, not just your detectors.
A listed service also stops honoring a [custom ffmpeg or go2rtc build](/configuration/advanced/system#custom-dependencies) kept in `/config`, since that directory stays owned by the unprivileged user and a binary there would run as root. `FRIGATE_RUN_AS_ROOT=true` has no such restriction.
Recordings and exports are owned by `PUID`/`PGID` as soon as they're written, even by a root service. Snapshots, thumbnails, and other files under `clips/` are corrected on each restart, so they can show as root-owned from the host until then. A listed service also keeps root's home directory, so library caches go to the container layer instead of `/config`. The same applies to the [detector runtimes](/frigate/network_requirements#detector-runtimes) Frigate installs at first start (Hailo, MemryX, AXEngine): a root `frigate` service installs them into `/root/.local`, which is lost when the container is recreated, and never loads a copy left behind in `/config/.local`.
Listing all three services is not the same as `FRIGATE_RUN_AS_ROOT=true`. The escape hatch never touches ownership; the list keeps the ownership handling active. A few more details:
- An unknown name in the list stops the container at startup, rather than silently leaving a service unprivileged.
- Changing the list runs the full ownership sweep once on the next boot.
- If both are set, `FRIGATE_RUN_AS_ROOT=true` wins and the list is ignored.
- With Docker's `user:`, the list does nothing, since the container never has root to keep.
## Migrating an existing install
Volumes from earlier versions of Frigate are owned by root, so ownership has to be aligned with the runtime user once. This happens automatically on the first boot after upgrading.
On large recordings volumes, do it from the host beforehand instead. The boot sweep runs before any service starts, so a multi-terabyte `/media/frigate` can hold the container in startup long enough for Docker's healthcheck to mark it unhealthy, and orchestrators that watch health will restart it mid-sweep. If you'd rather not run the script, raise the healthcheck start period instead (`--start-period=1800s`, or `start_period: 1800s` under `healthcheck:` in compose).
Grab [`fix-permissions.sh`](https://github.com/blakeblackshear/frigate/blob/dev/docker/migration/fix-permissions.sh) from the Frigate repo and dry run it first:
Pass `PUID` and `PGID` as the third and fourth arguments if you're not using the default `1000:1000`. The script wraps the same helper the container uses, so the result is identical either way. Override the image it pulls with `FRIGATE_IMAGE=...` if you're not on `stable`.
Both the script and the boot sweep report progress, so you can tell a slow sweep from a stuck one:
```
[INFO] fix-ownership: scanning /media/frigate for ownership mismatches; this may take a while on large filesystems
[WARN] fix-ownership: adjusting ownership of 4823941 entries under /media/frigate
[INFO] fix-ownership: finished /media/frigate in 12m 4s
```
The scan has no percentage because the total isn't known until it finishes. Watch the boot sweep with `docker logs -f frigate`.
Once the volumes are aligned, start Frigate normally. A file at `/config/.permissions_version` records what was done, so later boots skip the sweep unless you change `PUID`/`PGID`.
If something under your volumes can't be chowned, a read-only btrfs snapshot directory for example, the sweep warns and names the path and doesn't record the migration as finished. It retries on the next boot instead. Either move those paths outside `/media/frigate` or expect the scan to repeat.
### Network storage
Recordings on a NAS behave differently, so check what you have before migrating:
**SMB and CIFS** don't store per-file ownership at all. It's synthesized from the mount options, so a per-file `chown` fails and isn't needed. Mount the share as the uid and gid Frigate runs as, and every file already looks correct to the sweep:
**NFS** exports default to `root_squash` on most servers, which maps the container's root to `nobody`. The chown then fails, you get `[WARN] fix-ownership: some entries under /media/frigate could not be updated`, and since the sweep didn't finish it doesn't record the migration, so it retries on every boot.
The best fix is to not chown over NFS at all. Do it on the server, where there's no squash and no network round trip per file:
```bash
# on the NAS itself, against the exported directory
chown -R 1000:1000 /export/frigate
```
Frigate's sweep then finds nothing to change and records the migration normally. If you can't get a shell on the server, you can export temporarily with `no_root_squash`, migrate, and put it back, or leave ownership alone and set `PUID`/`PGID` to whichever uid already owns the files.
Either way the uid has to mean the same thing on both machines. NFS sends numeric uids, so container uid 1000 is uid 1000 on the server no matter what the usernames are.
Expect the first boot to be slow even when nothing needs changing, because checking ownership costs a round trip per file. That's a one-time cost. **If the sweep runs on every boot rather than once, ownership isn't actually being applied**, and the warning above will say so.
Keep `/config` on local storage either way. Frigate's database is SQLite and network shares handle its locking poorly. That's a long-standing recommendation, not something running non-root introduces.
## Rolling back
Set `FRIGATE_RUN_AS_ROOT=true` and restart. Everything runs as root again, exactly as it did before. This is the fastest way to get a broken install running while you sort out a device permission problem.
The escape hatch never changes ownership, and it clears the record of the last sweep on startup, so switching back to non-root later corrects whatever root created in the meantime. Toggling in either direction is safe.
## Hardware device access
Frigate grants the runtime user access to your devices at startup. Pass your hardware with `--device` (or `devices:` in compose) and detection and hardware acceleration work with no group or udev setup on the host.
The grant covers the common accelerator and camera nodes: GPU render nodes, Intel/AMD NPUs (`/dev/accel`), Coral, Hailo, Rockchip, Jetson, `/dev/video*`, and the USB bus. For hardware it misses, add your own paths with `DEVICE_ACL_PATHS`, a comma separated list of globs:
```yaml
environment:
DEVICE_ACL_PATHS:"/dev/mydev*"
```
Set `FRIGATE_DEVICE_ACLS=false` if you manage device permissions yourself and want Frigate to leave them alone.
Frigate grants access by adding an ACL entry for the runtime users. The device's owner and mode are unchanged, and nothing is made world accessible. One thing to know: `--device` nodes belong to the container, but a bind mounted `/dev/bus/usb` (the usual Coral USB setup) shares the host's device nodes, so the entry is visible on the host until udev recreates the node.
### Manual setup
You only need this for hardware the automatic grant can't reach, or for Docker's `user:` mode, where there's no root startup to do the granting.
Your accelerator most likely worked in older versions because Frigate ran as root. Device nodes are usually owned by `root:root`, and root either matches the group or skips the check entirely. The runtime user does neither, so a device that worked before can become unreadable with no change to your Frigate config.
#### Read what your device requires
Find the node and look at its owner, group, and mode:
Then work out which of the three permission sets applies to the runtime user. It isn't the owner, since that's root, so it gets the group bits if it belongs to that GID and otherwise falls through to "other". In the example above "other" is empty, so without membership in group 105 the runtime user can't open the node.
Watch for a node that looks permissive but isn't. A USB Coral defaults to this:
The group is `0`, so "other" applies to the runtime user, and "other" here is read only. `libedgetpu` needs to write to the node, so detection fails with `No EdgeTPU was detected` as though no Coral were attached. Read access alone isn't enough for most accelerators.
#### Grant access
Give the runtime user the GID with `EXTRA_GROUPS`, a comma separated list of numeric host GIDs. They're added to both the `frigate` and `go2rtc` users, which matters because go2rtc needs its own render and video access for hardware accelerated restreams.
```yaml
environment:
EXTRA_GROUPS:"105,44"# host render and video GIDs
```
Use numeric GIDs from the host, not names. Group names don't have to match between the host and the container, and the kernel only checks the number. If the GID doesn't exist in the image, Frigate creates a placeholder group for it.
Two things that look like they should work but don't:
- Docker's `group_add` has no effect in the default or `PUID` modes. Frigate rebuilds the supplementary group list from `/etc/group` when it drops privileges, which discards what Docker passed in. It is the right tool with Docker's `user:`, where no privilege drop happens and `EXTRA_GROUPS` does nothing.
-`privileged: true` doesn't help. It grants capabilities to root, and the runtime user isn't root, so the file permissions on the node still apply.
If the node's group is `root` or the mode denies the group, no `EXTRA_GROUPS` value will help. You need a udev rule first.
#### Verify access
Check the group landed, then check the runtime user can open the node. Test for write, not just read:
`vainfo` should reach `va_openDriver() returns 0` and list profiles. Complaints about `XDG_RUNTIME_DIR` or an X server above that are normal. OpenVINO should list `GPU`; if it returns only `CPU`, detection has fallen back and inference will be much slower without an error in the log.
To tell a permissions problem from anything else, start the container once with `FRIGATE_RUN_AS_ROOT=true`. If the device works as root and not otherwise, it's node permissions and a udev rule is the fix. If it's missing either way, the problem is your device mapping or the host, and isn't related to running non-root.
#### udev rules by device
Rules go in `/etc/udev/rules.d/` on the host and take effect after:
```bash
sudo udevadm control --reload-rules && sudo udevadm trigger
```
A device that's already connected sometimes keeps its original ownership through a trigger. If `ls -ln` doesn't show the new group, replug it, or reboot for a built-in device.
**Coral USB** needs two rules, because the device re-enumerates after loading firmware. It appears as Global Unichip `1a6e` before and Google `18d1` after, with a different node each time. A rule covering only `1a6e` gives you a Coral that starts up once and then disappears mid-run.
Map the whole `/dev/bus/usb` rather than a single node, for the same reason. Most hosts put `plugdev` at GID 46 and the image agrees, so a USB Coral often needs no `EXTRA_GROUPS` entry. Confirm with `getent group plugdev` and add the number if your host differs.
**Coral PCIe** is often `crw------- root root`, which only root can open:
```
SUBSYSTEM=="apex", MODE="0660", GROUP="apex"
```
Create the group with `sudo groupadd -f apex`, then add its GID to `EXTRA_GROUPS`.
**Hailo** works the same way. Grant `/dev/hailo0` a group and add that GID:
**Intel and AMD GPUs** usually need nothing beyond `EXTRA_GROUPS`, since most distributions ship a `render` group that owns `/dev/dri/renderD128`. The GID often differs between the host and the image, so pass the host's number rather than assuming the name resolves. Debian based images have no `render` group at all.
#### Quick reference
What each device needs when you're setting it up by hand. The automatic grant covers most of these already, so start here only if it didn't.
| Intel/AMD GPU (VAAPI/QSV) | `/dev/dri/renderD128` | Host render GID in `EXTRA_GROUPS`, from `getent group render` |
| Intel/AMD NPU | `/dev/accel` | udev rule granting a group, then that GID in `EXTRA_GROUPS` |
| Coral USB | `/dev/bus/usb` | udev rules for both `1a6e` and `18d1`; usually already covered by `plugdev` 46 |
| Coral PCIe | `/dev/apex_0` | udev rule granting a group, then that GID in `EXTRA_GROUPS` |
| Hailo | `/dev/hailo0` | udev rule granting a group, then that GID in `EXTRA_GROUPS` |
| NVIDIA | nvidia runtime | Nothing, works with the nvidia-container-toolkit defaults |
| AMD ROCm | `/dev/kfd`, `/dev/dri` | Host `video` and `render` GIDs in `EXTRA_GROUPS` |
| Raspberry Pi | `/dev/video11` | Host `video` GID in `EXTRA_GROUPS` |
| Rockchip | `/dev/dri`, `/dev/dma_heap`, `/dev/rga`, `/dev/mpp_service` | Commonly `root:root``0600`, so all four need udev rules. If you can't grant all four, use `FRIGATE_RUN_AS_ROOT` |
| Axera (AXCL) | `/dev/ax_*` per the AXCL driver docs | Unverified. Check node ownership on your hardware before assuming this works |
| Synaptics SL1680 | per the Synaptics docs | Unverified |
| MemryX | per the MemryX docs | Still requires `privileged: true`, which means root. Out of scope for non-root operation |
| Nvidia Jetson | nvidia runtime plus Jetson nodes | Unverified. The nvidia runtime handles mapping, but check `/dev/nvhost-*` ownership on your board |
| VeriSilicon NPU (Teflon) | per the driver, commonly `/dev/galcore` | Unverified. Check node ownership on your hardware before assuming this works |
| CPU detector | none | Nothing, no device is opened |
| ZMQ detector | none | Nothing, inference happens over a socket |
| Apple Silicon | none | Nothing, the NPU client runs on the host and Frigate reaches it over the network |
## Hardened deployment
A read-only root filesystem means the container can't modify itself, only the volumes you give it. It works in the default mode and under Docker's `user:`, but not with `PUID`/`PGID` or `EXTRA_GROUPS`, which both need to write to `/etc`.
Start with the default mode. It keeps go2rtc on its own restricted user and still grants your hardware automatically, at the cost of a short root startup that finishes before any service runs.
```yaml
services:
frigate:
container_name:frigate
image:ghcr.io/blakeblackshear/frigate:stable
restart:unless-stopped
stop_grace_period:30s
read_only:true
security_opt:
- no-new-privileges:true
shm_size:"512mb"# size for your cameras, see the shm-size calculation
devices:
- /dev/dri/renderD128:/dev/dri/renderD128# your hardware, granted at startup
volumes:
- /etc/localtime:/etc/localtime:ro
- /path/to/your/config:/config
- /path/to/your/storage:/media/frigate
tmpfs:
- /tmp:size=256m
- /tmp/cache:size=1000000000# recording segments, sized as before
- /run:exec,nosuid,nodev,mode=0755,size=16m
ports:
- "8971:8971"
- "8554:8554"# RTSP feeds
- "8555:8555/tcp"# WebRTC over tcp
- "8555:8555/udp"# WebRTC over udp
```
`/run` has to allow `exec`. With a read-only root filesystem s6 copies its service scripts into `/run` and runs them from there, and tmpfs mounts default to `noexec`. The equivalent for `docker run` is `--tmpfs /run:exec,nosuid,nodev,mode=0755`. Spelling out `nosuid` and `nodev` matters: passing any tmpfs options replaces Docker's defaults instead of adjusting them, so asking for `exec` alone would drop those two as well.
