Update hardware recs

.cspell/frigate-dictionary.txt

@@ -1,311 +1,168 @@
-aarch
-absdiff
-airockchip
-Alloc
-Amcrest
-amdgpu
-analyzeduration
-Annke
-apexcharts
-arange
-argmax
-argmin
-argpartition
-ascontiguousarray
-astype
-authelia
-authentik
-autodetected
-automations
-autotrack
-autotracked
-autotracker
-autotracking
-balena
-Beelink
-BGRA
-BHWC
-blackshear
-blakeblackshear
-bottombar
-buildx
-castable
-cdist
-Celeron
-cgroups
-chipset
-chromadb
-Chromecast
-cmdline
-codeowner
-CODEOWNERS
-codeproject
-colormap
-colorspace
-comms
-coro
-ctypeslib
-CUDA
-Cuvid
-Dahua
-datasheet
-debconf
-deci
-deepstack
-defragment
-devcontainer
-DEVICEMAP
-discardcorrupt
-dpkg
-dsize
-dtype
-ECONNRESET
-edgetpu
-fastapi
-faststart
-fflags
-ffprobe
-fillna
-flac
-foscam
-fourcc
-framebuffer
-fregate
-frégate
-fromarray
-frombuffer
-frontdoor
-fstype
-fullchain
-fullscreen
-genai
-generativeai
-genpts
-getpid
-gpuload
-HACS
-Hailo
-hass
-hconcat
-healthcheck
-hideable
-Hikvision
-homeassistant
-homekit
-homography
-hsize
-hstack
-httpx
-hwaccel
-hwdownload
-hwmap
-hwupload
-iloc
-imagestream
-imdecode
-imencode
-imread
-imutils
-imwrite
-interp
-iostat
-iotop
-itemsize
-Jellyfin
-jetson
-jetsons
-joserfc
-jsmpeg
-jsonify
-Kalman
-keepalive
-keepdims
-labelmap
-letsencrypt
-levelname
-LIBAVFORMAT
-libedgetpu
-libnvinfer
-libva
-libwebp
-libx
-libyolo
-linalg
-localzone
-logpipe
-Loryta
-lstsq
-lsusb
-markupsafe
-maxsplit
-MEMHOSTALLOC
-memlimit
-meshgrid
-metadatas
-migraphx
-minilm
-mjpeg
-mkfifo
-mobiledet
-mobilenet
-modelpath
-mosquitto
-mountpoint
-movflags
-mpegts
-mqtt
-mse
-msenc
-namedtuples
-nbytes
-nchw
-ndarray
-ndimage
-nethogs
-newaxis
-nhwc
-NOBLOCK
-nobuffer
-nokey
-NONBLOCK
-noninteractive
-noprint
-Norfair
-nptype
-NTSC
-numpy
-nvenc
-nvhost
-nvml
-nvmpi
-ollama
-onnx
-onnxruntime
-onvif
-ONVIF
-openai
-opencv
-openvino
-OWASP
-paho
-passwordless
-popleft
-posthog
-postprocess
-poweroff
-preexec
-probesize
-protobuf
-pstate
-psutil
-pubkey
-putenv
-pycache
-pydantic
-pyobj
-pysqlite
-pytz
-pywebpush
-qnap
-quantisation
-Radeon
-radeonsi
-radeontop
-rawvideo
-rcond
-RDONLY
-rebranded
-referer
-reindex
-Reolink
-restream
-restreamed
-restreaming
-rkmpp
-rknn
-rkrga
-rockchip
-rocm
-rocminfo
-rootfs
 rtmp
+edgetpu
+labelmap
+rockchip
+jetson
+rocm
+vaapi
+CUDA
+hwaccel
 RTSP
-ruamel
-scroller
-setproctitle
-setpts
-shms
-SIGUSR
-skylake
-sleeptime
-SNDMORE
-socs
-sqliteq
-sqlitevecq
-ssdlite
-statm
-stimeout
-stylelint
-subclassing
-substream
-superfast
-surveillance
-svscan
-Swipeable
-sysconf
-tailscale
-Tapo
-tensorrt
+Hikvision
+Dahua
+Amcrest
+Reolink
+Loryta
+Beelink
+Celeron
+vaapi
+blakeblackshear
+workdir
+onvif
+autotracking
+openvino
 tflite
+deepstack
+codeproject
+udev
+tailscale
+restream
+restreaming
+webrtc
+ssdlite
+mobilenet
+mosquitto
+datasheet
+Jellyfin
+Radeon
+libva
+Ubiquiti
+Unifi
+Tapo
+Annke
+autotracker
+autotracked
+variations
+ONVIF
+traefik
+devcontainer
+rootfs
+ffprobe
+autotrack
+logpipe
+imread
+imwrite
+imencode
+imutils
 thresholded
 timelapse
-tmpfs
-tobytes
-toggleable
-traefik
-tzlocal
-Ubiquiti
-udev
-udevadm
 ultrafast
-unichip
-unidecode
-Unifi
-unixepoch
-unraid
-unreviewed
-userdata
-usermod
-uvicorn
-vaapi
+sleeptime
+radeontop
 vainfo
-variations
-vbios
-vconcat
-vitb
-vstream
-vsync
-wallclock
-webp
-webpush
-webrtc
+tmpfs
+homography
 websockets
-webui
-werkzeug
-workdir
-WRONLY
-wsgirefserver
-wsgiutils
-wsize
-xaddr
-xmaxs
-xmins
-XPUB
-XSUB
-ymaxs
-ymins
-yolo
-yolonas
-yolox
+LIBAVFORMAT
+NTSC
+onnxruntime
+fourcc
+radeonsi
+paho
+imagestream
+jsonify
+cgroups
+sysconf
+memlimit
+gpuload
+nvml
+setproctitle
+psutil
+Kalman
+frontdoor
+namedtuples
 zeep
-zerolatency
+fflags
+probesize
+wallclock
+rknn
+socs
+pydantic
+shms
+imdecode
+colormap
+webui
+mse
+jsmpeg
+unreviewed
+Chromecast
+Swipeable
+flac
+scroller
+cmdline
+toggleable
+bottombar
+opencv
+apexcharts
+buildx
+mqtt
+rawvideo
+defragment
+Norfair
+subclassing
+yolo
+tensorrt
+blackshear
+stylelint
+HACS
+homeassistant
+hass
+castable
+mobiledet
+framebuffer
+mjpeg
+substream
+codeowner
+noninteractive
+restreamed
+mountpoint
+fstype
+OWASP
+iotop
+letsencrypt
+fullchain
+lsusb
+iostat
+usermod
+balena
+passwordless
+debconf
+dpkg
+poweroff
+surveillance
+qnap
+homekit
+colorspace
+quantisation
+skylake
+Cuvid
+foscam
+onnx
+numpy
+protobuf
+aarch
+amdgpu
+chipset
+referer
+mpegts
+webp
+authelia
+authentik
+unichip
+rebranded
+udevadm
+automations
+unraid
+hideable
+healthcheck
+keepalive

.devcontainer/devcontainer.json

@@ -52,8 +52,7 @@
         "csstools.postcss",
         "blanu.vscode-styled-jsx",
         "bradlc.vscode-tailwindcss",
-        "charliermarsh.ruff",
-        "eamodio.gitlens"
+        "charliermarsh.ruff"
       ],
       "settings": {
         "remote.autoForwardPorts": false,

.devcontainer/post_create.sh

@@ -3,12 +3,10 @@
 set -euxo pipefail
 
 # Cleanup the old github host key
-if [[ -f ~/.ssh/known_hosts ]]; then
-  # Add new github host key
-  sed -i -e '/AAAAB3NzaC1yc2EAAAABIwAAAQEAq2A7hRGmdnm9tUDbO9IDSwBK6TbQa+PXYPCPy6rbTrTtw7PHkccKrpp0yVhp5HdEIcKr6pLlVDBfOLX9QUsyCOV0wzfjIJNlGEYsdlLJizHhbn2mUjvSAHQqZETYP81eFzLQNnPHt4EVVUh7VfDESU84KezmD5QlWpXLmvU31\/yMf+Se8xhHTvKSCZIFImWwoG6mbUoWf9nzpIoaSjB+weqqUUmpaaasXVal72J+UX2B+2RPW3RcT0eOzQgqlJL3RKrTJvdsjE3JEAvGq3lGHSZXy28G3skua2SmVi\/w4yCE6gbODqnTWlg7+wC604ydGXA8VJiS5ap43JXiUFFAaQ==/d' ~/.ssh/known_hosts
-  curl -L https://api.github.com/meta | jq -r '.ssh_keys | .[]' | \
-    sed -e 's/^/github.com /' >> ~/.ssh/known_hosts
-fi
+sed -i -e '/AAAAB3NzaC1yc2EAAAABIwAAAQEAq2A7hRGmdnm9tUDbO9IDSwBK6TbQa+PXYPCPy6rbTrTtw7PHkccKrpp0yVhp5HdEIcKr6pLlVDBfOLX9QUsyCOV0wzfjIJNlGEYsdlLJizHhbn2mUjvSAHQqZETYP81eFzLQNnPHt4EVVUh7VfDESU84KezmD5QlWpXLmvU31\/yMf+Se8xhHTvKSCZIFImWwoG6mbUoWf9nzpIoaSjB+weqqUUmpaaasXVal72J+UX2B+2RPW3RcT0eOzQgqlJL3RKrTJvdsjE3JEAvGq3lGHSZXy28G3skua2SmVi\/w4yCE6gbODqnTWlg7+wC604ydGXA8VJiS5ap43JXiUFFAaQ==/d' ~/.ssh/known_hosts
+# Add new github host key
+curl -L https://api.github.com/meta | jq -r '.ssh_keys | .[]' | \
+  sed -e 's/^/github.com /' >> ~/.ssh/known_hosts
 
 # Frigate normal container runs as root, so it have permission to create
 # the folders. But the devcontainer runs as the host user, so we need to
@@ -19,7 +17,7 @@ sudo chown -R "$(id -u):$(id -g)" /media/frigate
 # When started as a service, LIBAVFORMAT_VERSION_MAJOR is defined in the
 # s6 service file. For dev, where frigate is started from an interactive
 # shell, we define it in .bashrc instead.
-echo 'export LIBAVFORMAT_VERSION_MAJOR=$(/usr/lib/ffmpeg/7.0/bin/ffmpeg -version | grep -Po "libavformat\W+\K\d+")' >> $HOME/.bashrc
+echo 'export LIBAVFORMAT_VERSION_MAJOR=$(ffmpeg -version | grep -Po "libavformat\W+\K\d+")' >> $HOME/.bashrc
 
 make version
 

.github/DISCUSSION_TEMPLATE/camera-support.yml

@@ -90,9 +90,6 @@ body:
         - HassOS Addon
         - Docker Compose
         - Docker CLI
-        - Proxmox via Docker
-        - Proxmox via TTeck Script
-        - Windows WSL2
     validations:
       required: true
   - type: dropdown
@@ -105,7 +102,7 @@ body:
         - TensorRT
         - RKNN
         - Other
-        - CPU (no coral)
+        - CPU (no Coral)
     validations:
       required: true
   - type: dropdown

.github/DISCUSSION_TEMPLATE/config-support.yml

@@ -76,17 +76,6 @@ body:
         - HassOS Addon
         - Docker Compose
         - Docker CLI
-        - Proxmox via Docker
-        - Proxmox via TTeck Script
-        - Windows WSL2
-    validations:
-      required: true
-  - type: textarea
-    id: docker
-    attributes:
-      label: docker-compose file or Docker CLI command
-      description: This will be automatically formatted into code, so no need for backticks.
-      render: yaml
     validations:
       required: true
   - type: dropdown

.github/DISCUSSION_TEMPLATE/detector-support.yml

@@ -48,6 +48,28 @@ body:
       render: shell
     validations:
       required: true
+  - type: textarea
+    id: go2rtclogs
+    attributes:
+      label: Relevant go2rtc log output
+      description: Please copy and paste any relevant go2rtc log output. Include logs before and after your exact error when possible. Logs can be viewed via the Frigate UI, Docker, or the go2rtc dashboard. This will be automatically formatted into code, so no need for backticks.
+      render: shell
+    validations:
+      required: true
+  - type: dropdown
+    id: os
+    attributes:
+      label: Operating system
+      options:
+        - HassOS
+        - Debian
+        - Other Linux
+        - Proxmox
+        - UNRAID
+        - Windows
+        - Other
+    validations:
+      required: true
   - type: dropdown
     id: install-method
     attributes:
@@ -56,9 +78,6 @@ body:
         - HassOS Addon
         - Docker Compose
         - Docker CLI
-        - Proxmox via Docker
-        - Proxmox via TTeck Script
-        - Windows WSL2
     validations:
       required: true
   - type: dropdown

.github/DISCUSSION_TEMPLATE/general-support.yml

@@ -68,6 +68,20 @@ body:
       label: Frigate stats
       description: Output from frigate's /api/stats endpoint
       render: json
+  - type: dropdown
+    id: os
+    attributes:
+      label: Operating system
+      options:
+        - HassOS
+        - Debian
+        - Other Linux
+        - Proxmox
+        - UNRAID
+        - Windows
+        - Other
+    validations:
+      required: true
   - type: dropdown
     id: install-method
     attributes:
@@ -76,17 +90,6 @@ body:
         - HassOS Addon
         - Docker Compose
         - Docker CLI
-        - Proxmox via Docker
-        - Proxmox via TTeck Script
-        - Windows WSL2
-    validations:
-      required: true
-  - type: textarea
-    id: docker
-    attributes:
-      label: docker-compose file or Docker CLI command
-      description: This will be automatically formatted into code, so no need for backticks.
-      render: yaml
     validations:
       required: true
   - type: dropdown

.github/DISCUSSION_TEMPLATE/hardware-acceleration-support.yml

@@ -24,6 +24,12 @@ body:
       description: Visible on the System page in the Web UI. Please include the full version including the build identifier (eg. 0.14.0-ea36ds1)
     validations:
       required: true
+  - type: input
+    attributes:
+      label: In which browser(s) are you experiencing the issue with?
+      placeholder: Google Chrome 88.0.4324.150
+      description: >
+        Provide the full name and don't forget to add the version!
   - type: textarea
     id: config
     attributes:
@@ -64,6 +70,20 @@ body:
       render: shell
     validations:
       required: true
+  - type: dropdown
+    id: os
+    attributes:
+      label: Operating system
+      options:
+        - HassOS
+        - Debian
+        - Other Linux
+        - Proxmox
+        - UNRAID
+        - Windows
+        - Other
+    validations:
+      required: true
   - type: dropdown
     id: install-method
     attributes:
@@ -72,22 +92,6 @@ body:
         - HassOS Addon
         - Docker Compose
         - Docker CLI
-        - Proxmox via Docker
-        - Proxmox via TTeck Script
-        - Windows WSL2
-    validations:
-      required: true
-  - type: dropdown
-    id: object-detector
-    attributes:
-      label: Object Detector
-      options:
-        - Coral
-        - OpenVino
-        - TensorRT
-        - RKNN
-        - Other
-        - CPU (no coral)
     validations:
       required: true
   - type: dropdown

.github/actions/setup/action.yml

@@ -33,9 +33,9 @@ runs:
       with:
         string: ${{ github.repository }}
     - name: Set up QEMU
-      uses: docker/setup-qemu-action@v3
+      uses: docker/setup-qemu-action@v2
     - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v3
+      uses: docker/setup-buildx-action@v2
     - name: Log in to the Container registry
       uses: docker/login-action@465a07811f14bebb1938fbed4728c6a1ff8901fc
       with:

.github/pull_request_template.md

@@ -1,32 +0,0 @@
-## Proposed change
-<!--
-  Describe what this pull request does and how it will benefit users of Frigate.
-  Please describe in detail any considerations, breaking changes, etc. that are 
-  made in this pull request.
--->
-
-
-## Type of change
-
-- [ ] Dependency upgrade
-- [ ] Bugfix (non-breaking change which fixes an issue)
-- [ ] New feature
-- [ ] Breaking change (fix/feature causing existing functionality to break)
-- [ ] Code quality improvements to existing code
-- [ ] Documentation Update
-
-## Additional information
-
-- This PR fixes or closes issue: fixes #
-- This PR is related to issue: 
-
-## Checklist
-
-<!--
-  Put an `x` in the boxes that apply.
--->
-
-- [ ] The code change is tested and works locally.
-- [ ] Local tests pass. **Your PR cannot be merged unless tests pass**
-- [ ] There is no commented out code in this PR.
-- [ ] The code has been formatted using Ruff (`ruff format frigate`)

.github/workflows/ci.yml

@@ -6,8 +6,6 @@ on:
     branches:
       - dev
       - master
-    paths-ignore:
-      - "docs/**"
 
 # only run the latest commit to avoid cache overwrites
 concurrency:
@@ -19,13 +17,11 @@ env:
 
 jobs:
   amd64_build:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     name: AMD64 Build
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -42,13 +38,11 @@ jobs:
           tags: ${{ steps.setup.outputs.image-name }}-amd64
           cache-from: type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64
   arm64_build:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     name: ARM Build
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -66,9 +60,8 @@ jobs:
             ${{ steps.setup.outputs.image-name }}-standard-arm64
           cache-from: type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64
       - name: Build and push RPi build
-        uses: docker/bake-action@v6
+        uses: docker/bake-action@v4
         with:
-          source: .
           push: true
           targets: rpi
           files: docker/rpi/rpi.hcl
@@ -76,14 +69,21 @@ jobs:
             rpi.tags=${{ steps.setup.outputs.image-name }}-rpi
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64,mode=max
+      - name: Build and push Rockchip build
+        uses: docker/bake-action@v3
+        with:
+          push: true
+          targets: rk
+          files: docker/rockchip/rk.hcl
+          set: |
+            rk.tags=${{ steps.setup.outputs.image-name }}-rk
+            *.cache-from=type=gha
   jetson_jp4_build:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     name: Jetson Jetpack 4
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -95,9 +95,8 @@ jobs:
           BASE_IMAGE: timongentzsch/l4t-ubuntu20-opencv:latest
           SLIM_BASE: timongentzsch/l4t-ubuntu20-opencv:latest
           TRT_BASE: timongentzsch/l4t-ubuntu20-opencv:latest
-        uses: docker/bake-action@v6
+        uses: docker/bake-action@v4
         with:
-          source: .
           push: true
           targets: tensorrt
           files: docker/tensorrt/trt.hcl
@@ -106,13 +105,11 @@ jobs:
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp4
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp4,mode=max
   jetson_jp5_build:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     name: Jetson Jetpack 5
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -124,9 +121,8 @@ jobs:
           BASE_IMAGE: nvcr.io/nvidia/l4t-tensorrt:r8.5.2-runtime
           SLIM_BASE: nvcr.io/nvidia/l4t-tensorrt:r8.5.2-runtime
           TRT_BASE: nvcr.io/nvidia/l4t-tensorrt:r8.5.2-runtime
-        uses: docker/bake-action@v6
+        uses: docker/bake-action@v4
         with:
-          source: .
           push: true
           targets: tensorrt
           files: docker/tensorrt/trt.hcl
@@ -135,15 +131,13 @@ jobs:
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp5
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp5,mode=max
   amd64_extra_builds:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     name: AMD64 Extra Build
     needs:
       - amd64_build
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -152,9 +146,8 @@ jobs:
       - name: Build and push TensorRT (x86 GPU)
         env:
           COMPUTE_LEVEL: "50 60 70 80 90"
-        uses: docker/bake-action@v6
+        uses: docker/bake-action@v4
         with:
-          source: .
           push: true
           targets: tensorrt
           files: docker/tensorrt/trt.hcl
@@ -162,75 +155,61 @@ jobs:
             tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64,mode=max
-  arm64_extra_builds:
-    runs-on: ubuntu-22.04
-    name: ARM Extra Build
-    needs:
-      - arm64_build
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-      - name: Set up QEMU and Buildx
-        id: setup
-        uses: ./.github/actions/setup
-        with:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      - name: Build and push Rockchip build
-        uses: docker/bake-action@v6
-        with:
-          source: .
-          push: true
-          targets: rk
-          files: docker/rockchip/rk.hcl
-          set: |
-            rk.tags=${{ steps.setup.outputs.image-name }}-rk
-            *.cache-from=type=gha
-  combined_extra_builds:
-    runs-on: ubuntu-22.04
-    name: Combined Extra Builds
-    needs:
-      - amd64_build
-      - arm64_build
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-      - name: Set up QEMU and Buildx
-        id: setup
-        uses: ./.github/actions/setup
-        with:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      - name: Build and push Hailo-8l build
-        uses: docker/bake-action@v6
-        with:
-          source: .
-          push: true
-          targets: h8l
-          files: docker/hailo8l/h8l.hcl
-          set: |
-            h8l.tags=${{ steps.setup.outputs.image-name }}-h8l
-            *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-h8l
-            *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-h8l,mode=max
-      - name: AMD/ROCm general build
-        env:
-          AMDGPU: gfx
-          HSA_OVERRIDE: 0
-        uses: docker/bake-action@v6
-        with:
-          source: .
-          push: true
-          targets: rocm
-          files: docker/rocm/rocm.hcl
-          set: |
-            rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
-            *.cache-from=type=gha
+      #- name: AMD/ROCm general build
+      #  env:
+      #    AMDGPU: gfx
+      #    HSA_OVERRIDE: 0
+      #  uses: docker/bake-action@v3
+      #  with:
+      #    push: true
+      #    targets: rocm
+      #    files: docker/rocm/rocm.hcl
+      #    set: |
+      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
+      #      *.cache-from=type=gha
+      #- name: AMD/ROCm gfx900
+      #  env:
+      #    AMDGPU: gfx900
+      #    HSA_OVERRIDE: 1
+      #    HSA_OVERRIDE_GFX_VERSION: 9.0.0
+      #  uses: docker/bake-action@v3
+      #  with:
+      #    push: true
+      #    targets: rocm
+      #    files: docker/rocm/rocm.hcl
+      #    set: |
+      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm-gfx900
+      #      *.cache-from=type=gha
+      #- name: AMD/ROCm gfx1030
+      #  env:
+      #    AMDGPU: gfx1030
+      #    HSA_OVERRIDE: 1
+      #    HSA_OVERRIDE_GFX_VERSION: 10.3.0
+      #  uses: docker/bake-action@v3
+      #  with:
+      #    push: true
+      #    targets: rocm
+      #    files: docker/rocm/rocm.hcl
+      #    set: |
+      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm-gfx1030
+      #      *.cache-from=type=gha
+      #- name: AMD/ROCm gfx1100
+      #  env:
+      #    AMDGPU: gfx1100
+      #    HSA_OVERRIDE: 1
+      #    HSA_OVERRIDE_GFX_VERSION: 11.0.0
+      #  uses: docker/bake-action@v3
+      #  with:
+      #    push: true
+      #    targets: rocm
+      #    files: docker/rocm/rocm.hcl
+      #    set: |
+      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm-gfx1100
+      #      *.cache-from=type=gha
   # The majority of users running arm64 are rpi users, so the rpi
   # build should be the primary arm64 image
   assemble_default_build:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     name: Assemble and push default build
     needs:
       - amd64_build
@@ -241,7 +220,7 @@ jobs:
         with:
           string: ${{ github.repository }}
       - name: Log in to the Container registry
-        uses: docker/login-action@9780b0c442fbb1117ed29e0efdff1e18412f7567
+        uses: docker/login-action@0d4c9c5ea7693da7b068278f7b52bda2a190a446
         with:
           registry: ghcr.io
           username: ${{ github.actor }}