Size `/tmp` deliberately. It now carries nginx's config copy and its five proxy temp directories as well as the recording cache. Keeping `/tmp/cache` as its own nested tmpfs, as above, leaves your existing [cache sizing](/frigate/installation#storage) untouched and adds a small allowance for nginx. If you'd rather use one tmpfs over all of `/tmp`, size it as your cache budget plus roughly 50MB, or recordings begin failing once the cache fills.
The self signed certificate is written to `/config/tls`, which stays writable. Certificates you mount at `/etc/letsencrypt/live/frigate` work unchanged and still take precedence.
[Detector runtimes](/frigate/network_requirements#detector-runtimes) that Frigate installs at first start (Hailo, MemryX, AXEngine) are staged in `/tmp` and installed into `/config/.local`, so they work with a read-only root filesystem in the default mode and under `user:`. A root `frigate` service installs into `/root/.local` instead, which a read-only root filesystem prevents; either leave `frigate` out of `FRIGATE_ROOT_SERVICES` or drop `read_only`.
Soak a hardened deployment for 24 hours against real cameras before relying on it. A read-only root filesystem turns an occasional write into a failure that startup won't reveal.
### Never starting as root
To remove root from the container entirely, add Docker's `user:`:
```yaml
user:"1000:1000"# NOT compatible with PUID/PGID, see the run modes table
tmpfs:
- /tmp:size=256m
- /tmp/cache:size=1000000000
- /run:exec,nosuid,nodev,mode=0755,uid=1000,gid=1000,size=16m# uid must match user:
```
`/run` has to be owned by that uid as well. s6 writes its runtime state there before anything else starts, and with no root in the container a root-owned `/run` stops it during init with `cannot create /run/test of writability`. Keep `uid` and `gid` in the tmpfs options matching `user:`, and don't carry that pair back into the default mode, where a root-owned `/run` is what keeps the unprivileged services out of s6's runtime state.
This only bites once root is genuinely gone. s6's init helper is setuid, so `user:` on its own still lets init regain root and correct `/run` itself. The `no-new-privileges:true` above is what blocks that, which is also what makes the `/run` ownership mandatory. Dropping it would hide the problem by handing init root again.
Two things change, and the first one will break a working install if you skip it. The startup device grants can't run, because there is no root left to run them, so every device you pass stops working until you grant that uid access yourself with `group_add:` or a udev rule; see [Manual setup](#manual-setup). Expect this to surface as a driver error rather than a permission error, like `No VA display found` from VAAPI. And every service then runs as that one uid, so go2rtc no longer gets its own restricted user. `/config` and `/media/frigate` have to be owned by that uid already, since Frigate never adjusts ownership in this mode. Switching an existing install over also leaves `/config/go2rtc_homekit.yml` owned by the go2rtc user, which this mode can't write; `chown` it to your uid or HomeKit pairing changes stop persisting. Frigate warns and starts either way.
This mode can also take `cap_drop: [ALL]`, which the default mode cannot: starting as root needs `CAP_CHOWN` for the ownership sweep, `CAP_SETUID` and `CAP_SETGID` to drop to the runtime user, and `CAP_FOWNER` for the device grants.
### Per-variant exceptions
- **Rockchip** needs `- /sys/:/sys/:ro` alongside its device nodes, in addition to everything above.
- **MemryX** and **QNAP Container Station** still require `privileged: true` per their own documentation, which gives back most of what this layout removes. MemryX also downloads its models to `/memryx_models` on the root filesystem, so it can't run read-only regardless. Its SDK is installed into `/config/.local` like the other detector runtimes.
## Network isolation
Everything above limits what a compromised container can do to the host. It doesn't limit what your cameras can do to your network. Camera firmware is closed source, rarely patched, and not something you can audit, and none of it needs internet access for Frigate to work.
Put the cameras on their own VLAN or subnet, give the Frigate host a route into it, and deny that VLAN any route out. Frigate reaches in to pull streams, the cameras reach nothing. A second NIC on the Frigate host is the simplest version of this, and a tagged VLAN on the NIC you already have works just as well.
Here's the deny as nftables on the router, with cameras on `vlan20` and the Frigate host at `192.168.10.5`:
```
table inet cameras {
chain forward {
type filter hook forward priority filter; policy accept;
ct state established,related accept
iifname "vlan20" ip daddr 192.168.10.5 accept
iifname "vlan20" drop
}
}
```
It's in its own table so it can sit alongside an existing ruleset without touching it. Streams keep working because Frigate opens those connections and the return traffic is `established`. Cameras can still reach each other on their own VLAN, since that traffic never reaches the router, so use client isolation on the switch if that matters to you.
Two things break when you do this. The manufacturer's phone app stops working, which is the point, and camera clocks drift, because most of them set their time over NTP and are bad at it. Point them at an NTP server on your own network rather than opening the VLAN back up, or their timestamps and Frigate's will disagree.
Frigate itself needs some outbound access, though nearly all of it is optional. The startup version check is the only piece that's on by default, and `telemetry.version_check: false` turns it off. Everything else (model downloads for the enrichment features, push notifications, Frigate+, and cloud GenAI providers) only reaches out once you enable that feature. See [Network Requirements](/frigate/network_requirements) for the full list and how to run fully offline.
For containers that only talk to each other, an internal compose network gets you the same isolation without involving the router:
```yaml
services:
frigate:
networks:[default, iot]
# the rest of your frigate service
mosquitto:
image:eclipse-mosquitto
networks:[iot]
networks:
iot:
internal:true
```
`internal: true` gives that network no route off the host, so the broker isn't reachable from anywhere else on your LAN. Frigate sits on both networks and keeps its normal outbound path.
One Docker specific trap: published ports are inserted ahead of the host firewall, so `ufw deny 8971` doesn't do what it looks like it does. Bind the port to the interface you want instead, like `127.0.0.1:8971:8971` for a reverse proxy on the same host, or your LAN address for everything else.
## Known limitations
`telemetry.stats.network_bandwidth` uses nethogs, which needs `CAP_NET_ADMIN` and `CAP_NET_RAW` and therefore root. The stat is turned off automatically when Frigate isn't running as root, with one warning in the log. Use `FRIGATE_ROOT_SERVICES=frigate` (or `FRIGATE_RUN_AS_ROOT=true`) if you need it.
go2rtc's ffmpeg processes no longer appear in Intel GPU stats. Frigate reads per-process GPU usage from `/proc/<pid>/fdinfo`, which the kernel won't let one user read for another user's processes, so anything go2rtc spawns is invisible to it. Overall GPU utilization is unaffected.
If you mount your own TLS certificate at `/etc/letsencrypt/live/frigate`, the private key has to be readable by the runtime user, which runs nginx. Frigate hands the key to that user at startup if the mount is writable; on a read-only mount, make the key readable by uid 1000 (or your `PUID`) yourself.
If you're debugging nginx, run the config check as the runtime user with stdout discarded:
Running `nginx -t` as root hands nginx's runtime directories to root as a side effect, which breaks the running workers until the service restarts, and the config's `/dev/stdout` logs can't be reopened through a root-owned `docker exec` pipe. The results print on stderr either way.
@@ -22,8 +22,9 @@ Frigate supports multiple different detectors that work on different types of ha
**Most Hardware**
- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB, Mini PCIe, and m.2 formats allowing for a wide range of compatibility with devices.
- [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
- [Hailo](#hailo): The Hailo-8, Hailo-8L and Hailo-8R AI Acceleration modules are available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
- <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
- <CommunityBadge /> [DEEPX](#deepx-npu): The DEEPX NPU is available in m.2 format and as a HAT+ for the Raspberry Pi 5, offering broad compatibility across various platforms.
**AMD**
@@ -68,19 +69,73 @@ Frigate supports multiple different detectors that work on different types of ha
:::note
Multiple detectors can not be mixed for object detection (ex: OpenVINO and Coral EdgeTPU can not be used for object detection at the same time).
A single model can not be spread across different detector types (ex: OpenVINO and Coral EdgeTPU can not run the same model at the same time). Configuring more than one model, each on its own detector type, is supported.
This does not affect using hardware for accelerating other tasks such as [semantic search](./semantic_search.md)
:::
### Configuring models and hardware
Object detection is configured with a `models` list. Each entry describes one model and the hardware it runs on:
```yaml
models:
- devices:
- openvino:GPU
path:/config/model_cache/yolov9-s.onnx
model_type:yolo-generic
width:320
height:320
```
Each entry in `devices` is a detector type, optionally followed by a colon and a device for that detector, such as `edgetpu:pci:0`, `openvino:NPU`, or `tensorrt:0`. The per-detector sections below document the device values each one accepts. Listing several devices runs the model on all of them, and listing the **same** device more than once runs additional inference processes against it, which can improve throughput on hardware that keeps up with more than one stream:
```yaml
models:
- devices:
- openvino:GPU
- openvino:GPU
```
Coral EdgeTPU and MemryX accelerators can only be opened by one process, so those devices can not be repeated.
### Running more than one model
Cameras can be split across models by scene, which is useful when indoor and outdoor cameras benefit from differently trained models. Each model declares the `scene` it is for, and each camera picks one with `detect -> scene`:
```yaml
models:
- scene:outdoor
path:plus://your-outdoor-model
devices:
- edgetpu:pci:0
- scene:indoor
path:/config/model_cache/indoor.onnx
model_type:yolo-generic
devices:
- openvino:GPU
cameras:
driveway:
detect:
scene:outdoor
...
hallway:
detect:
scene:indoor
...
```
Available scenes are `all`, `indoor`, `outdoor`, `indoor_thermal`, and `outdoor_thermal`. A model with a scene of `all` is used by every camera that does not set one, and `all` is the default when a model does not declare a scene. Changing a camera's scene requires a restart.
### Choosing a model size
Along with picking a detector for your hardware, you will choose a model's **input resolution** (such as `320x320` or `640x640`) and, for model families like YOLOv9, a **variant size** (`tiny`, `small`, etc.). Both affect the balance between accuracy and the inference time your hardware can sustain.
**Resolution (320x320 vs 640x640):** Frigate is optimized for `320x320` models, and `320x320` is the best choice for the vast majority of setups. Frigate is specifically designed to compensate for the smaller model by cropping a region of motion from the full frame and zooming into it before running detection, so a `320x320` model is actually _better_ at small and distant objects, not worse. A `640x640` model is slower and uses more resources, and its main benefit is fitting more objects into a single inference when many objects are spread across a large area. Recent versions of Frigate have improved support for `640x640` models, but `320x320` remains the recommended starting point for nearly all setups.
**Variant size (tiny/small/medium):** Larger variants are gradually more accurate but slower. Whether the difference is noticeable depends on your specific cameras and scenes. A good rule of thumb is to use the largest model your hardware can run without skipping detections, which you can monitor on the <NavPath path="System > Metrics > Cameras" /> page in the UI. Better accuracy only helps if your detector keeps up with the detection load across all cameras.
**Variant size (tiny/small/medium):** Larger variants are gradually more accurate but slower. Whether the difference is noticeable depends on your specific cameras and scenes. A good rule of thumb is to use the largest model your hardware can run without skipping detections, which you can monitor on the <NavPath path="Health and Metrics > Cameras" /> page in the UI. Better accuracy only helps if your detector keeps up with the detection load across all cameras.
**Acceptable inference time depends on your hardware.** Inference time alone does not tell the whole story, because different hardware has different capacity. A GPU can run multiple instances of the same model concurrently, so an inference time around 30ms can still keep up with several cameras. A Google Coral runs only a single instance of the model, so it needs a much lower inference time (around 10ms) to keep up.
@@ -92,11 +147,11 @@ The best detection accuracy comes from a model trained on images that look like
# Officially Supported Detectors
Frigate provides a number of builtin detector types. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
Frigate provides a number of builtin detector types. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. Each of a model's devices runs in a dedicated process, and they pull from a common queue of detection requests from the cameras assigned to that model.
## Edge TPU Detector
The Edge TPU detector type runs TensorFlow Lite models utilizing the Google Coral delegate for hardware acceleration. To configure an Edge TPU detector, set the `"type"` attribute to`"edgetpu"`.
The Edge TPU detector type runs TensorFlow Lite models utilizing the Google Coral delegate for hardware acceleration. To use it, prefix a model's device with`edgetpu`.
The Edge TPU device can be specified using the `"device"` attribute according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api). If not set, the delegate will use the first device it finds.
@@ -111,16 +166,15 @@ See [common Edge TPU troubleshooting steps](/troubleshooting/edgetpu) if the Edg
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`.
Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown.
</TabItem>
<TabItem value="yaml">
```yaml
detectors:
coral:
type:edgetpu
device:usb
models:
- devices:
- edgetpu:usb
```
</TabItem>
@@ -131,19 +185,16 @@ detectors:
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors, specifying `usb:0` and `usb:1` as the device for each.
Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown and check each Coral the model should run on.
</TabItem>
<TabItem value="yaml">
```yaml
detectors:
coral1:
type:edgetpu
device:usb:0
coral2:
type:edgetpu
device:usb:1
models:
- devices:
- edgetpu:usb:0
- edgetpu:usb:1
```
</TabItem>
@@ -156,16 +207,15 @@ _warning: may have [compatibility issues](https://github.com/blakeblackshear/fri
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then leave the device field empty.
Navigate to <NavPath path="Settings > System > Detection models" /> and select the **Coral EdgeTPU** entry from the **Hardware** dropdown.
</TabItem>
<TabItem value="yaml">
```yaml
detectors:
coral:
type:edgetpu
device:""
models:
- devices:
- 'edgetpu:'
```
</TabItem>
@@ -176,16 +226,15 @@ detectors:
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `pci`.
Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (PCIe)** from the **Hardware** dropdown.
</TabItem>
<TabItem value="yaml">
```yaml
detectors:
coral:
type:edgetpu
device:pci
models:
- devices:
- edgetpu:pci
```
</TabItem>
@@ -196,19 +245,16 @@ detectors:
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors, specifying `pci:0` and `pci:1` as the device for each.
Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (PCIe)** from the **Hardware** dropdown and check each Coral the model should run on.
</TabItem>
<TabItem value="yaml">
```yaml
detectors:
coral1:
type:edgetpu
device:pci:0
coral2:
type:edgetpu
device:pci:1
models:
- devices:
- edgetpu:pci:0
- edgetpu:pci:1
```
</TabItem>
@@ -219,19 +265,16 @@ detectors:
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors with different device types (e.g., `usb` and `pci`).
Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown. USB and PCIe Corals are listed as separate hardware, so mixing the two on one model has to be done in YAML.
</TabItem>
<TabItem value="yaml">
```yaml
detectors:
coral_usb:
type:edgetpu
device:usb
coral_pci:
type:edgetpu
device:pci
models:
- devices:
- edgetpu:usb
- edgetpu:pci
```
</TabItem>
@@ -243,9 +286,9 @@ detectors:
---
## Hailo-8
## Hailo
This detector is available for use with both Hailo-8 and Hailo-8L AI Acceleration Modules. The integration automatically detects your hardware architecture via the Hailo CLI and selects the appropriate default model if no custom model is specified.
This detector is available for use with the Hailo-8, Hailo-8L and Hailo-8R AI Acceleration Modules. The integration identifies which of them is attached and selects the matching default model if no custom model is specified.
See the [installation docs](../frigate/installation.md#hailo-8) for information on configuring the Hailo hardware.
@@ -255,16 +298,22 @@ If no custom model is provided, the Hailo detector downloads a default model fro
:::
:::info
The HailoRT runtime is not part of the Frigate image. It is downloaded and installed into `/config/.local` the first time a Hailo detector is configured, verified against pinned checksums, and updated automatically when a Frigate release pins a new version. If the container has no internet access, see [Detector runtimes](/frigate/network_requirements#detector-runtimes) for how to provide the files yourself.
:::
### Configuration {#configuration-hailo}
When configuring the Hailo detector, you have two options to specify the model: a local **path** or a **URL**.
If both are provided, the detector will first check for the model at the given local path. If the file is not found, it will download the model from the specified URL. The model file is cached under `/config/model_cache/hailo`.
For additional ready-to-use models, please visit: https://github.com/hailo-ai/hailo_model_zoo
Hailo8 supports all models in the Hailo Model Zoo that include HailoRT post-processing. You're welcome to choose any of these pre-configured models for your implementation.
Hailo supports all models in the Hailo Model Zoo that include HailoRT post-processing. You're welcome to choose any of these pre-configured models for your implementation.
> **Note:**
> The config.path parameter can accept either a local file path or a URL ending with .hef. When provided, the detector will first check if the path is a local file path. If the file exists locally, it will use it directly. If the file is not found locally or if a URL was provided, it will attempt to download the model from the specified URL.
@@ -273,7 +322,7 @@ Hailo8 supports all models in the Hailo Model Zoo that include HailoRT post-proc
## OpenVINO Detector
The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel NPUs. To configure an OpenVINO detector, set the `"type"` attribute to`"openvino"`.
The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel NPUs. To use it, prefix a model's device with`openvino`.
The OpenVINO device to be used is specified using the `"device"` attribute according to the naming conventions in the [Device Documentation](https://docs.openvino.ai/2025/openvino-workflow/running-inference/inference-devices-and-modes.html). The most common devices are `CPU`, `GPU`, or `NPU`.
@@ -286,19 +335,18 @@ OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will al
When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
The NPU device must be passed into the container by adding `/dev/accel:/dev/accel` to the `devices` section of your compose file. Frigate grants the runtime user access to the device automatically; see [hardware device access](/configuration/non_root#hardware-device-access) if you manage device permissions yourself.
The NPU firmware is loaded by the host kernel and is not part of the Frigate image. Everything else the NPU needs is bundled in the container, so host NPU libraries should never be mounted in.
Frigate bundles a specific version of Intel's [linux-npu-driver](https://github.com/intel/linux-npu-driver/releases), and the host firmware must come from that release or a newer one. Firmware older than the bundled driver may fail with `MAPPED_INFERENCE_VERSION is NOT compatible with the ELF`, where `Expected` is the version the firmware supports and `received` is the version the bundled compiler produced. Distributions often package older firmware than the driver Frigate ships, so check the build date on the host with `sudo dmesg | grep -i vpu` and update it there if needed.
@@ -313,6 +361,12 @@ Intel NPUs cannot be used under Home Assistant OS, which does not include the NP
## Apple Silicon detector
:::warning
The Apple Silicon detector client is being reworked. Its extra options no longer have a place in the config, so only the endpoint carried in the device string is honored right now, and `request_timeout_ms` and `linger_ms` are ignored. Anything else is dropped when your config is migrated.
:::
The NPU in Apple Silicon can't be accessed from within a container, so the [Apple Silicon detector client](https://github.com/frigate-nvr/apple-silicon-detector) must first be setup. It is recommended to use the Frigate docker image with `-standard-arm64` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-standard-arm64`.
### Setup {#setup-apple-silicon}
@@ -453,11 +507,10 @@ If the correct build is used for your GPU then the GPU will be detected and used
When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
```yaml
detectors:
onnx_0:
type: onnx
onnx_1:
type: onnx
models:
- devices:
- onnx
- onnx
```
:::
@@ -470,7 +523,7 @@ detectors:
## CPU Detector (not recommended)
The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To configure a CPU based detector, set the `"type"` attribute to `"cpu"`.
The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To use it, set a model's device to `cpu`.
:::danger
@@ -480,7 +533,7 @@ The CPU detector is not recommended for general use. If you do not have GPU or E
The number of threads used by the interpreter can be specified using the `"num_threads"` attribute, and defaults to `3.`
A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with the model's `path`.
### Configuration {#configuration-cpu}
@@ -488,24 +541,6 @@ A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and
When using CPU detectors, you can add one CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.
## Deepstack / CodeProject.AI Server Detector
The Deepstack / CodeProject.AI Server detector for Frigate allows you to integrate Deepstack and CodeProject.AI object detection capabilities into Frigate. CodeProject.AI and DeepStack are open-source AI platforms that can be run on various devices such as the Raspberry Pi, Nvidia Jetson, and other compatible hardware. It is important to note that the integration is performed over the network, so the inference times may not be as fast as native Frigate detectors, but it still provides an efficient and reliable solution for object detection and tracking.
### Setup {#setup-deepstack}
To get started with CodeProject.AI, visit their [official website](https://www.codeproject.com/Articles/5322557/CodeProject-AI-Server-AI-the-easy-way) to follow the instructions to download and install the AI server on your preferred device. Detailed setup instructions for CodeProject.AI are outside the scope of the Frigate documentation.
To integrate CodeProject.AI into Frigate, configure the detector as follows:
Replace `<your_codeproject_ai_server_ip>` and `<port>` with the IP address and port of your CodeProject.AI server.
To verify that the integration is working correctly, start Frigate and observe the logs for any error messages related to CodeProject.AI. Additionally, you can check the Frigate web interface to see if the objects detected by CodeProject.AI are being displayed and tracked properly.
# Community Supported Detectors
## MemryX MX3
@@ -516,6 +551,12 @@ See the [installation docs](../frigate/installation.md#memryx-mx3) for informati
To configure a MemryX detector, simply set the `type` attribute to `memryx` and follow the configuration guide below.
:::info
The MemryX SDK is not part of the Frigate image. It is downloaded and installed into `/config/.local` the first time a MemryX detector is configured, verified against pinned checksums, and updated automatically when a Frigate release pins a new version. If the container has no internet access, see [Detector runtimes](/frigate/network_requirements#detector-runtimes) for how to provide the files yourself.
@@ -552,7 +593,7 @@ For detailed instructions on compiling models, refer to the [MemryX Compiler](ht
3. Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.
4. Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
4. Bind-mount the `.zip` file into the container and specify its path using the model's `path` in your config.
5. Update `labelmap_path` to match your custom model's labels.
@@ -572,6 +613,63 @@ For detailed instructions on compiling models, refer to the [MemryX Compiler](ht
---
## DEEPX NPU
This detector is available for use with the DEEPX NPU, both the DX-M1 M.2 module and the DX-M1M on the Sixfab AI HAT+ for the Raspberry Pi 5. The configuration below applies unchanged to either form factor. DEEPX NPU support in Frigate is developed and maintained by [Sixfab](https://sixfab.com).
See the [installation docs](../frigate/installation.md#deepx-npu) for information on installing the DEEPX kernel driver and runtime on the host and passing the NPU through to the container.
To run a model on a DEEPX NPU, list a `deepx` device on that model.
:::info
The DX-RT Python bindings are not part of the Frigate image. They are downloaded and installed into `/config/.local` the first time a DEEPX device is configured, verified against pinned checksums, and updated automatically when a Frigate release pins a new version. If the container has no internet access, see [Detector runtimes](/frigate/network_requirements#detector-runtimes) for how to provide the files yourself.
Frigate does not bundle a model for this detector. Models must be compiled to DEEPX's `.dxnn` format. Two model types are supported:
- `yolo-generic` for YOLO object detection models, the recommended default. The detector reads the model's output layout from the compiled file, so anchor-based, anchor-free and NMS-in-head models all work with the same configuration, as do models compiled with DEEPX's Post-Processing Unit (PPU) support.
- `yolox` for YOLOX models compiled without PPU support, whose raw head needs Frigate's YOLOX decoder. A YOLOX model compiled with PPU support works under either `yolox` or `yolo-generic`.
The quickest way to get one is the [DEEPX ModelZoo](https://developer.deepx.ai/modelzoo), which publishes pre-compiled `.dxnn` files for a range of YOLO object detection models. Download the `.dxnn`, bind-mount it into the container, and point the model's `path` at it. Alternatively, compile your own model with the DX-COM compiler. The recommended starting point is `yolox-s_640x640_ppu.dxnn`, the fastest ModelZoo model measured through Frigate:
For PPU models, use a `.dxnn` compiled with DX-COM 2.4.0 or later. Frigate reads the PPU head layout the compiler writes into the file and refuses to load a PPU model without it.
`model_type` must be set to `yolo-generic` or `yolox` to match the model; `yolo-generic` is the recommended default unless the model is a raw YOLOX export. Frigate defaults it to `ssd`, which this detector does not support, so the detector refuses to start on a model that leaves it unset.
`width` and `height` must match the resolution the model was compiled for. Quantization parameters are baked into the `.dxnn` file at compile time, so no normalization is applied on the host and Frigate's default `input_tensor`, `input_pixel_format`, and `input_dtype` values do not need to be overridden.
A DEEPX device is `PCIe:<index>`, as reported on the detector settings page. The NPU daemon multiplexes across processes, so the same device may be listed more than once to run additional inference processes against it:
```yaml
models:
- devices:
- deepx:PCIe:0
- deepx:PCIe:0
```
#### Label maps
The object detection models in the DEEPX ModelZoo are trained on the standard 80-class COCO label set, so `labelmap_path` must be set to `/labelmap/coco-80.txt`. Frigate's default label map uses an extended 91-class COCO scheme, and leaving it in place will cause detections to be reported as the wrong object type. For `yolo-generic` models the label map is also what the detector uses to tell the output layout, so a label map with the wrong number of classes is reported as an error at startup.
---
## NVidia TensorRT Detector
Nvidia Jetson devices may be used for object detection using the TensorRT libraries. Due to the size of the additional libraries, this detector is only provided in images with the `-tensorrt-jp6` tag suffix, e.g. `ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp6`. This detector is designed to work with Yolo models for object detection.
@@ -682,13 +780,10 @@ If no custom model is provided, the RKNN detector downloads a default model from
When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming NPU resources are available. An example configuration would be:
```yaml
detectors:
rknn_0:
type: rknn
num_cores: 0
rknn_1:
type: rknn
num_cores: 0
models:
- devices:
- rknn:0
- rknn:0
```
:::
@@ -779,6 +874,12 @@ The AXEngine detector downloads its default model from HuggingFace on first star
:::
:::info
The AXEngine python package is not part of the Frigate image. It is downloaded and installed into `/config/.local` the first time an AXEngine detector is configured, verified against a pinned checksum, and updated automatically when a Frigate release pins a new version. If the container has no internet access, see [Detector runtimes](/frigate/network_requirements#detector-runtimes) for how to provide the file yourself.