.github/workflows/dependabot-auto-merge.yaml

@@ -0,0 +1,24 @@
+name: dependabot-auto-merge
+on: pull_request
+
+permissions:
+  contents: write
+
+jobs:
+  dependabot-auto-merge:
+    runs-on: ubuntu-latest
+    if: github.actor == 'dependabot[bot]'
+    steps:
+      - name: Get Dependabot metadata
+        id: metadata
+        uses: dependabot/fetch-metadata@v2
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+      - name: Enable auto-merge for Dependabot PRs
+        if: steps.metadata.outputs.dependency-type == 'direct:development' && (steps.metadata.outputs.update-type == 'version-update:semver-minor' || steps.metadata.outputs.update-type == 'version-update:semver-patch')
+        run: |
+          gh pr review --approve "$PR_URL"
+          gh pr merge --auto --squash "$PR_URL"
+        env:
+          PR_URL: ${{ github.event.pull_request.html_url }}
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/pull_request.yml

@@ -1,9 +1,6 @@
 name: On pull request
 
-on:
-  pull_request:
-    paths-ignore:
-      - "docs/**"
+on: pull_request
 
 env:
   DEFAULT_PYTHON: 3.9
@@ -19,8 +16,6 @@ jobs:
       DOCKER_BUILDKIT: "1"
     steps:
       - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 16.x
@@ -40,8 +35,6 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 16.x
@@ -56,8 +49,6 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 20.x
@@ -73,10 +64,8 @@ jobs:
     steps:
       - name: Check out the repository
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - name: Set up Python ${{ env.DEFAULT_PYTHON }}
-        uses: actions/setup-python@v5.3.0
+        uses: actions/setup-python@v5.1.0
         with:
           python-version: ${{ env.DEFAULT_PYTHON }}
       - name: Install requirements
@@ -96,8 +85,6 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 16.x

.github/workflows/release.yml

@@ -11,26 +11,21 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - id: lowercaseRepo
         uses: ASzc/change-string-case-action@v6
         with:
           string: ${{ github.repository }}
       - name: Log in to the Container registry
-        uses: docker/login-action@9780b0c442fbb1117ed29e0efdff1e18412f7567
+        uses: docker/login-action@0d4c9c5ea7693da7b068278f7b52bda2a190a446
         with:
           registry: ghcr.io
           username: ${{ github.actor }}
           password: ${{ secrets.GITHUB_TOKEN }}
       - name: Create tag variables
-        env:
-          TAG: ${{ github.ref_name }}
-          LOWERCASE_REPO: ${{ steps.lowercaseRepo.outputs.lowercase }}
         run: |
-          BUILD_TYPE=$([[ "${TAG}" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]] && echo "stable" || echo "beta")
+          BUILD_TYPE=$([[ "${{ github.ref_name }}" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]] && echo "stable" || echo "beta")
           echo "BUILD_TYPE=${BUILD_TYPE}" >> $GITHUB_ENV
-          echo "BASE=ghcr.io/${LOWERCASE_REPO}" >> $GITHUB_ENV
+          echo "BASE=ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}" >> $GITHUB_ENV
           echo "BUILD_TAG=${GITHUB_SHA::7}" >> $GITHUB_ENV
           echo "CLEAN_VERSION=$(echo ${GITHUB_REF##*/} | tr '[:upper:]' '[:lower:]' | sed 's/^[v]//')" >> $GITHUB_ENV
       - name: Tag and push the main image
@@ -39,14 +34,14 @@ jobs:
           STABLE_TAG=${BASE}:stable
           PULL_TAG=${BASE}:${BUILD_TAG}
           docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG} docker://${VERSION_TAG}
-          for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk h8l rocm; do
+          for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk; do
             docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG}-${variant} docker://${VERSION_TAG}-${variant}
           done
 
           # stable tag
           if [[ "${BUILD_TYPE}" == "stable" ]]; then
             docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG} docker://${STABLE_TAG}
-            for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk h8l rocm; do
+            for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk; do
               docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG}-${variant} docker://${STABLE_TAG}-${variant}
             done
           fi

.github/workflows/stale.yml

@@ -23,9 +23,7 @@ jobs:
           exempt-pr-labels: "pinned,security,dependencies"
           operations-per-run: 120
       - name: Print outputs
-        env:
-          STALE_OUTPUT: ${{ join(steps.stale.outputs.*, ',') }}
-        run: echo "$STALE_OUTPUT"
+        run: echo ${{ join(steps.stale.outputs.*, ',') }}
 
   # clean_ghcr:
   #   name: Delete outdated dev container images
@@ -40,3 +38,4 @@ jobs:
   #         account-type: personal
   #         token: ${{ secrets.GITHUB_TOKEN }}
   #         token-type: github-token
+

.gitignore

@@ -1,6 +1,5 @@
 .DS_Store
-__pycache__
-.mypy_cache
+*.pyc
 *.swp
 debug
 .vscode/*

.vscode/launch.json

@@ -3,9 +3,10 @@
   "configurations": [
     {
       "name": "Python: Launch Frigate",
-      "type": "debugpy",
+      "type": "python",
       "request": "launch",
-      "module": "frigate"
+      "module": "frigate",
+      "justMyCode": true
     }
   ]
 }

CODEOWNERS

@@ -4,4 +4,3 @@
 /docker/tensorrt/*jetson* @madsciencetist
 /docker/rockchip/ @MarcA711
 /docker/rocm/ @harakas
-/docker/hailo8l/ @spanner3003

Makefile

@@ -1,9 +1,11 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.15.2
+VERSION = 0.14.1
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
+CURRENT_UID := $(shell id -u)
+CURRENT_GID := $(shell id -g)
 BOARDS= #Initialized empty
 
 include docker/*/*.mk
@@ -16,38 +18,25 @@ version:
 	echo 'VERSION = "$(VERSION)-$(COMMIT_HASH)"' > frigate/version.py
 
 local: version
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-		--tag frigate:latest \
-		--load
+	docker buildx build --target=frigate --tag frigate:latest --load --file docker/main/Dockerfile .
 
 amd64:
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \
-		--platform linux/amd64
+	docker buildx build --platform linux/amd64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) --file docker/main/Dockerfile .
 
 arm64:
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \
-		--platform linux/arm64
+	docker buildx build --platform linux/arm64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) --file docker/main/Dockerfile .
 
 build: version amd64 arm64
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \
-		--platform linux/arm64/v8,linux/amd64
+	docker buildx build --platform linux/arm64/v8,linux/amd64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) --file docker/main/Dockerfile .
 
 push: push-boards
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-		--tag $(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH) \
-		--platform linux/arm64/v8,linux/amd64 \
-		--push
+	docker buildx build --push --platform linux/arm64/v8,linux/amd64 --target=frigate --tag $(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH) --file docker/main/Dockerfile .
 
 run: local
 	docker run --rm --publish=5000:5000 --volume=${PWD}/config:/config frigate:latest
 
 run_tests: local
-	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest \
-		python3 -u -m unittest
-	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest \
-		python3 -u -m mypy --config-file frigate/mypy.ini frigate
+	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest python3 -u -m unittest
+	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest python3 -u -m mypy --config-file frigate/mypy.ini frigate
 
 .PHONY: run_tests

benchmark.py

@@ -4,7 +4,6 @@ from statistics import mean
 
 import numpy as np
 
-import frigate.util as util
 from frigate.config import DetectorTypeEnum
 from frigate.object_detection import (
     ObjectDetectProcess,
@@ -61,7 +60,7 @@ def start(id, num_detections, detection_queue, event):
     object_detector.cleanup()
     print(f"{id} - Processed for {duration:.2f} seconds.")
     print(f"{id} - FPS: {object_detector.fps.eps():.2f}")
-    print(f"{id} - Average frame processing time: {mean(frame_times) * 1000:.2f}ms")
+    print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
 
 
 ######
@@ -91,7 +90,7 @@ edgetpu_process_2 = ObjectDetectProcess(
 )
 
 for x in range(0, 10):
-    camera_process = util.Process(
+    camera_process = mp.Process(
         target=start, args=(x, 300, detection_queue, events[str(x)])
     )
     camera_process.daemon = True

cspell.json

@@ -7,8 +7,7 @@
     "*.db",
     "node_modules",
     "__pycache__",
-    "dist",
-    "/audio-labelmap.txt"
+    "dist"
   ],
   "language": "en",
   "dictionaryDefinitions": [

docker-compose.yml

@@ -23,7 +23,7 @@ services:
     #                     count: 1
     #                     capabilities: [gpu]
     environment:
-      YOLO_MODELS: ""
+      YOLO_MODELS: yolov7-320
     devices:
       - /dev/bus/usb:/dev/bus/usb
       # - /dev/dri:/dev/dri # for intel hwaccel, needs to be updated for your hardware

docker/hailo8l/Dockerfile

@@ -1,40 +0,0 @@
-# syntax=docker/dockerfile:1.6
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-# Build Python wheels
-FROM wheels AS h8l-wheels
-
-COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
-COPY docker/hailo8l/requirements-wheels-h8l.txt /requirements-wheels-h8l.txt
-
-RUN sed -i "/https:\/\//d" /requirements-wheels.txt
-
-# Create a directory to store the built wheels
-RUN mkdir /h8l-wheels
-
-# Build the wheels
-RUN pip3 wheel --wheel-dir=/h8l-wheels -c /requirements-wheels.txt -r /requirements-wheels-h8l.txt
-
-FROM wget AS hailort
-ARG TARGETARCH
-RUN --mount=type=bind,source=docker/hailo8l/install_hailort.sh,target=/deps/install_hailort.sh \
-    /deps/install_hailort.sh
-
-# Use deps as the base image
-FROM deps AS h8l-frigate
-
-# Copy the wheels from the wheels stage
-COPY --from=h8l-wheels /h8l-wheels /deps/h8l-wheels
-COPY --from=hailort /hailo-wheels /deps/hailo-wheels
-COPY --from=hailort /rootfs/ /
-
-# Install the wheels
-RUN pip3 install -U /deps/h8l-wheels/*.whl
-RUN pip3 install -U /deps/hailo-wheels/*.whl
-
-# Copy base files from the rootfs stage
-COPY --from=rootfs / /
-
-# Set workdir
-WORKDIR /opt/frigate/

docker/hailo8l/h8l.hcl

@@ -1,34 +0,0 @@
-target wget {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64","linux/amd64"]
-  target = "wget"
-}
-
-target wheels {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64","linux/amd64"]
-  target = "wheels"
-}
-
-target deps {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64","linux/amd64"]
-  target = "deps"
-}
-
-target rootfs {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64","linux/amd64"]
-  target = "rootfs"
-}
-
-target h8l {
-  dockerfile = "docker/hailo8l/Dockerfile"
-  contexts = {
-    wget = "target:wget"
-    wheels = "target:wheels"
-    deps = "target:deps"
-    rootfs = "target:rootfs"
-  }
-  platforms = ["linux/arm64","linux/amd64"]
-}

docker/hailo8l/h8l.mk

@@ -1,15 +0,0 @@
-BOARDS += h8l
-
-local-h8l: version
-	docker buildx bake --file=docker/hailo8l/h8l.hcl h8l \
-		--set h8l.tags=frigate:latest-h8l \
-		--load
-
-build-h8l: version
-	docker buildx bake --file=docker/hailo8l/h8l.hcl h8l \
-		--set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l
-
-push-h8l: build-h8l
-	docker buildx bake --file=docker/hailo8l/h8l.hcl h8l \
-	 	--set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l \
-		--push

docker/hailo8l/install_hailort.sh

@@ -1,19 +0,0 @@
-#!/bin/bash
-
-set -euxo pipefail
-
-hailo_version="4.19.0"
-
-if [[ "${TARGETARCH}" == "amd64" ]]; then
-    arch="x86_64"
-elif [[ "${TARGETARCH}" == "arm64" ]]; then
-    arch="aarch64"
-fi
-
-wget -qO- "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${TARGETARCH}.tar.gz" |
-    tar -C / -xzf -
-
-mkdir -p /hailo-wheels
-
-wget -P /hailo-wheels/ "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${hailo_version}-cp39-cp39-linux_${arch}.whl"
-

docker/hailo8l/requirements-wheels-h8l.txt

@@ -1,12 +0,0 @@
-appdirs==1.4.*
-argcomplete==2.0.*
-contextlib2==0.6.*
-distlib==0.3.*
-filelock==3.8.*
-future==0.18.*
-importlib-metadata==5.1.*
-importlib-resources==5.1.*
-netaddr==0.8.*
-netifaces==0.10.*
-verboselogs==1.7.*
-virtualenv==20.17.*

docker/hailo8l/user_installation.sh

@@ -1,48 +0,0 @@
-#!/bin/bash
-
-# Update package list and install dependencies
-sudo apt-get update
-sudo apt-get install -y build-essential cmake git wget
-
-arch=$(uname -m)
-
-if [[ $arch == "x86_64" ]]; then
-    sudo apt install -y linux-headers-$(uname -r);
-else
-    sudo apt install -y linux-modules-extra-$(uname -r);
-fi
-
-# Clone the HailoRT driver repository
-git clone --depth 1 --branch v4.19.0 https://github.com/hailo-ai/hailort-drivers.git
-
-# Build and install the HailoRT driver
-cd hailort-drivers/linux/pcie
-sudo make all
-sudo make install
-
-# Load the Hailo PCI driver
-sudo modprobe hailo_pci
-
-if [ $? -ne 0 ]; then
-  echo "Unable to load hailo_pci module, common reasons for this are:"
-  echo "- Key was rejected by service: Secure Boot is enabling disallowing install."
-  echo "- Permissions are not setup correctly."
-  exit 1
-fi
-
-# Download and install the firmware
-cd ../../
-./download_firmware.sh
-
-# verify the firmware folder is present
-if [ ! -d /lib/firmware/hailo ]; then
-  sudo mkdir /lib/firmware/hailo
-fi
-sudo mv hailo8_fw.*.bin /lib/firmware/hailo/hailo8_fw.bin
-
-# Install udev rules
-sudo cp ./linux/pcie/51-hailo-udev.rules /etc/udev/rules.d/
-sudo udevadm control --reload-rules && sudo udevadm trigger
-
-echo "HailoRT driver installation complete."
-echo "reboot your system to load the firmware!"

docker/main/Dockerfile

@@ -30,16 +30,6 @@ RUN --mount=type=tmpfs,target=/tmp --mount=type=tmpfs,target=/var/cache/apt \
     --mount=type=cache,target=/root/.ccache \
     /deps/build_nginx.sh
 
-FROM wget AS sqlite-vec
-ARG DEBIAN_FRONTEND
-
-# Build sqlite_vec from source
-COPY docker/main/build_sqlite_vec.sh /deps/build_sqlite_vec.sh
-RUN --mount=type=tmpfs,target=/tmp --mount=type=tmpfs,target=/var/cache/apt \
-    --mount=type=bind,source=docker/main/build_sqlite_vec.sh,target=/deps/build_sqlite_vec.sh \
-    --mount=type=cache,target=/root/.ccache \
-    /deps/build_sqlite_vec.sh
-
 FROM scratch AS go2rtc
 ARG TARGETARCH
 WORKDIR /rootfs/usr/local/go2rtc/bin
@@ -158,8 +148,6 @@ RUN apt-get -qq update \
     gfortran openexr libatlas-base-dev libssl-dev\
     libtbb2 libtbb-dev libdc1394-22-dev libopenexr-dev \
     libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev \
-    # sqlite3 dependencies
-    tclsh \
     # scipy dependencies
     gcc gfortran libopenblas-dev liblapack-dev && \
     rm -rf /var/lib/apt/lists/*
@@ -173,10 +161,6 @@ RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
 COPY docker/main/requirements.txt /requirements.txt
 RUN pip3 install -r /requirements.txt
 
-# Build pysqlite3 from source
-COPY docker/main/build_pysqlite3.sh /build_pysqlite3.sh
-RUN /build_pysqlite3.sh
-
 COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
 RUN pip3 wheel --wheel-dir=/wheels -r /requirements-wheels.txt
 
@@ -184,7 +168,6 @@ RUN pip3 wheel --wheel-dir=/wheels -r /requirements-wheels.txt
 # Collect deps in a single layer
 FROM scratch AS deps-rootfs
 COPY --from=nginx /usr/local/nginx/ /usr/local/nginx/
-COPY --from=sqlite-vec /usr/local/lib/ /usr/local/lib/
 COPY --from=go2rtc /rootfs/ /
 COPY --from=libusb-build /usr/local/lib /usr/local/lib
 COPY --from=tempio /rootfs/ /
@@ -205,16 +188,7 @@ ARG APT_KEY_DONT_WARN_ON_DANGEROUS_USAGE=DontWarn
 ENV NVIDIA_VISIBLE_DEVICES=all
 ENV NVIDIA_DRIVER_CAPABILITIES="compute,video,utility"
 
-# Disable tokenizer parallelism warning
-# https://stackoverflow.com/questions/62691279/how-to-disable-tokenizers-parallelism-true-false-warning/72926996#72926996
-ENV TOKENIZERS_PARALLELISM=true
-# https://github.com/huggingface/transformers/issues/27214
-ENV TRANSFORMERS_NO_ADVISORY_WARNINGS=1
-
-# Set OpenCV ffmpeg loglevel to fatal: https://ffmpeg.org/doxygen/trunk/log_8h.html
-ENV OPENCV_FFMPEG_LOGLEVEL=8
-
-ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
+ENV PATH="/usr/lib/btbn-ffmpeg/bin:/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
 
 # Install dependencies
 RUN --mount=type=bind,source=docker/main/install_deps.sh,target=/deps/install_deps.sh \
@@ -240,7 +214,7 @@ ENV S6_CMD_WAIT_FOR_SERVICES_MAXTIME=0
 ENTRYPOINT ["/init"]
 CMD []
 
-HEALTHCHECK --start-period=300s --start-interval=5s --interval=15s --timeout=5s --retries=3 \
+HEALTHCHECK --start-period=120s --start-interval=5s --interval=15s --timeout=5s --retries=3 \
     CMD curl --fail --silent --show-error http://127.0.0.1:5000/api/version || exit 1
 
 # Frigate deps with Node.js and NPM for devcontainer

docker/main/build_pysqlite3.sh

@@ -1,35 +0,0 @@
-#!/bin/bash
-
-set -euxo pipefail
-
-SQLITE3_VERSION="96c92aba00c8375bc32fafcdf12429c58bd8aabfcadab6683e35bbb9cdebf19e" # 3.46.0
-PYSQLITE3_VERSION="0.5.3"
-
-# Fetch the source code for the latest release of Sqlite.
-if [[ ! -d "sqlite" ]]; then
-  wget https://www.sqlite.org/src/tarball/sqlite.tar.gz?r=${SQLITE3_VERSION} -O sqlite.tar.gz
-  tar xzf sqlite.tar.gz
-  cd sqlite/
-  LIBS="-lm" ./configure --disable-tcl --enable-tempstore=always
-  make sqlite3.c
-  cd ../
-  rm sqlite.tar.gz
-fi
-
-# Grab the pysqlite3 source code.
-if [[ ! -d "./pysqlite3" ]]; then
-  git clone https://github.com/coleifer/pysqlite3.git
-fi
-
-cd pysqlite3/
-git checkout ${PYSQLITE3_VERSION}
-
-# Copy the sqlite3 source amalgamation into the pysqlite3 directory so we can
-# create a self-contained extension module.
-cp "../sqlite/sqlite3.c" ./
-cp "../sqlite/sqlite3.h" ./
-
-# Create the wheel and put it in the /wheels dir.
-sed -i "s|name='pysqlite3-binary'|name=PACKAGE_NAME|g" setup.py
-python3 setup.py build_static
-pip3 wheel . -w /wheels

docker/main/build_sqlite_vec.sh

@@ -1,31 +0,0 @@
-#!/bin/bash
-
-set -euxo pipefail
-
-SQLITE_VEC_VERSION="0.1.3"
-
-cp /etc/apt/sources.list /etc/apt/sources.list.d/sources-src.list
-sed -i 's|deb http|deb-src http|g' /etc/apt/sources.list.d/sources-src.list
-apt-get update
-apt-get -yqq build-dep sqlite3 gettext git
-
-mkdir /tmp/sqlite_vec
-# Grab the sqlite_vec source code.
-wget -nv https://github.com/asg017/sqlite-vec/archive/refs/tags/v${SQLITE_VEC_VERSION}.tar.gz
-tar -zxf v${SQLITE_VEC_VERSION}.tar.gz -C /tmp/sqlite_vec
-
-cd /tmp/sqlite_vec/sqlite-vec-${SQLITE_VEC_VERSION}
-
-mkdir -p vendor
-wget -O sqlite-amalgamation.zip https://www.sqlite.org/2024/sqlite-amalgamation-3450300.zip
-unzip sqlite-amalgamation.zip
-mv sqlite-amalgamation-3450300/* vendor/
-rmdir sqlite-amalgamation-3450300
-rm sqlite-amalgamation.zip
-
-# build loadable module
-make loadable
-
-# install it
-cp dist/vec0.* /usr/local/lib
-

docker/main/install_deps.sh

@@ -8,13 +8,11 @@ apt-get -qq install --no-install-recommends -y \
     apt-transport-https \
     gnupg \
     wget \
-    lbzip2 \
     procps vainfo \
     unzip locales tzdata libxml2 xz-utils \
     python3.9 \
     python3-pip \
     curl \
-    lsof \
     jq \
     nethogs
 
@@ -41,68 +39,39 @@ apt-get -qq install --no-install-recommends --no-install-suggests -y \
 
 # btbn-ffmpeg -> amd64
 if [[ "${TARGETARCH}" == "amd64" ]]; then
-    mkdir -p /usr/lib/ffmpeg/5.0
-    mkdir -p /usr/lib/ffmpeg/7.0
+    mkdir -p /usr/lib/btbn-ffmpeg
     wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linux64-gpl-5.1.tar.xz"
-    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1
-    rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/5.0/doc /usr/lib/ffmpeg/5.0/bin/ffplay
-    wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linux64-gpl-7.0.tar.xz"
-    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1
-    rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/7.0/doc /usr/lib/ffmpeg/7.0/bin/ffplay
+    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/btbn-ffmpeg --strip-components 1
+    rm -rf btbn-ffmpeg.tar.xz /usr/lib/btbn-ffmpeg/doc /usr/lib/btbn-ffmpeg/bin/ffplay
 fi
 