:::
### Configuration {#configuration-axengine}
When configuring the AXEngine detector, you have to specify the model name.
@@ -9,7 +9,7 @@ import NavPath from "@site/src/components/NavPath";
Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM-DD/HH/<camera_name>/MM.SS.mp4` in **UTC time**. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy. Frigate chooses the largest matching retention value between the recording retention and the tracked object retention when determining if a recording should be removed.
New recording segments are written from the camera stream to cache, they are only moved to disk if they match the setup recording retention policy.
New recording segments are written from the camera stream to cache, they are only moved to disk if they pass a validation check and match the setup recording retention policy.
:::tip
@@ -275,6 +275,165 @@ record:
This configuration will retain recording segments that overlap with alerts and detections for 10 days. Because multiple tracked objects can reference the same recording segments, this avoids storing duplicate footage for overlapping tracked objects and reduces overall storage needs.
## Sub Stream Recording
In addition to the main recording stream, Frigate can record a second, lower quality stream for each camera. This serves two purposes:
- **Quality selection during playback**: A quality selector (`Auto`, `Original`, or `Low`) appears in History view for cameras with sub stream recording enabled. `Original` and `Low` play only that stream's recordings. Time ranges where the selected stream has no footage are skipped during playback, and the selector notes when the selected stream has no recordings at all in the viewed time range. With `Auto` (the default), playback prefers the original quality and automatically falls back to the low quality stream when the connection cannot keep up, or for time ranges where the original recordings have expired. The selector shows each stream's video codec and audio details beneath the options; footage recorded by older Frigate versions shows no details.
- **Quality selection when exporting**: A `Quality` selector (`Auto`, `Original`, or `Low`) is available for cameras with sub stream recording enabled. See [exporting](#exporting-a-camera-that-records-two-streams) for details on each option.
- **Extended retention**: Sub stream recordings have their own retention settings, fully independent of the main recordings. By giving the low quality recordings a longer retention period, you can keep weeks or months of low quality history using a fraction of the storage, and that history remains playable after the main recordings expire. Playback falls back to the low quality recordings automatically, and the timeline shows a muted treatment for time ranges where only low quality footage remains. Timeline previews are kept for as long as either stream still has recordings, so scrubbing works across the whole retained history.
### Configuring sub stream recording
Sub stream recording uses the `record_sub` input role. This role can be assigned to the same input as `detect`, so in the common case where detect already uses the camera's sub stream, no additional camera connection is needed. Like the main recording stream, sub stream segments are copied directly from the camera stream without re-encoding, so the recording quality is determined by the source stream.
The following examples keep 7 days of full quality continuous recordings and 60 days of low quality continuous recordings:
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and select the camera.
- In **Camera inputs**, enable the **Record (Sub Stream)** role on the stream you want to record at low quality, commonly the same stream that has the **Detect** role. Only one stream may have this role, and it cannot be assigned to the same stream as the **Record** role.
Navigate to <NavPath path="Settings > Camera configuration > Recording" /> and select the camera.
- Set **Enable recording** to on
- Set **Continuous retention > Retention days** to `7`
- Set **Sub stream recording > Enable sub stream recording** to on
- Set **Sub stream recording > Sub stream continuous retention > Retention days** to `60`
The camera setup wizard also offers the **Record (Sub Stream)** role when assigning stream roles for a newly added camera.
</TabItem>
<TabItem value="yaml">
```yaml
cameras:
front_door:
ffmpeg:
inputs:
- path:rtsp://camera/main
roles:
- record
- path:rtsp://camera/sub
roles:
- detect
- record_sub
record:
enabled:true
continuous:
days:7
sub:
enabled:true
continuous:
days:60
```
If your camera does not provide a suitable sub stream (or the sub stream is already used at a resolution you don't want to record), you can use a go2rtc transcode as the source for `record_sub` instead:
The `record.sub` config supports the same retention structure as the main recording config: `continuous`, `motion`, `alerts`, and `detections` each with their own `days` (and `mode` for alerts and detections). The pre-capture and post-capture windows for alerts and detections are taken from the main `record.alerts` and `record.detections` config. Extending `sub.alerts.days` or `sub.detections.days` beyond the main values also keeps those review items visible in the review timeline for the longer window, with playback falling back to the low quality stream once the main recordings expire.
:::note
Recording must be enabled (`record.enabled`) for sub stream recording to run, and Frigate will fail to start if `record.sub.enabled` is set without a `record_sub` role assigned to one of the camera's inputs.
:::
### How Auto picks a quality
`Auto` measures throughput on every segment download and compares it against the original stream's bitrate (computed from the recorded footage itself). Playback drops to the low quality stream when any of these happen:
- A freeze lasts 4 seconds (10 seconds when it starts within 2 seconds of a seek, since the seek target is rarely buffered), or freezes total 7 seconds within the last minute.
- 3 downloads in a row measure below the original bitrate plus 10%, dropping quality before a stall ever becomes visible.
- No first frame appears within 10 seconds, or loading fails outright.
Playback returns to full quality only when measured throughput exceeds the original bitrate by 50%, checked continuously while playing the low quality stream and again at each new hour. The asymmetric thresholds (1.1x to drop, 1.5x to return) keep a borderline connection from switching back and forth.
The most recent measurement is remembered on the device: a connection last measured below the original bitrate (or below 3 Mbps when the bitrate is not yet known) starts playback on the low quality stream so a first frame appears immediately, then upgrades within a few segments if the speed allows.
The quality selector shows which stream Auto is currently playing and why. A browser with Data Saver enabled stays on the low quality stream, a browser that cannot decode the original stream's codec (for example H.265 without HEVC support) plays the low quality stream for that camera, and pinning `Original` or `Low` bypasses Auto entirely.
### Sub stream output args
By default the sub stream is recorded with the same [output args](/configuration/ffmpeg_presets#output-args-presets) as the main recording stream, so it inherits any customization made to `ffmpeg.output_args.record`. Setting `ffmpeg.output_args.record_sub` gives the sub stream its own args instead. Like all `ffmpeg` config, this can be set globally or per camera.
The most common reason to set this is a pair of streams whose audio differs. Many cameras send AAC on the main stream but PCM on the sub stream, and PCM cannot be copied into an mp4 recording. Copying the main stream's audio avoids re-encoding audio that is already AAC, while the sub stream still needs to be transcoded:
```yaml
ffmpeg:
output_args:
# main stream audio is already AAC, so copy it
record:preset-record-generic-audio-copy
# sub stream audio is PCM, so transcode it to AAC
record_sub:preset-record-generic-audio-aac
```
Other reasons to set this are recording a sub stream whose codec needs a different preset than the main stream, such as `preset-record-mjpeg`, or forcing a matching audio sample rate across the two streams with manual args ending in `-c:a aac -ar 16000`.
:::warning
Avoid removing audio from only one of the two streams (for example with `-an`). When one stream has audio and the other does not, playback of time ranges that combine both qualities is silent, so stripping audio from the sub stream also silences the merged timeline.
:::
### Which stream do features use?
As a general rule, features that read recordings prefer the main stream and fall back to the sub stream for time ranges where the main recordings have expired. Analytics features use only the main stream.
| Recording playback (History and Review) | Both (main preferred with sub fallback by default), or exactly one stream when a quality is selected manually |
| Tracking details and Explore clip playback | Main, falling back to sub where the main recordings have expired |
| Exports | Both (main preferred with sub fallback by default), or exactly one stream when a quality is selected in the export dialog |
| Clip downloads | Main; sub is used when no main recordings remain in the range (streams are never mixed in one file) |
| Frames grabbed from a recording in History (download snapshot, submit frame to Frigate+) | Main preferred, sub fallback |
| Audio extraction (e.g., transcription) | Main preferred, sub fallback |
| Motion search | Main only |
| Review timeline motion data | Main only |
| Storage usage statistics | Both streams counted, and listed separately per camera |
This table covers only features that read recordings from disk. Tracked object snapshots and thumbnails (the images shown in Explore and sent with notifications, and the images submitted to Frigate+ from a tracked object) are captured live from the `detect` stream as the object is tracked, never from recordings, so sub stream recording does not affect them.
### Trade-offs
- Recording a second stream increases overall storage use. The increase is typically small relative to the main recordings, since the low quality stream is much smaller. Both streams are cached before being written to disk, so cache use goes up as well. See [the `/tmp/cache` area is separate](#the-tmpcache-area-is-separate) if you start seeing `No space left on device` errors after enabling it.
- The go2rtc transcode approach continuously encodes the low quality stream, which uses CPU or GPU resources. This cost only applies to the transcode path; recording the camera's native sub stream does not re-encode. See the [go2rtc hardware acceleration documentation](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) for accelerating the transcode.
- Many camera sub streams do not include audio. If the source stream has no audio, the low quality recordings will not have audio.
- **Matching video codecs and audio settings between the two streams gives the smoothest playback.** When playback combines both qualities on one timeline (the default `Auto` behavior: for example original quality during events with low quality in between, or low quality history after the original recordings expire) and the streams use different video codecs or audio settings, for example H.265 on the main stream and H.264 on the sub stream, or 16 kHz audio on one and 8 kHz on the other, playback still works: Frigate inserts a decoder reset at each quality transition, which can cause a barely-perceptible pause there. Configuring both streams in the camera's firmware to use the same video codec, audio codec, and sample rate makes transitions fully seamless, and a mismatched audio sample rate can also be corrected with [sub stream output args](#sub-stream-output-args). If one stream has audio and the other does not, combined time ranges play **without audio**; selecting a single quality with the playback selector always keeps that stream's audio.
## Can I have "continuous" recordings, but only at certain times?
Using Frigate UI, Home Assistant, or MQTT, cameras can be automated to only record in certain situations or at certain times.
@@ -291,7 +450,7 @@ For advanced use cases, the [custom export HTTP API](../integrations/api/export-
POST /export/custom/{camera_name}/start/{start_time}/end/{end_time}
```
The request body accepts `ffmpeg_input_args` and `ffmpeg_output_args` to control encoding, frame rate, filters, and other FFmpeg options. If neither is provided, Frigate defaults to time-lapse output settings (25x speed, 30 FPS).
The request body accepts `ffmpeg_input_args` and `ffmpeg_output_args` to control encoding, frame rate, filters, and other FFmpeg options. If neither is provided, Frigate defaults to time-lapse output settings (25x speed, 30 FPS) with audio removed (`-an`). When providing your own `ffmpeg_input_args`, include `-an` if you want audio stripped from the export.
The following example exports a time-lapse at 60x speed with 25 FPS:
Normal operation may leave small numbers of orphaned files until Frigate's scheduled cleanup, but crashes, configuration changes, or upgrades may cause more orphaned files that Frigate does not clean up. This feature checks the file system for media files and removes any that are not referenced in the database.
The Maintenance pane in the Frigate UI or an API endpoint `POST /api/media/sync` can be used to trigger a media sync. When using the API, a job ID is returned and the operation continues on the server. Status can be checked with the `/api/media/sync/status/{job_id}` endpoint.
The Maintenance pane in the Frigate UI or an API endpoint `POST /api/media/sync` can be used to trigger a media sync. When using the API, a job ID is returned and the operation continues on the server. Status can be checked with the `/api/media/sync/status/{job_id}` endpoint. Results include the disk space reclaimed, or with `dry_run: true`, the space that would be reclaimed.
Setting `verbose: true` writes a detailed report of every orphaned file and database entry to `/config/media_sync/<job_id>.txt`. For recordings, the report separates orphaned database entries (DB records whose files are missing from disk) from orphaned files (files on disk with no corresponding database record).
@@ -358,7 +517,7 @@ The storage usage Frigate reports will not exactly match what the operating syst
### How Frigate measures recording usage
The **Recordings** value on the Storage Metrics page (<NavPath path="System > Storage" />), and the per-camera **Camera Storage** breakdown, is the sum of the recording segment sizes Frigate has written, taken from Frigate's database. It is **not** computed by a scan of the disk. Frigate tracks usage this way by design: repeatedly walking the entire drive to total its size would keep hard drives spun up and add unnecessary I/O.
The **Recordings** value on the Storage Metrics page (<NavPath path="Health and Metrics > Storage" />), and the per-camera **Camera Storage** breakdown, is the sum of the recording segment sizes Frigate has written, taken from Frigate's database. It is **not** computed by a scan of the disk. Frigate tracks usage this way by design: repeatedly walking the entire drive to total its size would keep hard drives spun up and add unnecessary I/O.
The disk **total** shown beside it, and the free-space figure Frigate uses to decide when to delete recordings, instead come from the operating system's report for the whole filesystem mounted at `/media/frigate`. As a result, the **Unused** value on the page is _total disk capacity minus Frigate's recordings_, not the drive's real free space, which will be lower whenever anything else is stored on the disk.
@@ -197,7 +197,7 @@ For cameras that support two-way talk, go2rtc will automatically establish an au
To prevent this, you must configure two separate stream instances:
1. One stream instance with `#backchannel=0` for Frigate's viewing, recording, and detection (prevents go2rtc from establishing the blocking backchannel)
2. A second stream instance without`#backchannel=0` for two-way talk functionality (can be used by Frigate's WebRTC viewer or other applications)
2. A second stream instance with no`#` parameters at all for two-way talk functionality (can be used by Frigate's WebRTC viewer or other applications)
Configuration example:
@@ -215,13 +215,15 @@ In this configuration:
- `front_door` stream is used by Frigate for viewing, recording, and detection. The `#backchannel=0` parameter prevents go2rtc from establishing the audio output backchannel, so it won't block two-way talk access.