 # ffmpeg -> arm64
 if [[ "${TARGETARCH}" == "arm64" ]]; then
-    mkdir -p /usr/lib/ffmpeg/5.0
-    mkdir -p /usr/lib/ffmpeg/7.0
+    mkdir -p /usr/lib/btbn-ffmpeg
     wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linuxarm64-gpl-5.1.tar.xz"
-    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1
-    rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/5.0/doc /usr/lib/ffmpeg/5.0/bin/ffplay
-    wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linuxarm64-gpl-7.0.tar.xz"
-    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1
-    rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/7.0/doc /usr/lib/ffmpeg/7.0/bin/ffplay
+    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/btbn-ffmpeg --strip-components 1
+    rm -rf btbn-ffmpeg.tar.xz /usr/lib/btbn-ffmpeg/doc /usr/lib/btbn-ffmpeg/bin/ffplay
 fi
 
 # arch specific packages
 if [[ "${TARGETARCH}" == "amd64" ]]; then
-    # use debian bookworm for amd / intel-i965 driver packages
+    # use debian bookworm for hwaccel packages
     echo 'deb https://deb.debian.org/debian bookworm main contrib non-free' >/etc/apt/sources.list.d/debian-bookworm.list
     apt-get -qq update
     apt-get -qq install --no-install-recommends --no-install-suggests -y \
-        i965-va-driver intel-gpu-tools onevpl-tools \
-        libva-drm2 \
-        mesa-va-drivers radeontop
-
+        intel-opencl-icd \
+        mesa-va-drivers radeontop libva-drm2 intel-media-va-driver-non-free i965-va-driver libmfx1 intel-gpu-tools
     # something about this dependency requires it to be installed in a separate call rather than in the line above
     apt-get -qq install --no-install-recommends --no-install-suggests -y \
         i965-va-driver-shaders
-
-    # intel packages use zst compression so we need to update dpkg
-    apt-get install -y dpkg
-
     rm -f /etc/apt/sources.list.d/debian-bookworm.list
-
-    # use intel apt intel packages
-    wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
-    echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | tee /etc/apt/sources.list.d/intel-gpu-jammy.list
-    apt-get -qq update
-    apt-get -qq install --no-install-recommends --no-install-suggests -y \
-        intel-opencl-icd=24.35.30872.31-996~22.04 intel-level-zero-gpu=1.3.29735.27-914~22.04 intel-media-va-driver-non-free=24.3.3-996~22.04 \
-        libmfx1=23.2.2-880~22.04 libmfxgen1=24.2.4-914~22.04 libvpl2=1:2.13.0.0-996~22.04
-
-    rm -f /usr/share/keyrings/intel-graphics.gpg
-    rm -f /etc/apt/sources.list.d/intel-gpu-jammy.list
 fi
 
 if [[ "${TARGETARCH}" == "arm64" ]]; then
     apt-get -qq install --no-install-recommends --no-install-suggests -y \
-        libva-drm2 mesa-va-drivers radeontop
+        libva-drm2 mesa-va-drivers
 fi
 
-# install vulkan
-apt-get -qq install --no-install-recommends --no-install-suggests -y \
-    libvulkan1 mesa-vulkan-drivers
-
 apt-get purge gnupg apt-transport-https xz-utils -y
 apt-get clean autoclean -y
 apt-get autoremove --purge -y

docker/main/requirements-wheels.txt

@@ -1,14 +1,8 @@
 click == 8.1.*
-# FastAPI
-aiohttp == 3.11.2
-starlette == 0.41.2
-starlette-context == 0.3.6
-fastapi == 0.115.*
-uvicorn == 0.30.*
-slowapi == 0.1.*
+Flask == 3.0.*
+Flask_Limiter == 3.7.*
 imutils == 0.5.*
-joserfc == 1.0.*
-pathvalidate == 3.2.*
+joserfc == 0.11.*
 markupsafe == 2.1.*
 mypy == 1.6.1
 numpy == 1.26.*
@@ -17,14 +11,16 @@ opencv-python-headless == 4.9.0.*
 paho-mqtt == 2.1.*
 pandas == 2.2.*
 peewee == 3.17.*
-peewee_migrate == 1.13.*
-psutil == 6.1.*
-pydantic == 2.8.*
+peewee_migrate == 1.12.*
+psutil == 5.9.*
+pydantic == 2.7.*
 git+https://github.com/fbcotter/py3nvml#egg=py3nvml
-pytz == 2024.*
-pyzmq == 26.2.*
+PyYAML == 6.0.*
+pytz == 2024.1
+pyzmq == 26.0.*
 ruamel.yaml == 0.18.*
 tzlocal == 5.2
+types-PyYAML == 6.0.*
 requests == 2.32.*
 types-requests == 2.32.*
 scipy == 1.13.*
@@ -32,16 +28,5 @@ norfair == 2.2.*
 setproctitle == 1.3.*
 ws4py == 0.5.*
 unidecode == 1.3.*
-# OpenVino & ONNX
-openvino == 2024.3.*
-onnxruntime-openvino == 1.19.* ; platform_machine == 'x86_64'
-onnxruntime == 1.19.* ; platform_machine == 'aarch64'
-# Embeddings
-transformers == 4.45.*
-# Generative AI
-google-generativeai == 0.8.*
-ollama == 0.3.*
-openai == 1.51.*
-# push notifications
-py-vapid == 1.9.*
-pywebpush == 2.0.*
+onnxruntime == 1.18.*
+openvino == 2024.1.*

docker/main/rootfs/etc/s6-overlay/s6-rc.d/frigate/run

@@ -42,14 +42,10 @@ function migrate_db_path() {
     fi
 }
 
-function set_libva_version() {
-    local ffmpeg_path=$(python3 /usr/local/ffmpeg/get_ffmpeg_path.py)
-    export LIBAVFORMAT_VERSION_MAJOR=$($ffmpeg_path -version | grep -Po "libavformat\W+\K\d+")
-}
-
 echo "[INFO] Preparing Frigate..."
 migrate_db_path
-set_libva_version
+export LIBAVFORMAT_VERSION_MAJOR=$(ffmpeg -version | grep -Po 'libavformat\W+\K\d+')
+
 echo "[INFO] Starting Frigate..."
 
 cd /opt/frigate || echo "[ERROR] Failed to change working directory to /opt/frigate"

docker/main/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/run

@@ -43,10 +43,7 @@ function get_ip_and_port_from_supervisor() {
     export FRIGATE_GO2RTC_WEBRTC_CANDIDATE_INTERNAL="${ip_address}:${webrtc_port}"
 }
 
-function set_libva_version() {
-    local ffmpeg_path=$(python3 /usr/local/ffmpeg/get_ffmpeg_path.py)
-    export LIBAVFORMAT_VERSION_MAJOR=$($ffmpeg_path -version | grep -Po "libavformat\W+\K\d+")
-}
+export LIBAVFORMAT_VERSION_MAJOR=$(ffmpeg -version | grep -Po 'libavformat\W+\K\d+')
 
 if [[ -f "/dev/shm/go2rtc.yaml" ]]; then
     echo "[INFO] Removing stale config from last run..."
@@ -66,8 +63,6 @@ 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
 
-set_libva_version
-
 readonly config_path="/config"
 
 if [[ -x "${config_path}/go2rtc" ]]; then

docker/main/rootfs/usr/local/ffmpeg/get_ffmpeg_path.py

@@ -1,45 +0,0 @@
-import json
-import os
-import shutil
-import sys
-
-from ruamel.yaml import YAML
-
-sys.path.insert(0, "/opt/frigate")
-from frigate.const import (
-    DEFAULT_FFMPEG_VERSION,
-    INCLUDED_FFMPEG_VERSIONS,
-)
-
-sys.path.remove("/opt/frigate")
-
-yaml = YAML()
-
-config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
-
-# Check if we can use .yaml instead of .yml
-config_file_yaml = config_file.replace(".yml", ".yaml")
-if os.path.isfile(config_file_yaml):
-    config_file = config_file_yaml
-
-try:
-    with open(config_file) as f:
-        raw_config = f.read()
-
-    if config_file.endswith((".yaml", ".yml")):
-        config: dict[str, any] = yaml.load(raw_config)
-    elif config_file.endswith(".json"):
-        config: dict[str, any] = json.loads(raw_config)
-except FileNotFoundError:
-    config: dict[str, any] = {}
-
-path = config.get("ffmpeg", {}).get("path", "default")
-if path == "default":
-    if shutil.which("ffmpeg") is None:
-        print(f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg")
-    else:
-        print("ffmpeg")
-elif path in INCLUDED_FFMPEG_VERSIONS:
-    print(f"/usr/lib/ffmpeg/{path}/bin/ffmpeg")
-else:
-    print(f"{path}/bin/ffmpeg")

docker/main/rootfs/usr/local/go2rtc/create_config.py

@@ -2,23 +2,19 @@
 
 import json
 import os
-import shutil
 import sys
 from pathlib import Path
 
-from ruamel.yaml import YAML
+import yaml
 
 sys.path.insert(0, "/opt/frigate")
-from frigate.const import (
-    BIRDSEYE_PIPE,
-    DEFAULT_FFMPEG_VERSION,
-    INCLUDED_FFMPEG_VERSIONS,
+from frigate.const import BIRDSEYE_PIPE  # noqa: E402
+from frigate.ffmpeg_presets import (  # noqa: E402
+    parse_preset_hardware_acceleration_encode,
 )
-from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
 
 sys.path.remove("/opt/frigate")
 
-yaml = YAML()
 
 FRIGATE_ENV_VARS = {k: v for k, v in os.environ.items() if k.startswith("FRIGATE_")}
 # read docker secret files as env vars too
@@ -41,7 +37,7 @@ try:
         raw_config = f.read()
 
     if config_file.endswith((".yaml", ".yml")):
-        config: dict[str, any] = yaml.load(raw_config)
+        config: dict[str, any] = yaml.safe_load(raw_config)
     elif config_file.endswith(".json"):
         config: dict[str, any] = json.loads(raw_config)
 except FileNotFoundError:
@@ -109,32 +105,16 @@ else:
             **FRIGATE_ENV_VARS
         )
 
-# ensure ffmpeg path is set correctly
-path = config.get("ffmpeg", {}).get("path", "default")
-if path == "default":
-    if shutil.which("ffmpeg") is None:
-        ffmpeg_path = f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg"
-    else:
-        ffmpeg_path = "ffmpeg"
-elif path in INCLUDED_FFMPEG_VERSIONS:
-    ffmpeg_path = f"/usr/lib/ffmpeg/{path}/bin/ffmpeg"
-else:
-    ffmpeg_path = f"{path}/bin/ffmpeg"
-
-if go2rtc_config.get("ffmpeg") is None:
-    go2rtc_config["ffmpeg"] = {"bin": ffmpeg_path}
-elif go2rtc_config["ffmpeg"].get("bin") is None:
-    go2rtc_config["ffmpeg"]["bin"] = ffmpeg_path
-
 # need to replace ffmpeg command when using ffmpeg4
-if int(os.environ.get("LIBAVFORMAT_VERSION_MAJOR", "59") or "59") < 59:
-    if go2rtc_config["ffmpeg"].get("rtsp") is None:
+if int(os.environ["LIBAVFORMAT_VERSION_MAJOR"]) < 59:
+    if go2rtc_config.get("ffmpeg") is None:
+        go2rtc_config["ffmpeg"] = {
+            "rtsp": "-fflags nobuffer -flags low_delay -stimeout 5000000 -user_agent go2rtc/ffmpeg -rtsp_transport tcp -i {input}"
+        }
+    elif go2rtc_config["ffmpeg"].get("rtsp") is None:
         go2rtc_config["ffmpeg"]["rtsp"] = (
             "-fflags nobuffer -flags low_delay -stimeout 5000000 -user_agent go2rtc/ffmpeg -rtsp_transport tcp -i {input}"
         )
-else:
-    if go2rtc_config.get("ffmpeg") is None:
-        go2rtc_config["ffmpeg"] = {"path": ""}
 