- `front_door_twoway` stream is used for two-way talk functionality. This stream can be used by Frigate's WebRTC viewer when two-way talk is enabled, or by other applications (like Home Assistant Advanced Camera Card) that need access to the camera's audio output channel.
Any `#` parameter on a bare `rtsp://` source disables the backchannel unless the URL explicitly contains `#backchannel=1`. A two-way talk stream with something like `#video=h264` on it silently loses two-way audio, and Frigate will report that two-way talk is unavailable for that stream.
## Security: Restricted Stream Sources
For security reasons, the `echo:`, `expr:`, and `exec:` stream sources are disabled by default in go2rtc. These sources allow arbitrary command execution and can pose security risks if misconfigured.
If you attempt to use these sources in your configuration, the streams will be removed and an error message will be printed in the logs.
To enable these sources, you must set the environment variable `GO2RTC_ALLOW_ARBITRARY_EXEC=true`. This can be done in your Docker Compose file or container environment:
To enable these sources, you must set the environment variable `GO2RTC_ALLOW_ARBITRARY_EXEC=true`. This can be done in your Docker Compose file or container environment, or for Home Assistant App users with the `go2rtc_allow_arbitrary_exec` option in the App's configuration. The `environment_vars` section of the Frigate config can't enable it:
@@ -9,7 +9,7 @@ import NavPath from "@site/src/components/NavPath";
# TLS
Frigate's integrated NGINX server supports TLS certificates. By default Frigate will generate a self signed certificate that will be used for port 8971. Frigate is designed to make it easy to use whatever tool you prefer to manage certificates.
Frigate's integrated NGINX server supports TLS certificates. By default Frigate will generate a self signed certificate that will be used for port 8971, stored in `/config/tls` so it survives container recreation. Frigate is designed to make it easy to use whatever tool you prefer to manage certificates.
Frigate is often running behind a reverse proxy that manages TLS certificates for multiple services. You will likely need to set your reverse proxy to allow self signed certificates or you can disable TLS in Frigate's config. However, if you are running on a dedicated device that's separate from your proxy or if you expose Frigate directly to the internet, you may want to configure TLS with valid certificates.
@@ -45,7 +45,9 @@ frigate:
...
```
Within the folder, the private key is expected to be named `privkey.pem` and the certificate is expected to be named `fullchain.pem`.
Within the folder, the private key is expected to be named `privkey.pem` and the certificate is expected to be named `fullchain.pem`. Mounted certificates take precedence over the self signed pair in `/config/tls`.
`privkey.pem` must be readable by the runtime user that runs NGINX. Frigate hands it over at startup when the mount is writable; on a `:ro` mount, make it readable by uid 1000 (or your `PUID`) yourself. See [Running as a non-root user](/configuration/non_root).
Note that certbot uses symlinks, and those can't be followed by the container unless it has access to the targets as well, so if using certbot you'll also have to mount the `archive` folder for your domain, e.g.:
@@ -59,7 +61,7 @@ frigate:
```
Frigate automatically compares the fingerprint of the certificate at `/etc/letsencrypt/live/frigate/fullchain.pem` against the fingerprint of the TLS cert in NGINX every minute. If these differ, the NGINX config is reloaded to pick up the updated certificate.
Frigate automatically compares the fingerprint of the certificate it loaded, from either location, against the fingerprint of the TLS cert in NGINX every minute. If these differ, the NGINX config is reloaded to pick up the updated certificate.
If you issue Frigate valid certificates you will likely want to configure it to run on port 443 so you can access it without a port number like `https://your-frigate-domain.com` by mapping 8971 to 443.
@@ -73,4 +75,4 @@ frigate:
## ACME Challenge
Frigate also supports hosting the acme challenge files for the HTTP challenge method if needed. The challenge files should be mounted at `/etc/letsencrypt/www`.
Frigate also supports hosting the acme challenge files for the HTTP challenge method if needed. The challenge files should be mounted at `/etc/letsencrypt/www`. With a read-only root filesystem this has to be a mounted volume, since Frigate cannot create the directory itself.
@@ -204,11 +204,20 @@ Light guidelines and advice:
npm run lint
```
- Add to unit tests and ensure they pass. As much as possible, you should strive to _increase_ test coverage whenever making changes. This will help ensure features do not accidentally become broken in the future.
- If you run into error messages like "TypeError: Cannot read properties of undefined (reading 'context')" when running tests, this may be due to these issues (https://github.com/vitest-dev/vitest/issues/1910, https://github.com/vitest-dev/vitest/issues/1652) in vitest, but I haven't been able to resolve them.
- Ensure the backend [unit tests](#unit-tests) pass. Your PR cannot be merged unless tests pass.
```shell
python3 -u -m unittest
```
- Ensure the end-to-end tests pass. They run in Playwright against a production build with mocked API data, so they don't need a running Frigate instance. Add or update tests in `web/e2e/specs/` when you change UI behavior.
```console
npm run test
# First-time setup
npx playwright install chromium
# Build the app and run all tests
npm run e2e:build && npm run e2e
```
- Test in different browsers. Firefox, Chrome, and Safari all have different quirks that make them unique targets to interact with.
@@ -54,7 +54,7 @@ Frigate supports multiple different detectors that work on different types of ha
**Most Hardware**
- [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices offering a wide range of compatibility with devices.
- [Hailo](#hailo-8): The Hailo-8, Hailo-8L and Hailo-8R AI Acceleration modules are available in m.2 format with a HAT for RPi devices offering a wide range of compatibility with devices.
- [Supports many model architectures](../../configuration/object_detectors#configuration-hailo)
- Runs best with tiny or small size models
@@ -65,6 +65,11 @@ Frigate supports multiple different detectors that work on different types of ha
- [Supports many model architectures](../../configuration/object_detectors#memryx-mx3)
- Runs best with tiny, small, or medium-size models
- <CommunityBadge /> [DEEPX](#deepx-npu): The DEEPX NPU is available in m.2 format and as a HAT+ for the Raspberry Pi 5, allowing for a wide range of compatibility with devices.
- [Supports YOLO model architectures](../../configuration/object_detectors#deepx-npu)
- Runs best with tiny or small size models
- Runs efficiently on low power hardware
**AMD**
- [ROCm](#rocm---amd-gpu): ROCm can run on AMD Discrete GPUs to provide efficient object detection
@@ -111,12 +116,13 @@ Frigate supports multiple different detectors that work on different types of ha
### Hailo-8
Frigate supports both the Hailo-8 and Hailo-8L AI Acceleration Modules on compatible hardware platforms, including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate automatically identifies your hardware type and selects the appropriate default model when a custom model isn’t provided.
Frigate supports the Hailo-8, Hailo-8L and Hailo-8R AI Acceleration Modules on compatible hardware platforms, including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate identifies which of them is attached and selects the matching default model when a custom model isn’t provided.
**Default Model Configuration:**
- **Hailo-8L:** Default model is **YOLOv6n**.
- **Hailo-8:** Default model is **YOLOv6n**.
- **Hailo-8L:** Default model is **YOLOv6n**, compiled for the Hailo-8L.
- **Hailo-8:** Default model is **YOLOv6n**, compiled for the Hailo-8.
- **Hailo-8R:** Default model is the **Hailo-8** build of **YOLOv6n**, since the Hailo Model Zoo publishes no Hailo-8R build.
In real-world deployments, even with multiple cameras running concurrently, Frigate has demonstrated consistent performance. Testing on x86 platforms, with dual PCIe lanes, yields further improvements in FPS, throughput, and latency compared to the Raspberry Pi setup.
@@ -256,6 +262,32 @@ The MX3 is a pipelined architecture, where the maximum frames per second support
Inference speeds may vary depending on the host platform. The above data was measured on an **Intel 13700 CPU**. Platforms like Raspberry Pi, Orange Pi, and other ARM-based SBCs have different levels of processing capability, which may limit total FPS.
### DEEPX NPU
Frigate supports the DEEPX NPU in both of its form factors: the **DX-M1** M.2 module, which works on x86 (Intel/AMD) and ARM-based SBCs such as the Raspberry Pi 5, and the **DX-M1M** on the [Sixfab AI HAT+](https://docs.sixfab.com/docs/ai-hat-plus-raspberry-pi-5-quickstart) for the Raspberry Pi 5. Both use the same driver and runtime, so the configuration is identical for either one. DEEPX NPU support in Frigate is developed and maintained by [Sixfab](https://sixfab.com).
The DEEPX driver and runtime run on the Docker host rather than inside the Frigate container and must be installed before the NPU can be used. See the [installation docs](installation.md#deepx-npu) for the setup steps and [the detector docs](/configuration/object_detectors#deepx-npu) for the configuration.
Frigate does not bundle a model for this detector. Models use DEEPX's `.dxnn` format, and pre-compiled YOLO models can be downloaded from the [DEEPX ModelZoo](https://developer.deepx.ai/modelzoo). Prefer a model with a `_ppu` suffix whenever one is available for the architecture you want: these run part of the post-processing on the NPU itself and are considerably faster, roughly 2.5x for the same architecture and input size. **YOLOX-S with PPU is the recommended starting point.**
Inference times for a few recommended models, measured through Frigate's own stats on a DX-M1:
Other ModelZoo YOLO variants are also supported but have not been measured. Inference speeds vary with the host platform, so a slower host such as a Raspberry Pi 5 will report higher times than those above.
:::note
A few ModelZoo models can not be used with Frigate: SSD models (they are trained on Pascal VOC, so their labels do not match Frigate's), DAMO-YOLO models, face and pose models, and the PPU builds of YOLOv7.
:::
### Nvidia Jetson
Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
@@ -122,7 +122,9 @@ Additionally, the USB Coral draws a considerable amount of power. If using any o
### Hailo-8
The Hailo-8 and Hailo-8L AI accelerators are available in both M.2 and HAT form factors for the Raspberry Pi. The M.2 version typically connects to a carrier board for PCIe, which then interfaces with the Raspberry Pi 5 as part of the AI Kit. The HAT version can be mounted directly onto compatible Raspberry Pi models. Both form factors have been successfully tested on x86 platforms as well, making them versatile options for various computing environments.
The Hailo-8, Hailo-8L and Hailo-8R AI accelerators are available in both M.2 and HAT form factors for the Raspberry Pi. The M.2 version typically connects to a carrier board for PCIe, which then interfaces with the Raspberry Pi 5 as part of the AI Kit. The HAT version can be mounted directly onto compatible Raspberry Pi models. Both form factors have been successfully tested on x86 platforms as well, making them versatile options for various computing environments.
The HailoRT runtime is not part of the Frigate image; Frigate downloads and installs it at first start once a Hailo detector is configured. Containers without internet access can provide the files themselves, see [Detector runtimes](/frigate/network_requirements#detector-runtimes).
#### Installation
@@ -298,7 +300,7 @@ If you are using `docker run`, add this option to your command `--device /dev/ha
#### Configuration
Finally, configure [hardware object detection](/configuration/object_detectors#hailo-8) to complete the setup.
Finally, configure [hardware object detection](/configuration/object_detectors#hailo) to complete the setup.
### MemryX MX3
@@ -315,6 +317,8 @@ The MemryX MX3 Accelerator is available in the M.2 2280 form factor (like an NVM
To get started with MX3 hardware setup for your system, refer to the [Hardware Setup Guide](https://developer.memryx.com/2p1/get_started/install_hardware.html).
The MemryX SDK used inside the container is not part of the Frigate image; Frigate downloads and installs it at first start once a MemryX detector is configured. Containers without internet access can provide the file themselves, see [Detector runtimes](/frigate/network_requirements#detector-runtimes). The host side driver still has to be installed as described below.
Then follow these steps for installing the correct driver/runtime configuration:
1. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/dev/docker/memryx/user_installation.sh).
@@ -377,6 +381,99 @@ If you can't use Docker Compose, you can run the container with something simila
Finally, configure [hardware object detection](/configuration/object_detectors#memryx-mx3) to complete the setup.
### DEEPX NPU
The DEEPX NPU is available in two form factors, and Frigate supports both:
- **DX-M1** in the M.2 2280 form factor (like an NVMe SSD), for x86 (Intel/AMD) PCs, the Raspberry Pi 5, and other ARM SBCs with an exposed PCIe M.2 slot.
- **DX-M1M** on the [Sixfab AI HAT+](https://docs.sixfab.com/docs/ai-hat-plus-raspberry-pi-5-quickstart), a HAT+ board that connects to the Raspberry Pi 5 over PCIe Gen 3 x1.
Both present the NPU through the same PCIe driver and DX-RT runtime, so the setup below and the detector configuration are identical for either one. Nothing needs to change when moving between them.
DEEPX NPU support in Frigate is developed and maintained by [Sixfab](https://sixfab.com).
#### Versions
A DEEPX install has several separately versioned pieces, and they all have to agree. The driver, the runtime, and the daemon live on the Docker host; Frigate itself carries only the Python bindings, which it downloads on first start:
| Component | Version | Installed on | Installed by |
| NPU firmware | `v2.7.4` | The module | Flashed from the host |
| DX-RT bindings | `v3.4.0` | Frigate | Downloaded at first start |
:::warning
A version mismatch does not produce a startup error. It typically shows up as inference requests that are accepted but never return a result, so detections simply stop appearing while Frigate looks healthy. If that happens after a Frigate upgrade, check every version in the table before anything else.
:::
The installation script installs the DX-RT runtime on the host and enables `dxrt.service`, so the daemon starts at boot and any other program on the host can share the NPU with Frigate. Check the firmware version with `dxrt-cli --status` and update the module if it does not match the table above.
#### Installation
The DEEPX kernel driver must be installed on the host rather than in the container, because containers share the host kernel and cannot load kernel modules. Installing it creates the `/dev/dxrt*` device nodes that are passed through to Frigate. The same script installs the DX-RT runtime and enables `dxrt.service`, the daemon that owns the NPU and hands work to it on behalf of Frigate and anything else on the host.
1. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/dev/docker/deepx/user_installation.sh).
2. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
3. Run the script with `./user_installation.sh`
4. **Restart your computer** to complete driver installation.
Confirm the NPU is visible before continuing:
```bash
ls /dev/dxrt*
```
Then confirm the daemon is running and listening in `/run/dxrt`:
```bash
systemctl is-active dxrt.service
ls /run/dxrt/
```
#### Setup
To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
#### Docker configuration
Frigate needs the NPU device node and the directory holding the daemon's socket:
```yaml
services:
frigate:
devices:
- /dev/dxrt0:/dev/dxrt0
volumes:
- /run/dxrt:/run/dxrt
```
If you can't use Docker Compose, add `--device /dev/dxrt0:/dev/dxrt0 -v /run/dxrt:/run/dxrt` to your `docker run` command.
Add one `--device` per NPU, contiguously from `/dev/dxrt0`, since the client stops enumerating at the first gap.
The installation script configures `dxrt.service` to place its socket in `/run/dxrt` through a systemd drop-in. Mounting the directory rather than the socket file means the container sees the new socket after `dxrt.service` is restarted, rather than holding on to a deleted one.
`dxrtd` listens on an abstract socket as well, but that one does not cross into a container, so Frigate names the filesystem socket through `DXRT_DYNAMIC_IPC_ENDPOINT` on your behalf. Set that variable on the container yourself only if the daemon listens somewhere else, which means you also set it for `dxrtd` through its own systemd drop-in. The script writes `/etc/systemd/system/dxrt.service.d/frigate.conf` for exactly that, and has `dxrt.service` link the socket to `/tmp/dxrt_dynamic_ipc.sock` when it starts, so the host's own `dxrt-cli` and `dxtop` keep finding it at the default path they fall back to.
:::note
The DX-RT client exits when `dxrt.service` stops, so restart the Frigate container after restarting `dxrt.service`.
:::
The device node is needed as well as the socket, because the client opens the NPU directly even though the daemon arbitrates access. Without it, inference fails with `Device not found`.
`/dev/shm` does not need sharing.
The DX-RT python bindings are not shipped in the Frigate image. Frigate downloads them on first start when a DEEPX detector is configured, and caches them under `/config`.
#### Configuration
Finally, configure [hardware object detection](/configuration/object_detectors#deepx-npu) to complete the setup.
### Rockchip platform
Make sure that you use a linux distribution that comes with the rockchip BSP kernel 5.10 or 6.1 and necessary drivers (especially rkvdec2 and rknpu). To check, enter the following commands:
@@ -479,6 +576,8 @@ Follow these steps for installation:
To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
The AXEngine python package is not part of the Frigate image; Frigate downloads and installs it at first start once an AXEngine detector is configured. Containers without internet access can provide the file themselves, see [Detector runtimes](/frigate/network_requirements#detector-runtimes).
Next, grant Docker permissions to access your hardware by adding the following lines to your `docker-compose.yml` file:
```yaml
@@ -514,7 +613,7 @@ Generate a Frigate Docker Compose configuration based on your hardware and requi
services:
frigate:
container_name: frigate
privileged: true # this may not be necessary for all setups
# privileged: true # ONLY enable if your hardware requires it (see hardware-specific docs); prefer the device mappings below
restart: unless-stopped
stop_grace_period: 30s # allow enough time to shut down the various services
image: ghcr.io/blakeblackshear/frigate:stable
@@ -546,6 +645,30 @@ services:
</TabItem>
</Tabs>
### Recommended security options
Frigate does not need elevated container privileges for most setups. The following hardens the container; add the `devices`/`group_add` entries your hardware requires (see the hardware acceleration docs):
```yaml
services:
frigate:
...
security_opt:
- no-new-privileges:true
cap_drop:
- ALL
```
:::note
`telemetry.stats.network_bandwidth` uses nethogs, which requires root with NET_ADMIN/NET_RAW capabilities. If you enable that stat, omit `cap_drop: [ALL]` or add `cap_add: [NET_ADMIN, NET_RAW]`.
Platforms that genuinely require `privileged: true` (MemryX, some QNAP setups) are called out in their own sections and are unaffected by this guidance.
:::
Frigate's services run as an unprivileged user inside the container. See [Running as a non-root user](../configuration/non_root.md) for the run modes, the one time volume ownership migration, what each accelerator needs on the host, and the [hardened deployment](../configuration/non_root.md#hardened-deployment) layout with a read-only root filesystem.
**Docker CLI**
If you can't use Docker Compose, you can run the container with something similar to this:
@@ -612,6 +735,8 @@ Home Assistant OS users can install via the App repository.
5. Start the App
6. Use the _Open Web UI_ button to access the Frigate UI, then click in the _cog icon_ > _Configuration editor_ and configure Frigate to your liking
App users who can't set container environment variables can put `FRIGATE_` values in a `secrets.yaml` next to `config.yml` in `/addon_configs/<addon_directory>` instead. See [`secrets.yaml`](../configuration/advanced/system.md#secretsyaml).
| [AXERA AXEngine](/configuration/object_detectors) | Detection model | HuggingFace |
:::note
@@ -56,6 +56,24 @@ The default CPU, EdgeTPU, and OpenVINO object detection models are bundled into
:::
### Detector Runtimes
The SDKs for a few hardware detectors are not shipped in the Frigate image. They are downloaded the first time that detector is configured, verified against checksums pinned in the Frigate release, and installed into the Frigate user's home directory (`/config/.local` by default). Once installed they are not downloaded again until a Frigate release pins a new version.
If the container cannot reach GitHub, provide the files yourself:
1. Download the files for your architecture on a machine with internet access.
2. Place them, with exactly the file names listed above, in `/config/model_cache/runtimes/<detector>/`, where `<detector>` is the detector named in your config's `devices` (`hailo`, `memryx`, or `axengine`).
3. Start Frigate. Files whose checksum matches are installed without any download; a file with the wrong checksum is discarded and downloaded again, so a failed startup log names the file to replace.
The `GITHUB_ENDPOINT` mirror variable below applies to these downloads as well.
### Preventing Model Downloads
If you have already downloaded all required models and want to prevent Frigate from attempting any outbound connections to HuggingFace or the Transformers library, set the following environment variables on your Frigate container:
@@ -79,7 +97,7 @@ If your Frigate instance has restricted internet access, you can point model dow
| `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` | Unset (Keras uses its own default) | Custom classification training |
@@ -124,16 +142,12 @@ When [notifications](/configuration/notifications) are enabled and users have re
If an [MQTT broker](/integrations/mqtt) is configured, Frigate maintains a connection to the broker's host and port. This is typically a local network connection, but will require internet if you use a cloud-hosted MQTT broker.
### DeepStack / CodeProject.AI
When using the [DeepStack detector plugin](/configuration/object_detectors), Frigate sends images to the configured API endpoint for inference. This is typically local but depends on where the service is hosted.
## WebRTC (STUN)
For [WebRTC live streaming](/configuration/live), Frigate uses STUN for NAT traversal:
- **go2rtc** defaults to a local STUN listener (`stun:8555`), no internet required.
- **The web UI's WebRTC player** includes a fallback to Google's public STUN server (`stun:stun.l.google.com:19302`), which requires internet.
- **The web UI** uses the servers in `go2rtc.webrtc.ice_servers` for its WebRTC player and for the WebRTC connectivity check it runs when the Live view loads. If none are set, it uses Google's public STUN server (`stun:stun.l.google.com:19302`), which requires internet access from the browser. Set `ice_servers` to a STUN or TURN server on your network to avoid this.
@@ -60,7 +60,7 @@ If you’re running Frigate via Docker (recommended method), follow these steps:
```bash
docker logs frigate
```
- Visit the Frigate Web UI (default: `http://<your-ip>:5000`) to confirm the new version is running. The version number is displayed at the top of the System Metrics page.
- Visit the Frigate Web UI (default: `http://<your-ip>:5000`) to confirm the new version is running. The version number is displayed at the top of the Health and Metrics page.
import ConfigTabs from "@site/src/components/ConfigTabs";
import Tabs from "@theme/Tabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
@@ -132,21 +133,68 @@ services:
- "8554:8554" # RTSP feeds
```
Now you should be able to start Frigate by running `docker compose up -d` from within the folder containing `docker-compose.yml`. On startup, an admin user and password will be created and outputted in the logs. You can see this by running `docker logs frigate`. Frigate should now be accessible at `https://server_ip:8971` where you can login with the `admin` user and finish configuration using the Settings UI.
Now you should be able to start Frigate by running `docker compose up -d` from within the folder containing `docker-compose.yml`. On startup, an admin user and password will be created and outputted in the logs. You can see this by running `docker logs frigate`. Frigate should now be accessible at `https://server_ip:8971` where you can login with the `admin` user. With no cameras configured yet, the setup wizard runs on first login and walks you through the rest.
## Configuring Frigate
This section assumes that you already have an environment setup as described in [Installation](../frigate/installation.md). You should also configure your cameras according to the [camera setup guide](/frigate/camera_setup). Pay particular attention to the section on choosing a detect resolution.
### Step 1: Start Frigate
<Tabs
groupId="setup-method"
defaultValue="wizard"
values={[
{ label: "Setup wizard", value: "wizard" },
{ label: "Manual", value: "manual" },
]}
> <TabItem value="wizard">
The first time you open Frigate with no cameras configured, the setup wizard walks you through the basics. Every step can be skipped, everything it sets can be changed later in Settings, and once you finish or dismiss it, it doesn't come back.
:::note
Frigate only sees hardware that has been passed into the container. If you plan to use a GPU, a Coral, or another accelerator, add the device to your `docker-compose.yml` and restart before running the wizard, otherwise it won't appear in the detection or hardware acceleration steps. The Manual tab shows the device entries for an Intel or AMD GPU and for a Coral, and the [hardware acceleration](../configuration/hardware_acceleration_video.md) and [object detectors](../configuration/object_detectors.md) docs cover the rest.
:::
**Account**
Set a password for the `admin` account to replace the generated one from the logs, and add accounts for anyone else who needs access. This step is hidden if you have turned authentication off.
**Add a camera**
Opens the [Add Camera Wizard](../configuration/cameras.md#adding-a-camera-with-the-add-camera-wizard), which connects to the camera, tests each stream, and writes its configuration for you. You can add more than one before moving on.
**Object detection**
Lists the detection hardware Frigate found on your system, such as a Coral, an Intel GPU or NPU, or a discrete GPU, and configures the one you pick. NVIDIA and AMD GPUs need a model before detection can start, so the wizard offers your Frigate+ models if you have them, or lets you finish setup and add one later under <NavPath path="Settings > System > Detection models" />.
**Hardware acceleration**
Offers only the decoding methods your hardware supports. Auto picks one based on that hardware and the codec your camera sends, so a mixed h264 and h265 setup gets the right preset per camera.
**Recording**
Choose whether to record only when something is detected or around the clock, and how long to keep it.
The last screen summarizes what was set up. If a step changed something that needs a restart, the button restarts Frigate and returns you to the Live view once it is back.
The wizard configures the essentials only. Motion masks are not included and should be set up afterward, once you can identify the areas of the frame that trigger unwanted motion. See the [masks documentation](../configuration/masks.md). Zones, tracked object types, notifications, and MQTT are also configured in Settings.
</TabItem>
<TabItem value="manual">
On a new install the setup wizard opens first. Click **Skip setup and configure manually** on its welcome screen to dismiss it, and the steps below apply. The wizard won't come back once dismissed.
**Step 1: Start Frigate**
At this point you should be able to start Frigate and a basic config will be created automatically.
### Step 2: Add a camera
**Step 2: Add a camera**
Click the **Add Camera** button in <NavPath path="Settings > Global configuration > Camera management" /> to use the camera setup wizard to get your first camera added into Frigate. See [Adding a camera with the Add Camera Wizard](../configuration/cameras.md#adding-a-camera-with-the-add-camera-wizard) for a walkthrough of each step.
Now that you have a working camera configuration, set up hardware acceleration to minimize the CPU required to decode your video streams. See the [hardware acceleration](../configuration/hardware_acceleration_video.md) docs for examples applicable to your hardware.
@@ -190,7 +238,7 @@ cameras:
</TabItem>
</ConfigTabs>
### Step 4: Configure detectors
**Step 4: Configure detectors**
By default, Frigate will use a single OpenVINO detector running on the CPU.