 for name in go2rtc_config.get("streams", {}):
     stream = go2rtc_config["streams"][name]
@@ -165,7 +145,7 @@ if config.get("birdseye", {}).get("restream", False):
     birdseye: dict[str, any] = config.get("birdseye")
 
     input = f"-f rawvideo -pix_fmt yuv420p -video_size {birdseye.get('width', 1280)}x{birdseye.get('height', 720)} -r 10 -i {BIRDSEYE_PIPE}"
-    ffmpeg_cmd = f"exec:{parse_preset_hardware_acceleration_encode(ffmpeg_path, config.get('ffmpeg', {}).get('hwaccel_args', ''), input, '-rtsp_transport tcp -f rtsp {output}')}"
+    ffmpeg_cmd = f"exec:{parse_preset_hardware_acceleration_encode(config.get('ffmpeg', {}).get('hwaccel_args'), input, '-rtsp_transport tcp -f rtsp {output}')}"
 
     if go2rtc_config.get("streams"):
         go2rtc_config["streams"]["birdseye"] = ffmpeg_cmd

docker/main/rootfs/usr/local/nginx/conf/nginx.conf

@@ -104,8 +104,6 @@ http {
 
             add_header Cache-Control "no-store";
             expires off;
-
-            keepalive_disable safari;
         }
 
         location /stream/ {
@@ -226,7 +224,7 @@ http {
 
         location ~* /api/.*\.(jpg|jpeg|png|webp|gif)$ {
             include auth_request.conf;
-            rewrite ^/api/(.*)$ /$1 break;
+            rewrite ^/api/(.*)$ $1 break;
             proxy_pass http://frigate_api;
             include proxy.conf;
         }

docker/main/rootfs/usr/local/nginx/get_tls_settings.py

@@ -3,9 +3,7 @@
 import json
 import os
 
-from ruamel.yaml import YAML
-
-yaml = YAML()
+import yaml
 
 config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
 
@@ -19,7 +17,7 @@ try:
         raw_config = f.read()
 
     if config_file.endswith((".yaml", ".yml")):
-        config: dict[str, any] = yaml.load(raw_config)
+        config: dict[str, any] = yaml.safe_load(raw_config)
     elif config_file.endswith(".json"):
         config: dict[str, any] = json.loads(raw_config)
 except FileNotFoundError:

docker/rockchip/Dockerfile

@@ -22,6 +22,5 @@ ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.0.0/librknnrt
 
 RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffmpeg
 RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffprobe
-ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-7/ffmpeg /usr/lib/ffmpeg/6.0/bin/
-ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-7/ffprobe /usr/lib/ffmpeg/6.0/bin/
-ENV PATH="/usr/lib/ffmpeg/6.0/bin/:${PATH}"
+ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-5/ffmpeg /usr/lib/btbn-ffmpeg/bin/
+ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-5/ffprobe /usr/lib/btbn-ffmpeg/bin/

docker/rockchip/rk.mk

@@ -1,15 +1,10 @@
 BOARDS += rk
 
 local-rk: version
-	docker buildx bake --file=docker/rockchip/rk.hcl rk \
-		--set rk.tags=frigate:latest-rk \
-		--load
+	docker buildx bake --load --file=docker/rockchip/rk.hcl --set rk.tags=frigate:latest-rk rk
 
 build-rk: version
-	docker buildx bake --file=docker/rockchip/rk.hcl rk \
-		--set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk
+	docker buildx bake --file=docker/rockchip/rk.hcl --set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk rk
 
 push-rk: build-rk
-	docker buildx bake --file=docker/rockchip/rk.hcl rk \
-		--set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk \
-		--push
+	docker buildx bake --push --file=docker/rockchip/rk.hcl --set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk rk

docker/rocm/Dockerfile

@@ -23,11 +23,11 @@ COPY docker/rocm/rocm-pin-600 /etc/apt/preferences.d/
 
 RUN apt-get update
 
-RUN apt-get -y install --no-install-recommends migraphx hipfft roctracer
+RUN apt-get -y install --no-install-recommends migraphx
 RUN apt-get -y install --no-install-recommends migraphx-dev
 
 RUN mkdir -p /opt/rocm-dist/opt/rocm-$ROCM/lib
-RUN cd /opt/rocm-$ROCM/lib && cp -dpr libMIOpen*.so* libamd*.so* libhip*.so* libhsa*.so* libmigraphx*.so* librocm*.so* librocblas*.so* libroctracer*.so* librocfft*.so* /opt/rocm-dist/opt/rocm-$ROCM/lib/
+RUN cd /opt/rocm-$ROCM/lib && cp -dpr libMIOpen*.so* libamd*.so* libhip*.so* libhsa*.so* libmigraphx*.so* librocm*.so* librocblas*.so* /opt/rocm-dist/opt/rocm-$ROCM/lib/
 RUN cd /opt/rocm-dist/opt/ && ln -s rocm-$ROCM rocm
 
 RUN mkdir -p /opt/rocm-dist/etc/ld.so.conf.d/
@@ -69,11 +69,7 @@ RUN apt-get -y install libnuma1
 
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
-
-COPY docker/rocm/requirements-wheels-rocm.txt /requirements.txt
-RUN python3 -m pip install --upgrade pip \
-    && pip3 uninstall -y onnxruntime-openvino \
-    && pip3 install -r /requirements.txt
+COPY docker/rocm/rootfs/ /
 
 #######################################################################
 FROM scratch AS rocm-dist
@@ -83,7 +79,6 @@ ARG AMDGPU
 
 COPY --from=rocm /opt/rocm-$ROCM/bin/rocminfo /opt/rocm-$ROCM/bin/migraphx-driver /opt/rocm-$ROCM/bin/
 COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*$AMDGPU* /opt/rocm-$ROCM/share/miopen/db/
-COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx908* /opt/rocm-$ROCM/share/miopen/db/
 COPY --from=rocm /opt/rocm-$ROCM/lib/rocblas/library/*$AMDGPU* /opt/rocm-$ROCM/lib/rocblas/library/
 COPY --from=rocm /opt/rocm-dist/ /
 COPY --from=debian-build /opt/rocm/lib/migraphx.cpython-39-x86_64-linux-gnu.so /opt/rocm-$ROCM/lib/
@@ -106,3 +101,6 @@ ENV HSA_OVERRIDE_GFX_VERSION=$HSA_OVERRIDE_GFX_VERSION
 #######################################################################
 FROM rocm-prelim-hsa-override$HSA_OVERRIDE as rocm-deps
 
+# Request yolov8 download at startup
+ENV DOWNLOAD_YOLOV8=1
+

docker/rocm/requirements-wheels-rocm.txt

@@ -1 +0,0 @@
-onnxruntime-rocm @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v1.0.0/onnxruntime_rocm-1.17.3-cp39-cp39-linux_x86_64.whl

docker/rocm/rocm.mk

@@ -4,50 +4,14 @@ BOARDS += rocm
 ROCM_CHIPSETS:=gfx900:9.0.0 gfx1030:10.3.0 gfx1100:11.0.0
 
 local-rocm: version
-	$(foreach chipset,$(ROCM_CHIPSETS), \
-		AMDGPU=$(word 1,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE=1 \
-		docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-			--set rocm.tags=frigate:latest-rocm-$(word 1,$(subst :, ,$(chipset))) \
-			--load \
-	&&) true
-	
-	unset HSA_OVERRIDE_GFX_VERSION && \
-	HSA_OVERRIDE=0 \
-	AMDGPU=gfx \
-	docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-		--set rocm.tags=frigate:latest-rocm \
-		--load
+	$(foreach chipset,$(ROCM_CHIPSETS),AMDGPU=$(word 1,$(subst :, ,$(chipset))) HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) HSA_OVERRIDE=1 docker buildx bake --load --file=docker/rocm/rocm.hcl --set rocm.tags=frigate:latest-rocm-$(word 1,$(subst :, ,$(chipset))) rocm;)
+	unset HSA_OVERRIDE_GFX_VERSION && HSA_OVERRIDE=0 AMDGPU=gfx docker buildx bake --load --file=docker/rocm/rocm.hcl --set rocm.tags=frigate:latest-rocm rocm
 
 build-rocm: version
-	$(foreach chipset,$(ROCM_CHIPSETS), \
-		AMDGPU=$(word 1,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE=1 \
-		docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-			--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) \
-	&&) true
-
-	unset HSA_OVERRIDE_GFX_VERSION && \
-	HSA_OVERRIDE=0 \
-	AMDGPU=gfx \
-	docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-		--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm
+	$(foreach chipset,$(ROCM_CHIPSETS),AMDGPU=$(word 1,$(subst :, ,$(chipset))) HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) HSA_OVERRIDE=1 docker buildx bake --file=docker/rocm/rocm.hcl --set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) rocm;)
+	unset HSA_OVERRIDE_GFX_VERSION && HSA_OVERRIDE=0 AMDGPU=gfx docker buildx bake --file=docker/rocm/rocm.hcl --set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm rocm
 
 push-rocm: build-rocm
-	$(foreach chipset,$(ROCM_CHIPSETS), \
-		AMDGPU=$(word 1,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE=1 \
-		docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-			--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) \
-			--push \
-	&&) true
+	$(foreach chipset,$(ROCM_CHIPSETS),AMDGPU=$(word 1,$(subst :, ,$(chipset))) HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) HSA_OVERRIDE=1 docker buildx bake --push --file=docker/rocm/rocm.hcl --set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) rocm;)
+	unset HSA_OVERRIDE_GFX_VERSION && HSA_OVERRIDE=0 AMDGPU=gfx docker buildx bake --push --file=docker/rocm/rocm.hcl --set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm rocm
 
-	unset HSA_OVERRIDE_GFX_VERSION && \
-	HSA_OVERRIDE=0 \
-	AMDGPU=gfx \
-	docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-		--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm \
-		--push

docker/rocm/rootfs/etc/s6-overlay/s6-rc.d/compile-rocm-models/run

@@ -0,0 +1,20 @@
+#!/command/with-contenv bash
+# shellcheck shell=bash
+# Compile YoloV8 ONNX files into ROCm MIGraphX files
+
+OVERRIDE=$(cd /opt/frigate && python3 -c 'import frigate.detectors.plugins.rocm as rocm; print(rocm.auto_override_gfx_version())')
+
+if ! test -z "$OVERRIDE"; then
+  echo "Using HSA_OVERRIDE_GFX_VERSION=${OVERRIDE}"
+  export HSA_OVERRIDE_GFX_VERSION=$OVERRIDE
+fi
+
+for onnx in /config/model_cache/yolov8/*.onnx
+do
+  mxr="${onnx%.onnx}.mxr"
+  if ! test -f $mxr; then
+    echo "processing $onnx into $mxr"
+    /opt/rocm/bin/migraphx-driver compile $onnx --optimize --gpu --enable-offload-copy --binary -o $mxr
+  fi
+done
+

docker/rocm/rootfs/etc/s6-overlay/s6-rc.d/compile-rocm-models/type

@@ -0,0 +1 @@
+oneshot

docker/rocm/rootfs/etc/s6-overlay/s6-rc.d/compile-rocm-models/up

@@ -0,0 +1 @@
+/etc/s6-overlay/s6-rc.d/compile-rocm-models/run

docker/rpi/rpi.mk

@@ -1,15 +1,10 @@
 BOARDS += rpi
 
 local-rpi: version
-	docker buildx bake --file=docker/rpi/rpi.hcl rpi \
-		--set rpi.tags=frigate:latest-rpi \
-		--load
+	docker buildx bake --load --file=docker/rpi/rpi.hcl --set rpi.tags=frigate:latest-rpi rpi
 
 build-rpi: version
-	docker buildx bake --file=docker/rpi/rpi.hcl rpi \
-		--set rpi.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rpi
+	docker buildx bake --file=docker/rpi/rpi.hcl --set rpi.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rpi rpi
 
 push-rpi: build-rpi
-	docker buildx bake --file=docker/rpi/rpi.hcl rpi \
-		--set rpi.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rpi \
-		--push
+	docker buildx bake --push --file=docker/rpi/rpi.hcl --set rpi.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rpi rpi

docker/tensorrt/Dockerfile.amd64

@@ -18,7 +18,6 @@ RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels
     pip3 install -U /deps/trt-wheels/*.whl && \
     ldconfig
 