@@ -204,8 +252,8 @@ You need to refer to **Configure hardware acceleration** above to enable the con
<ConfigTabs>
<TabItem value="ui">
1. Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type**`OpenVINO` and **Device**`GPU`
2. On the same page, in the **Custom Model** tab, configure the model settings for OpenVINO:
1. Navigate to <NavPath path="Settings > System > Detection models" /> and select **Intel GPU** from the **Hardware** dropdown
2. On the same model, open the **Custom Model** tab and configure the model settings for OpenVINO:
@@ -222,15 +270,12 @@ You need to refer to **Configure hardware acceleration** above to enable the con
```yaml {3-6,9-15,20-21}
mqtt: ...
detectors: # <---- add detectors
ov:
type: openvino # <---- use openvino detector
device: GPU
# We will use the default MobileNet_v2 model from OpenVINO.
model:
width: 300
height: 300
models: # <---- add models
- devices:
- openvino:GPU # <---- use the openvino detector on the GPU
# We will use the default MobileNet_v2 model from OpenVINO.
width: 300
height: 300
input_tensor: nhwc
input_pixel_format: bgr
path: /openvino-model/ssdlite_mobilenet_v2.xml
@@ -273,7 +318,7 @@ services:
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type**`EdgeTPU` and **Device**`usb`.
Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown.
</TabItem>
<TabItem value="yaml">
@@ -281,10 +326,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a
```yaml {3-6,11-12}
mqtt: ...
detectors: # <---- add detectors
coral:
type: edgetpu
device: usb
models: # <---- add models
- devices:
- edgetpu:usb
cameras:
name_of_your_camera:
@@ -303,7 +347,7 @@ More details on available detectors can be found [here](../configuration/object_
Restart Frigate and you should start seeing detections for `person`. If you want to track other objects, they can be configured in <NavPath path="Settings > Global configuration > Objects" /> or via the [configuration file reference](../configuration/advanced/reference.md).
### Step 5: Setup motion masks
**Step 5: Setup motion masks**
Now that you have optimized your configuration for decoding the video stream, you will want to check to see where to implement motion masks. Click on the camera from the main dashboard, then select the gear icon in the top right, enable the [Debug view](/usage/live#the-single-camera-view), and finally enable the switch for Motion Boxes. Watch for areas that continuously trigger unwanted motion to be detected. Common areas to mask include camera timestamps and trees that frequently blow in the wind. The goal is to avoid wasting object detection cycles looking at these areas.
@@ -321,10 +365,9 @@ If you are using YAML to configure Frigate instead of the UI, your configuration
In order to review activity in the Frigate UI, recordings need to be enabled.
@@ -357,7 +400,7 @@ In order to review activity in the Frigate UI, recordings need to be enabled.
```yaml {16-17}
mqtt: ...
detectors: ...
models: ...
cameras:
name_of_your_camera:
@@ -390,7 +433,10 @@ If you only plan to use Frigate for recording, it is still recommended to define
By default, Frigate will retain video of all tracked objects for 10 days. The full set of options for recording can be found [here](../configuration/advanced/reference.md).
### Step 7: Complete config
</TabItem>
</Tabs>
### Complete config
At this point you have a complete config with basic functionality.
@@ -304,7 +304,7 @@ Topic with current state of notifications. Published values are `ON` and `OFF`.
### `frigate/<camera_name>/status/<role>`
Publishes the current health status of each role that is enabled (`audio`, `detect`, `record`). Possible values are:
Publishes the current health status of each role that is enabled (`audio`, `detect`, `record`, `record_sub`). `record_sub` is only published for cameras with [sub stream recording](/configuration/record#sub-stream-recording) enabled, and is tracked separately from `record` so a healthy main stream can't hide a stalled sub stream. Possible values are:
- `online`: Stream is running and being processed
- `offline`: Stream is offline and is being restarted
@@ -553,22 +553,25 @@ must be enabled in the configuration.
Topic with current state of Birdseye for a camera. Published values are `ON` and `OFF`.
### `frigate/<camera_name>/birdseye_mode/set`
### `frigate/<camera_name>/birdseye_modes/set`
Topic to set Birdseye mode for a camera. Birdseye offers different modes to customize under which circumstances the camera is shown.
Topic to set the Birdseye activity types for a camera. Send one uppercase activity type or combine multiple types with commas, for example `MOTION,ALERTS`.
_Note: Changing the value from `CONTINUOUS`-> `MOTION | OBJECTS` will take up to 30 seconds for
_Note: Changing the value from `CONTINUOUS`to non-continuous activity types will take up to 30 seconds for
| `MOTION`| Shown if motion was detected within the last 30 seconds |
| `ALL_OBJECTS` | Shown if a tracked object was present within the last 30 seconds |
| `ALERTS` | Shown while an alert review item is in progress |
| `DETECTIONS` | Shown while a detection review item is in progress |
| `NONE` | Never included |
### `frigate/<camera_name>/birdseye_mode/state`
### `frigate/<camera_name>/birdseye_modes/state`
Topic with current state of the Birdseye mode for a camera. Published values are`CONTINUOUS`, `MOTION`, `OBJECTS`.
Topic with the current Birdseye activity types for a camera. Multiple enabled types are published as a comma-separated value in the order`CONTINUOUS`, `MOTION`, `ALL_OBJECTS`, `ALERTS`, `DETECTIONS`. `NONE` is published when no activity types are enabled.
@@ -59,13 +59,12 @@ You can view all of your submitted images at [https://plus.frigate.video](https:
Once you have [requested your first model](../plus/first_model.md) and gotten your own model ID, it can be used with a special model path. No other information needs to be configured for Frigate+ models because it fetches the remaining config from Frigate+ automatically.
You can either choose the new model from the <NavPath path="Settings > System > Detectors and model" /> pane in the Frigate UI (the **Frigate+ Model** tab), or manually set the model at the root level in your config:
You can either choose the new model from the <NavPath path="Settings > System > Detection models" /> pane in the Frigate UI (on the **Frigate+** tab of the model you want to change), or set it on that model in your config:
```yaml
detectors: ...
model:
path: plus://<your_model_id>
models:
- devices: ...
path: plus://<your_model_id>
```
:::note
@@ -79,10 +78,11 @@ Models are downloaded into the `/config/model_cache` folder and only downloaded
If needed, you can override the labelmap for Frigate+ models. This is not recommended as renaming labels will break the Submit to Frigate+ feature if the labels are not available in Frigate+.
@@ -30,16 +30,15 @@ Models available in Frigate+ can be used with a special model path. No other inf
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Detectors and model" />. In the **Detection Model** section, choose the **Frigate+** tab. Select your new Frigate+ model from the **Available Frigate+ models** dropdown, then click **Save**. Restart Frigate to apply the change.
Navigate to <NavPath path="Settings > System > Detection models" />. On the model you want to change, choose the **Frigate+** tab and select your new Frigate+ model from the **Available Frigate+ models** dropdown, then click **Save**. Restart Frigate to apply the change.
@@ -41,7 +41,7 @@ Rockchip models are automatically converted as of 0.17. For 0.16, YOLOv9 onnx mo
## Supported detector types
Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), ONNX (`onnx`), Hailo (`hailo8l`), and Rockchip (`rknn`) detectors.
Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), ONNX (`onnx`), Hailo (`hailo`), and Rockchip (`rknn`) detectors.
| Hardware | Recommended Detector Type | Recommended Model Type |
@@ -66,17 +66,19 @@ An FFmpeg message meaning it probed the stream but never saw enough decodable vi
## Recording
<FaqItem id="no-new-recording-segments" question="No new recording segments were created for <camera> in the last 120s">
<FaqItem id="no-new-recording-segments" question="No new recording segments were created (or: No new valid recording segments were created / No valid segments created since last invalid segment) for <camera> in the last 120s">
Frigate's record watchdog is restarting the record FFmpeg process because no valid segment has reached the cache. This means the record stream is not connecting or the segments are being rejected (see the audio-codec entry below).
Frigate's record watchdog is restarting the record FFmpeg process because the camera stopped producing usable recordings. The wording distinguishes the cases: `No new recording segments` means no new segment file reached the cache, so ffmpeg isn't getting video out of the record stream; the two `valid` variants mean recordings are arriving but keep failing validation. Either way the fault is on the camera or network side, and the restart is Frigate trying to recover.
See [Recordings: the record stream isn't connecting](/troubleshooting/recordings#the-record-stream-isnt-connecting).
See [Recordings: no new recording segments were created](/troubleshooting/recordings#no-new-recording-segments-were-created).
</FaqItem>
<FaqItem id="invalid-or-missing-video-stream-in-segment" question="Invalid or missing video stream in segment. Discarding.">
<FaqItem id="invalid-or-missing-video-stream-in-segment" question="Invalid or missing video stream in segment. Discarding. / Discarding a corrupt recording segment / Failed to probe corrupt segment / Invalid recording segment detected">
A cached recording segment failed validation (no readable video stream) and was deleted. The most common cause is a segment that was truncated because the record FFmpeg process was killed mid-write, so this often appears alongside, and as a consequence of, the record-stream restarts above. A segment containing only audio triggers it too.
A cached recording segment failed validation and was deleted, either because it had no readable video stream or because its length was impossible. This nearly always means the camera stopped sending usable video partway through the segment: a camera that rebooted, dropped the connection, or ran out of simultaneous connections, or an unreliable link such as WiFi or a failing switch port. Broken camera timestamps (a "Smart Codec" / H.264+ mode) cause the corrupt-segment variants. The same stream failure trips the record watchdog, so the restarts above usually appear alongside these messages.
See [Recordings: invalid or missing video stream in segment](/troubleshooting/recordings#invalid-or-missing-video-stream-in-segment).
</FaqItem>
@@ -131,7 +133,7 @@ The process was killed by the CPU for executing an unsupported instruction. Ther
<FaqItem id="onnx-invalidprotobuf" question="ONNX Runtime InvalidProtobuf / failed to load model">
ONNX Runtime could not parse the model file. The file exists but its contents are not a valid ONNX model, usually a corrupted or interrupted download in `model_cache`, or the wrong file pointed at by `model.path`. Delete the cached model file so Frigate re-downloads it, and confirm `model.path` points at an actual `.onnx` model. See [ONNX detector configuration](/configuration/object_detectors#onnx).
ONNX Runtime could not parse the model file. The file exists but its contents are not a valid ONNX model, usually a corrupted or interrupted download in `model_cache`, or the wrong file pointed at by a model's `path`. Delete the cached model file so Frigate re-downloads it, and confirm the model's `path` points at an actual `.onnx` model. See [ONNX detector configuration](/configuration/object_detectors#onnx).
@@ -39,6 +39,20 @@ To do this efficiently the following setup is required:
When this is done correctly, the GPU will do the decoding and scaling which will result in a small increase in CPU usage but with better results.
### How can I rotate my camera's video feed?
Rotation is best done in the camera's firmware settings (usually called rotate, flip, or corridor mode) so the video arrives already rotated and no extra processing is needed. Check there first.
If your camera does not support rotation, go2rtc's ffmpeg module can rotate the stream with the `#rotate` parameter (`90`, `180`, `270`, or `-90`), but this is not recommended: rotation requires transcoding (re-encoding) the video, which significantly increases CPU usage, especially for high resolution streams.
Point the camera's inputs at the restream as described in the [restream docs](/configuration/restream.md), and swap `detect -> width` and `detect -> height` to match the rotated resolution.
### My mjpeg stream or snapshots look green and crazy
This almost always means that the width/height defined for your camera are not correct. Double check the resolution with VLC or another player. Also make sure you don't have the width and height values backwards.
@@ -121,7 +135,7 @@ You can still configure Frigate to use UDP by using ffmpeg input args or the pre
### Frigate is slow to start up with a "probing detect stream" message in the logs
When `detect.width` and `detect.height` are not set, Frigate probes each camera's detect stream on startup (and when saving the config) to auto-detect its resolution. For RTSP streams Frigate probes with ffprobe and automatically retries over TCP if UDP doesn't respond, with a 5 second timeout per attempt. A camera that cannot be reached over either transport will add up to ~10 seconds to startup before Frigate falls through with default dimensions, which may show up as width `0` and height `0` in Camera Probe Info under System Metrics.
When `detect.width` and `detect.height` are not set, Frigate probes each camera's detect stream on startup (and when saving the config) to auto-detect its resolution. For RTSP streams Frigate probes with ffprobe and automatically retries over TCP if UDP doesn't respond, with a 5 second timeout per attempt. A camera that cannot be reached over either transport will add up to ~10 seconds to startup before Frigate falls through with default dimensions, which may show up as width `0` and height `0` in Camera Probe Info under Health and Metrics.
To skip the probe entirely and make startup instant, set `detect.width` and `detect.height` explicitly in your camera config:
@@ -167,3 +181,9 @@ Frigate's object detection relies on a machine learning [model](../frigate/gloss
- If the false positive is always in the same fixed spot (like a statue or mailbox that reads as a person), add an [object filter mask](../configuration/masks.md#object-filter-masks) over that location.
Filters and masks only hide the incorrect result - they don't teach Frigate what the object actually is. For that, fine-tune your own model or use Frigate+.
### Where do I see problems Frigate has detected?
Open System > Health. The Notices list keeps a record of problems Frigate has found, and you can dismiss any entry to acknowledge it. Ongoing conditions, such as an offline camera or recordings deleted before their retention period, appear in the status bar for admins until they clear, and the status bar links to the Notices list while it has undismissed entries. On mobile, tap the warning icon in the bottom navigation bar to see them.
The Hardware section below the notices shows whether the detection hardware, hardware acceleration, and enrichment devices in your config were found and are being used, so a GPU that silently fell back to the CPU shows up as a warning. Run stream checks to probe every camera's streams for the same problems the camera wizard reports.
@@ -15,7 +15,7 @@ When a stream won't play or behaves oddly, the most important first step is to f
### 1. Read the go2rtc logs
Access the go2rtc logs in the Frigate UI under <NavPath path="System Logs" /> in the sidebar (select the **go2rtc** tab). If go2rtc cannot connect to your camera you will usually see a clear error here: `401 Unauthorized` (bad or incorrectly encoded credentials), `Connection refused` / `timeout` (wrong IP, port, or the camera is at its connection limit), or `404 Not Found` (wrong RTSP path, or the referenced stream name does not exist).
Access the go2rtc logs in the Frigate UI under <NavPath path="Logs" /> in the sidebar (select the **go2rtc** tab). If go2rtc cannot connect to your camera you will usually see a clear error here: `401 Unauthorized` (bad or incorrectly encoded credentials), `Connection refused` / `timeout` (wrong IP, port, or the camera is at its connection limit), or `404 Not Found` (wrong RTSP path, or the referenced stream name does not exist).
### 2. Test the stream in the go2rtc web interface
@@ -78,7 +78,9 @@ go2rtc:
:::warning
The `#`-modifiers (`#video=`, `#audio=`, `#hardware`,`#backchannel=0`, …) **only take effect on a source that is prefixed with `ffmpeg:`**. Adding them to a bare `rtsp://…#audio=opus` source does nothing: go2rtc ignores them. Likewise, when a source references another stream by name (e.g. `ffmpeg:back#audio=aac`), the name must match the stream key **exactly** (it is case sensitive), or the transcode is silently never produced. This is the single most common configuration mistake. In the Frigate UI, the **Use compatibility mode (ffmpeg)** toggle adds the `ffmpeg:` prefix for you.
The transcoding modifiers (`#video=`, `#audio=`, `#hardware`, …) **only take effect on a source that is prefixed with `ffmpeg:`**. Adding them to a bare `rtsp://…#audio=opus` source does nothing: go2rtc ignores them. Likewise, when a source references another stream by name (e.g. `ffmpeg:back#audio=aac`), the name must match the stream key **exactly** (it is case sensitive), or the transcode is silently never produced. This is the single most common configuration mistake. In the Frigate UI, the **Use compatibility mode (ffmpeg)** toggle adds the `ffmpeg:` prefix for you.
A bare `rtsp://` source reads a different set of modifiers: `#backchannel=`, `#media=`, `#timeout=`, and `#transport=`. These do nothing on an `ffmpeg:` source. Adding **any** modifier to a bare `rtsp://` source also disables the camera's backchannel unless the URL explicitly contains `#backchannel=1`, so a stream dedicated to two-way talk should carry no modifiers at all.
:::
@@ -153,7 +155,7 @@ WebRTC is only attempted when MSE fails or when using a camera's two-way talk fe
- **Codec mismatch**: WebRTC cannot carry H.265 or AAC. The stream backing the WebRTC view must provide Opus (or PCMA/PCMU) audio and H.264 video. Add an `ffmpeg:back#audio=opus` source as shown above.
- **Port `8555` not reachable, or no candidates set**: WebRTC needs port `8555` (both TCP and UDP) open and a reachable candidate advertised. On Docker installs running on a custom/overlay network, go2rtc may advertise unreachable container IPs as ICE candidates; setting `webrtc.filters.candidates: []` and supplying only your host's LAN IP resolves this. See [WebRTC extra configuration](/configuration/live#webrtc-extra-configuration).
- **Two-way talk** additionally requires a secure context (HTTPS or the authenticated port `8971`, because browsers block microphone access on plain HTTP). The camera's RTSP backchannel must also be handled correctly: go2rtc seizes the backchannel by default, which blocks two-way audio for other consumers and can inject static. Disable it on the primary stream with `#backchannel=0` and use a separate dedicated stream for talk, as documented in [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream).
- **Two-way talk** additionally requires a secure context (HTTPS or the authenticated port `8971`, because browsers block microphone access on plain HTTP). The camera's RTSP backchannel must also be handled correctly: go2rtc seizes the backchannel by default, which blocks two-way audio for other consumers and can inject static. Disable it on the primary stream with `#backchannel=0` and use a separate dedicated stream for talk, carrying no `#` modifiers of any kind, as documented in [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream).
@@ -209,6 +209,50 @@ If the record stream uses a "Smart Codec"/H.264+ mode or changes encoding parame
</FaqItem>
<FaqItem id="invalid-or-missing-video-stream-in-segment" question="I see the message: WARNING : Invalid or missing video stream in segment ... Discarding.">
Every recording segment is validated before it leaves the cache. Frigate probes each finished `.mp4` in `/tmp/cache` and requires a readable video stream and a valid duration before moving to storage. A segment that fails is deleted, so those ~10 seconds of footage are lost. Three messages come from this check:
- `Invalid or missing video stream in segment <path>. Discarding.` The segment holds no video, or could not be read at all.
- `Failed to probe corrupt segment <path>` followed by `Discarding a corrupt recording segment: <path>`. The segment was read, but its length could not be determined.
- `Discarding a corrupt recording segment: <path>` on its own. The segment's length is impossible (empty, or longer than ten minutes), which points at broken timestamps coming from the camera.
For each one, the camera watchdog also logs `Invalid recording segment detected for <camera> at <timestamp>`.
:::warning
This is almost always a **camera or network problem**, not a Frigate one. A segment is only complete once ffmpeg has finished writing it, so anything that interrupts the stream partway through leaves behind a file that cannot be saved. Frigate is reporting the interruption, not causing it.
:::
#### Start with the camera and the network
- **The camera dropped the connection.** Cameras reboot, reinitialize their stream when switching to night mode, and cut clients off when they are overloaded or out of simultaneous connections. Count everything pulling from the camera at once: Frigate's detect and record streams, go2rtc, a phone app, and any other NVR each use one. Routing all roles through a single [RTSP restream](/configuration/restream#reduce-connections-to-camera) so the camera only ever sees one connection often resolves this by itself.
- **The link to the camera is unreliable.** WiFi cameras, powerline adapters, a saturated uplink, a failing switch port, or a marginal cable all produce this pattern, and usually only on one camera at a time. WiFi cameras are [not recommended](https://ipcamtalk.com/threads/multiple-cameras-high-bandwidth.77100/#post-861110).
- **The camera cannot reliably send what it is being asked for.** A high bitrate 4K stream can be more than the camera's own hardware can encode and push out under load. Lower the bitrate, or record a lower-resolution profile.
- **The camera is using a "Smart Codec", H.264+, or H.265+ mode.** These change encoding parameters mid-stream and produce the broken timestamps behind the corrupt-segment variant. Turn the mode off and set the camera's keyframe interval equal to its frame rate. See [Segments are only ~1 second long](#segments-are-only-1-second-long).
Read the rest of the Frigate and/or go2rtc log around the **first** occurrence. When the camera or the network is at fault, other messages show up with it, such as `No frames received from <camera> in 20 seconds`, `Non-monotonic DTS`, `RTP: PT=xx: bad cseq`, `error while decoding MB`, or a connection timeout. Each of those is explained in [Common error messages](/troubleshooting/common_errors). To confirm the camera is the source, open its stream in the [go2rtc web interface](/troubleshooting/go2rtc) on port `1984` or play the same URL in VLC, and leave it running long enough for the failures to happen again.
#### If the camera and network check out
- **Audio the recording cannot store.** Some cameras send G.711 audio, which cannot be saved in an MP4 and stops segments from finalizing. See [Incompatible audio codec](#incompatible-audio-codec-recordings-silently-fail-to-save).
- **Frigate itself was stopped or restarted.** A single warning per camera around a restart is expected and needs no action.
- **The system ran out of room or memory.** A full `/tmp/cache`, or the host killing Frigate for using too much memory, cuts off the segment being written. Both leave other errors in the log alongside this one. See [No space left on device](#errno-28-no-space-left-on-device).
</FaqItem>
<FaqItem id="no-new-recording-segments-were-created" question="I see the message: ERROR : No new recording segments were created for <camera> in the last 120s. Restarting the ffmpeg record process...">
When a camera stops producing usable recordings for two minutes, Frigate restarts that camera's record process to try to recover. The wording tells you how far the recordings got:
- **`No new recording segments were created`**: no new segment file showed up in the cache at all, so ffmpeg isn't getting video out of the record stream. The camera is unreachable or refusing the connection, the stream URL, path, or credentials are wrong, or the camera accepted the connection and then sent nothing. See [The record stream isn't connecting](#the-record-stream-isnt-connecting).
- **`No new valid recording segments were created`** and **`No valid segments created since last invalid segment`**: recordings are arriving, but they keep failing validation, so the camera is sending video that cannot be saved. See [Invalid or missing video stream in segment](#invalid-or-missing-video-stream-in-segment) above.
The restart is Frigate recovering from a problem, not causing one. One of these after a camera reboot or a brief network drop is normal. Seeing them repeat every couple of minutes means the camera or the network is still failing, and the restarts can extend the damage, because each one cuts off the segment that was being written. Work from the earliest failure in that camera's log rather than from the restarts.
</FaqItem>
<FaqItem id="i-see-the-message-warning--unable-to-keep-up-with-recording-segments-in-cache-for-camera-keeping-the-5-most-recent-segments-out-of-6-and-discarding-the-rest" question="I see the message: WARNING : Unable to keep up with recording segments in cache for camera. Keeping the 5 most recent segments out of 6 and discarding the rest...">
This warning means the recording maintainer cannot move recording segments from the RAM cache to disk fast enough. When the cache fills up, Frigate discards the oldest segments to avoid running out of memory and crashing, so you lose recorded footage. This is almost always a storage throughput or system resource problem. Work through the steps below to identify which.
@@ -330,7 +374,7 @@ If segments are only ~1 second instead of ~10 seconds, the camera is sending cor
:::tip
You don't have to run `ffprobe` by hand to catch this. Open a camera's **Camera Probe Info** dialog (the info icon on the System → Metrics → Cameras page) and check the **Keyframe analysis** section. It probes the record stream and flags sparse or variable keyframes, which is what smart/"+" codecs (H.264+/H.265+) and long keyframe intervals produce.
You don't have to run `ffprobe` by hand to catch this. Open a camera's **Camera Probe Info** dialog (the info icon on the Health and Metrics → Cameras page) and check the **Keyframe analysis** section. It probes the record stream and flags sparse or variable keyframes, which is what smart/"+" codecs (H.264+/H.265+) and long keyframe intervals produce.
@@ -40,7 +40,7 @@ Deleting a group also clears any custom layout you saved for it.
## Rearranging a camera group layout
On desktop and tablet, each camera group has its own freely-arrangeable grid. Enter **Edit Layout** mode from the layout button in the lower-right corner: camera tiles gain a drag handle and corner resize handles. Drag a tile to reposition it and drag a corner to resize it (the aspect ratio is preserved). Exit edit mode to save. The layout is stored in your browser per device, so each device can have its own arrangement.
On desktop and tablet, each camera group has its own freely-arrangeable grid. Enter **Edit Layout** mode from the layout button in the lower-right corner: camera tiles gain a drag handle and corner resize handles. Drag a tile to reposition it and drag a corner to resize it (the aspect ratio is preserved). Exit edit mode to save. The layout is stored in your browser per device, so each device can have its own arrangement, and layouts can be exported to a file and imported on another device.
The default **All Cameras** dashboard is not manually arrangeable. It automatically sizes tiles based on each camera's aspect ratio (wide cameras span two columns, tall cameras span two rows).
@@ -68,7 +68,7 @@ For non-default groups, the context menu also exposes **Streaming Settings** for
- the **streaming method**: **No Streaming**, **Smart Streaming** (recommended), or **Continuous Streaming** (higher bandwidth), and
- **compatibility mode**, for devices that have trouble rendering the default player.
These settings are saved per group and per device in your browser, not in your config file.
These settings are saved per group and per device in your browser, not in your config file, and can be exported to a file and imported on another device.
Returns the selected model and its context size for each configured GenAI role. Reads only what the client saved when it initialized, so the provider is not queried for its model list.
operationId:genai_roles_genai_roles_get
responses:
'200':
description:Successful Response
content:
application/json:
schema:{}
security:
- frigateAdminAuth:[]
x-required-role:admin
/genai/probe:
post:
tags:
@@ -2946,6 +3004,44 @@ paths:
- frigateUserAuth:[]
x-required-role:any
description:'**Access:** Any authenticated user.'
/categorized_object_names:
get:
tags:
- App
summary:Get known object names by object type
description:|-
**Access:**Any authenticated user.
Returns the sub labels and attributes this install can attach,
grouped by object type. Unlike /sub_labels, which reflects what has already been
detected, this reads the config and model files, so it covers recognized face
names, named license plates, custom object classification categories, and the
**Access:**Authenticated user with access to the referenced camera.
Returns an HLS playlist pinned to one stream type (main or sub) for the specified timestamp-range on the specified camera. Append /master.m3u8 or /index.m3u8 for HLS playback.
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