-ENV LD_LIBRARY_PATH=/usr/local/lib/python3.9/dist-packages/tensorrt:/usr/local/cuda/lib64:/usr/local/lib/python3.9/dist-packages/nvidia/cufft/lib
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
 
@@ -27,7 +26,6 @@ FROM devcontainer AS devcontainer-trt
 
 COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
 COPY --from=trt-deps /usr/local/src/tensorrt_demos /usr/local/src/tensorrt_demos
-COPY --from=trt-deps /usr/local/cuda-12.1 /usr/local/cuda
 COPY docker/tensorrt/detector/rootfs/ /
 COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \

docker/tensorrt/Dockerfile.arm64

@@ -10,8 +10,8 @@ ARG DEBIAN_FRONTEND
 # Use a separate container to build wheels to prevent build dependencies in final image
 RUN apt-get -qq update \
     && apt-get -qq install -y --no-install-recommends \
-    python3.9 python3.9-dev \
-    wget build-essential cmake git \
+       python3.9 python3.9-dev \
+       wget build-essential cmake git \
     && rm -rf /var/lib/apt/lists/*
 
 # Ensure python3 defaults to python3.9
@@ -41,11 +41,7 @@ RUN --mount=type=bind,source=docker/tensorrt/detector/build_python_tensorrt.sh,t
     && TENSORRT_VER=$(cat /etc/TENSORRT_VER) /deps/build_python_tensorrt.sh
 
 COPY docker/tensorrt/requirements-arm64.txt /requirements-tensorrt.txt
-ADD https://nvidia.box.com/shared/static/9aemm4grzbbkfaesg5l7fplgjtmswhj8.whl /tmp/onnxruntime_gpu-1.15.1-cp39-cp39-linux_aarch64.whl
-
-RUN pip3 uninstall -y onnxruntime-openvino \
-    && pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt \
-    && pip3 install --no-deps /tmp/onnxruntime_gpu-1.15.1-cp39-cp39-linux_aarch64.whl
+RUN pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt
 
 FROM build-wheels AS trt-model-wheels
 ARG DEBIAN_FRONTEND

docker/tensorrt/Dockerfile.base

@@ -24,9 +24,8 @@ ENV S6_CMD_WAIT_FOR_SERVICES_MAXTIME=0
 
 COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
 COPY --from=trt-deps /usr/local/src/tensorrt_demos /usr/local/src/tensorrt_demos
-COPY --from=trt-deps /usr/local/cuda-12.* /usr/local/cuda
 COPY docker/tensorrt/detector/rootfs/ /
-ENV YOLO_MODELS=""
+ENV YOLO_MODELS="yolov7-320"
 
 HEALTHCHECK --start-period=600s --start-interval=5s --interval=15s --timeout=5s --retries=3 \
     CMD curl --fail --silent --show-error http://127.0.0.1:5000/api/version || exit 1

docker/tensorrt/detector/rootfs/etc/s6-overlay/s6-rc.d/trt-model-prepare/run

@@ -11,7 +11,6 @@ set -o errexit -o nounset -o pipefail
 MODEL_CACHE_DIR=${MODEL_CACHE_DIR:-"/config/model_cache/tensorrt"}
 TRT_VER=${TRT_VER:-$(cat /etc/TENSORRT_VER)}
 OUTPUT_FOLDER="${MODEL_CACHE_DIR}/${TRT_VER}"
-YOLO_MODELS=${YOLO_MODELS:-""}
 
 # Create output folder
 mkdir -p ${OUTPUT_FOLDER}
@@ -20,11 +19,6 @@ FIRST_MODEL=true
 MODEL_DOWNLOAD=""
 MODEL_CONVERT=""
 
-if [ -z "$YOLO_MODELS" ]; then
-    echo "tensorrt model preparation disabled"
-    exit 0
-fi
-
 for model in ${YOLO_MODELS//,/ }
 do
     # Remove old link in case path/version changed

docker/tensorrt/requirements-amd64.txt

@@ -8,7 +8,5 @@ nvidia-cuda-runtime-cu12 == 12.1.*; platform_machine == 'x86_64'
 nvidia-cuda-runtime-cu11 == 11.8.*; platform_machine == 'x86_64'
 nvidia-cublas-cu11 == 11.11.3.6; platform_machine == 'x86_64'
 nvidia-cudnn-cu11 == 8.6.0.*; platform_machine == 'x86_64'
-nvidia-cufft-cu11==10.*; platform_machine == 'x86_64'
-onnx==1.16.*; platform_machine == 'x86_64'
-onnxruntime-gpu==1.18.*; platform_machine == 'x86_64'
-protobuf==3.20.3; platform_machine == 'x86_64'
+onnx==1.14.0; platform_machine == 'x86_64'
+protobuf==3.20.3; platform_machine == 'x86_64'

docker/tensorrt/requirements-arm64.txt

@@ -1 +1 @@
-cuda-python == 11.7; platform_machine == 'aarch64'
+cuda-python == 11.7; platform_machine == 'aarch64'

docker/tensorrt/trt.mk

@@ -7,35 +7,20 @@ JETPACK4_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK4_BASE) SLIM_BASE=$(JETPACK4_BAS
 JETPACK5_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK5_BASE) SLIM_BASE=$(JETPACK5_BASE) TRT_BASE=$(JETPACK5_BASE)
 
 local-trt: version
-	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=frigate:latest-tensorrt \
-		--load
+	$(X86_DGPU_ARGS) docker buildx bake --load --file=docker/tensorrt/trt.hcl --set tensorrt.tags=frigate:latest-tensorrt tensorrt
 
 local-trt-jp4: version
-	$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=frigate:latest-tensorrt-jp4 \
-		--load
+	$(JETPACK4_ARGS) docker buildx bake --load --file=docker/tensorrt/trt.hcl --set tensorrt.tags=frigate:latest-tensorrt-jp4 tensorrt
 
 local-trt-jp5: version
-	$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=frigate:latest-tensorrt-jp5 \
-		--load
+	$(JETPACK5_ARGS) docker buildx bake --load --file=docker/tensorrt/trt.hcl --set tensorrt.tags=frigate:latest-tensorrt-jp5 tensorrt
 
 build-trt:
-	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt
-	$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4
-	$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5
+	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt tensorrt
+	$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4 tensorrt
+	$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5 tensorrt
 
 push-trt: build-trt
-	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt \
-		--push
-	$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4 \
-		--push
-	$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5 \
-		--push
+	$(X86_DGPU_ARGS) docker buildx bake --push --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt tensorrt
+	$(JETPACK4_ARGS) docker buildx bake --push --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4 tensorrt
+	$(JETPACK5_ARGS) docker buildx bake --push --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5 tensorrt

docs/README.md

@@ -1,10 +1,5 @@
 # Website
 
-This website is built using [Docusaurus 3.5](https://docusaurus.io/docs), a modern static website generator.
+This website is built using [Docusaurus 2](https://v2.docusaurus.io/), a modern static website generator.
 
 For installation and contributing instructions, please follow the [Contributing Docs](https://docs.frigate.video/development/contributing).
-
-# Development
-
-1. Run `npm i` to install dependencies
-2. Run `npm run start` to start the website

docs/docs/configuration/advanced.md

@@ -55,7 +55,7 @@ environment_vars:
 
 ### `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.
+Event 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.
 
 If you are storing your database on a network share (SMB, NFS, etc), you may get a `database is locked` error message on startup. You can customize the location of the database in the config if necessary.
 
@@ -176,19 +176,19 @@ listen [::]:5000 ipv6only=off;
 
 ### Custom ffmpeg build
 
-Included with Frigate is a build of ffmpeg that works for the vast majority of users. However, there exists some hardware setups which have incompatibilities with the included build. In this case, statically built ffmpeg binary can be downloaded to /config and used.
+Included with Frigate is a build of ffmpeg that works for the vast majority of users. However, there exists some hardware setups which have incompatibilities with the included build. In this case, a docker volume mapping can be used to overwrite the included ffmpeg build with an ffmpeg build that works for your specific hardware setup.
 
 To do this:
 
-1. Download your ffmpeg build and uncompress to the Frigate config folder.
+1. Download your ffmpeg build and uncompress to a folder on the host (let's use `/home/appdata/frigate/custom-ffmpeg` for this example).
 2. Update your docker-compose or docker CLI to include `'/home/appdata/frigate/custom-ffmpeg':'/usr/lib/btbn-ffmpeg':'ro'` in the volume mappings.
 3. Restart Frigate and the custom version will be used if the mapping was done correctly.
 
-NOTE: The folder that is set for the config needs to be the folder that contains `/bin`. So if the full structure is `/home/appdata/frigate/custom-ffmpeg/bin/ffmpeg` then the `ffmpeg -> path` field should be `/config/custom-ffmpeg/bin`.
+NOTE: The folder that is mapped from the host needs to be the folder that contains `/bin`. So if the full structure is `/home/appdata/frigate/custom-ffmpeg/bin/ffmpeg` then `/home/appdata/frigate/custom-ffmpeg` needs to be mapped to `/usr/lib/btbn-ffmpeg`.
 
 ### Custom go2rtc version
 
-Frigate currently includes go2rtc v1.9.2, there may be certain cases where you want to run a different version of go2rtc.
+Frigate currently includes go2rtc v1.9.4, there may be certain cases where you want to run a different version of go2rtc.
 
 To do this:
 
@@ -203,16 +203,16 @@ When frigate starts up, it checks whether your config file is valid, and if it i
 
 ### Via API
 
-Frigate can accept a new configuration file as JSON at the `/api/config/save` endpoint. When updating the config this way, Frigate will validate the config before saving it, and return a `400` if the config is not valid.
+Frigate can accept a new configuration file as JSON at the `/config/save` endpoint. When updating the config this way, Frigate will validate the config before saving it, and return a `400` if the config is not valid.
 
 ```bash
-curl -X POST http://frigate_host:5000/api/config/save -d @config.json
+curl -X POST http://frigate_host:5000/config/save -d @config.json
 ```
 
 if you'd like you can use your yaml config directly by using [`yq`](https://github.com/mikefarah/yq) to convert it to json:
 
 ```bash
-yq r -j config.yml | curl -X POST http://frigate_host:5000/api/config/save -d @-
+yq r -j config.yml | curl -X POST http://frigate_host:5000/config/save -d @-
 ```
 
 ### Via Command Line
@@ -225,5 +225,5 @@ docker run                                \
   --entrypoint python3                    \
   ghcr.io/blakeblackshear/frigate:stable  \
   -u -m frigate                           \
-  --validate-config
+  --validate_config
 ```

docs/docs/configuration/authentication.md

@@ -31,7 +31,7 @@ auth:
 
 ## Login failure rate limiting
 
-In order to limit the risk of brute force attacks, rate limiting is available for login failures. This is implemented with SlowApi, and the string notation for valid values is available in [the documentation](https://limits.readthedocs.io/en/stable/quickstart.html#examples).
+In order to limit the risk of brute force attacks, rate limiting is available for login failures. This is implemented with Flask-Limiter, and the string notation for valid values is available in [the documentation](https://flask-limiter.readthedocs.io/en/stable/configuration.html#rate-limit-string-notation).
 
 For example, `1/second;5/minute;20/hour` will rate limit the login endpoint when failures occur more than:
 

docs/docs/configuration/autotracking.md

@@ -41,7 +41,6 @@ cameras:
     ...
     onvif:
       # Required: host of the camera being connected to.
-      # NOTE: HTTP is assumed by default; HTTPS is supported if you specify the scheme, ex: "https://0.0.0.0".
       host: 0.0.0.0
       # Optional: ONVIF port for device (default: shown below).
       port: 8000
@@ -50,8 +49,6 @@ cameras:
       user: admin
       # Optional: password for login.
       password: admin
-      # Optional: Skip TLS verification from the ONVIF server (default: shown below)
-      tls_insecure: False
       # Optional: PTZ camera object autotracking. Keeps a moving object in
       # the center of the frame by automatically moving the PTZ camera.
       autotracking:

docs/docs/configuration/camera_specific.md

@@ -65,18 +65,6 @@ ffmpeg:
 
 ## Model/vendor specific setup
 
-### Amcrest & Dahua
-
-Amcrest & Dahua cameras should be connected to via RTSP using the following format:
-
-```
-rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=0 # this is the main stream
-rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=1 # this is the sub stream, typically supporting low resolutions only
-rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=2 # higher end cameras support a third stream with a mid resolution (1280x720, 1920x1080)
-rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=3 # new higher end cameras support a fourth stream with another mid resolution (1280x720, 1920x1080)
-
-```
-
 ### Annke C800
 
 This camera is H.265 only. To be able to play clips on some devices (like MacOs or iPhone) the H.265 stream has to be repackaged and the audio stream has to be converted to aac. Unfortunately direct playback of in the browser is not working (yet), but the downloaded clip can be played locally.
@@ -89,7 +77,7 @@ cameras:
         record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -tag:v hvc1 -bsf:v hevc_mp4toannexb -c:a aac
 
       inputs:
-        - path: rtsp://USERNAME:PASSWORD@CAMERA-IP/H264/ch1/main/av_stream # <----- Update for your camera
+        - path: rtsp://user:password@camera-ip:554/H264/ch1/main/av_stream # <----- Update for your camera
           roles:
             - detect
             - record
@@ -107,29 +95,6 @@ ffmpeg:
   input_args: preset-rtsp-blue-iris
 ```
 
-### Hikvision Cameras
-
-Hikvision cameras should be connected to via RTSP using the following format:
-
-```
-rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/101 # this is the main stream
-rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/102 # this is the sub stream, typically supporting low resolutions only
-rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/103 # higher end cameras support a third stream with a mid resolution (1280x720, 1920x1080)
-```
-
-:::note
-
-[Some users have reported](https://www.reddit.com/r/frigate_nvr/comments/1hg4ze7/hikvision_security_settings) that newer Hikvision cameras require adjustments to the security settings:
-
-```
-RTSP Authentication - digest/basic
-RTSP Digest Algorithm - MD5
-WEB Authentication - digest/basic
-WEB Digest Algorithm  - MD5
-```
-
-:::
-
 ### Reolink Cameras
 
 Reolink has older cameras (ex: 410 & 520) as well as newer camera (ex: 520a & 511wa) which support different subsets of options. In both cases using the http stream is recommended.
@@ -191,9 +156,7 @@ cameras:
 
 #### Reolink Doorbell
 
-The reolink doorbell supports two way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
-
-Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
+The reolink doorbell supports 2-way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
 
 ```yaml
 go2rtc:
@@ -218,7 +181,7 @@ go2rtc:
       - rtspx://192.168.1.1:7441/abcdefghijk
 ```
 
-[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#source-rtsp)
+[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#source-rtsp)
 
 In the Unifi 2.0 update Unifi Protect Cameras had a change in audio sample rate which causes issues for ffmpeg. The input rate needs to be set for record if used directly with unifi protect.
 
@@ -230,39 +193,4 @@ ffmpeg:
 
 ### TP-Link VIGI Cameras
 
-TP-Link VIGI cameras need some adjustments to the main stream settings on the camera itself to avoid issues. The stream needs to be configured as `H264` with `Smart Coding` set to `off`. Without these settings you may have problems when trying to watch recorded footage. For example Firefox will stop playback after a few seconds and show the following error message: `The media playback was aborted due to a corruption problem or because the media used features your browser did not support.`.
-
-## USB Cameras (aka Webcams)
-
-To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's [FFmpeg Device](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg-device) support:
-
-- Preparation outside of Frigate:
-  - Get USB camera path. Run `v4l2-ctl --list-devices` to get a listing of locally-connected cameras available. (You may need to install `v4l-utils` in a way appropriate for your Linux distribution). In the sample configuration below, we use `video=0` to correlate with a detected device path of `/dev/video0`
-  - Get USB camera formats & resolutions. Run `ffmpeg -f v4l2 -list_formats all -i /dev/video0` to get an idea of what formats and resolutions the USB Camera supports. In the sample configuration below, we use a width of 1024 and height of 576 in the stream and detection settings based on what was reported back.
-  - If using Frigate in a container (e.g. Docker on TrueNAS), ensure you have USB Passthrough support enabled, along with a specific Host Device (`/dev/video0`) + Container Device (`/dev/video0`) listed.
-
-- In your Frigate Configuration File, add the go2rtc stream and roles as appropriate:
-
-```
-go2rtc:
-  streams:
-    usb_camera:
-      - "ffmpeg:device?video=0&video_size=1024x576#video=h264" 
-
-cameras:
-  usb_camera:
-    enabled: true
-    ffmpeg:
-      inputs:
-        - path: rtsp://127.0.0.1:8554/usb_camera
-          input_args: preset-rtsp-restream
-          roles:
-            - detect
-            - record
-    detect:
-      enabled: false # <---- disable detection until you have a working camera feed
-      width: 1024
-      height: 576
-```
-
-
+TP-Link VIGI cameras need some adjustments to the main stream settings on the camera itself to avoid issues. The stream needs to be configured as `H264` with `Smart Coding` set to `off`. Without these settings you may have problems when trying to watch recorded events. For example Firefox will stop playback after a few seconds and show the following error message: `The media playback was aborted due to a corruption problem or because the media used features your browser did not support.`.

docs/docs/configuration/cameras.md

@@ -7,7 +7,7 @@ title: Camera Configuration
 
 Several inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create recordings from a higher resolution stream, or vice versa.
 
-A camera is enabled by default but can be temporarily disabled by using `enabled: False`. Existing tracked objects and recordings can still be accessed. Live streams, recording and detecting are not working. Camera specific configurations will be used.
+A camera is enabled by default but can be temporarily disabled by using `enabled: False`. Existing events and recordings can still be accessed. Live streams, recording and detecting are not working. Camera specific configurations will be used.
 
 Each role can only be assigned to one input per camera. The options for roles are as follows:
 
@@ -100,8 +100,6 @@ This list of working and non-working PTZ cameras is based on user feedback.
 | Ctronics PTZ                 |      ✅      |      ❌      |                                                                                                                                                 |
 | Dahua                        |      ✅      |      ✅      |                                                                                                                                                 |
 | Dahua DH-SD2A500HB           |      ✅      |      ❌      |                                                                                                                                                 |
-| Dahua DH-SD49825GB-HNR       |      ✅      |      ✅      |                                                                                                                                                 |
-| Dahua DH-P5AE-PV             |      ❌      |      ❌      |                                                                                                                                                 |
 | Foscam R5                    |      ✅      |      ❌      |                                                                                                                                                 |
 | Hanwha XNP-6550RH            |      ✅      |      ❌      |                                                                                                                                                 |
 | Hikvision                    |      ✅      |      ❌      | Incomplete ONVIF support (MoveStatus won't update even on latest firmware) - reported with HWP-N4215IH-DE and DS-2DE3304W-DE, but likely others |
@@ -111,7 +109,7 @@ This list of working and non-working PTZ cameras is based on user feedback.
 | Reolink E1 Zoom              |      ✅      |      ❌      |                                                                                                                                                 |
 | Reolink RLC-823A 16x         |      ✅      |      ❌      |                                                                                                                                                 |
 | Speco O8P32X                 |      ✅      |      ❌      |                                                                                                                                                 |
-| Sunba 405-D20X               |      ✅      |      ❌      | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatable.                                            |
+| Sunba 405-D20X               |      ✅      |      ❌      |                                                                                                                                                 |
 | Tapo                         |      ✅      |      ❌      | Many models supported, ONVIF Service Port: 2020                                                                                                 |
 | Uniview IPC672LR-AX4DUPK     |      ✅      |      ❌      | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands                                  |
 | Uniview IPC6612SR-X33-VG     |      ✅      |      ✅      | Leave `calibrate_on_startup` as `False`. A user has reported that zooming with `absolute` is working.                                           |

docs/docs/configuration/genai.md

@@ -1,220 +0,0 @@
----
-id: genai
-title: Generative AI
----
-
-Generative AI can be used to automatically generate descriptive text based on the thumbnails of your tracked objects. This helps with [Semantic Search](/configuration/semantic_search) in Frigate to provide more context about your tracked objects. Descriptions are accessed via the _Explore_ view in the Frigate UI by clicking on a tracked object's thumbnail.
-
-Requests for a description are sent off automatically to your AI provider at the end of the tracked object's lifecycle. Descriptions can also be regenerated manually via the Frigate UI.
-
-:::info
-
-Semantic Search must be enabled to use Generative AI.
-
-:::
-
-## Configuration
-
-Generative AI can be enabled for all cameras or only for specific cameras. There are currently 3 native providers available to integrate with Frigate. Other providers that support the OpenAI standard API can also be used. See the OpenAI section below.
-
-To use Generative AI, you must define a single provider at the global level of your Frigate configuration. 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_`.
-
-```yaml
-genai:
-  enabled: True
-  provider: gemini
-  api_key: "{FRIGATE_GEMINI_API_KEY}"
-  model: gemini-1.5-flash
-
-cameras:
-  front_camera: 
-    genai:
-      enabled: True # <- enable GenAI for your front camera
-      use_snapshot: True
-      objects:
-        - person
-      required_zones:
-        - steps
-  indoor_camera:
-    genai: 
-      enabled: False # <- disable GenAI for your indoor camera
-```
-
-By default, descriptions will be generated for all tracked objects and all zones. But you can also optionally specify `objects` and `required_zones` to only generate descriptions for certain tracked objects or zones.
-
-Optionally, you can generate the description using a snapshot (if enabled) by setting `use_snapshot` to `True`. By default, this is set to `False`, which sends the uncompressed images from the `detect` stream collected over the object's lifetime to the model. Once the object lifecycle ends, only a single compressed and cropped thumbnail is saved with the tracked object. Using a snapshot might be useful when you want to _regenerate_ a tracked object's description as it will provide the AI with a higher-quality image (typically downscaled by the AI itself) than the cropped/compressed thumbnail. Using a snapshot otherwise has a trade-off in that only a single image is sent to your provider, which will limit the model's ability to determine object movement or direction.
-
-## Ollama
-
-:::warning
-
-Using Ollama on CPU is not recommended, high inference times make using Generative AI impractical.
-
-:::
-
-[Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It provides a nice API over [llama.cpp](https://github.com/ggerganov/llama.cpp). It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance.
-
-Most of the 7b parameter 4-bit vision models will fit inside 8GB of VRAM. There is also a [Docker container](https://hub.docker.com/r/ollama/ollama) available.
-
-Parallel requests also come with some caveats. You will need to set `OLLAMA_NUM_PARALLEL=1` and choose a `OLLAMA_MAX_QUEUE` and `OLLAMA_MAX_LOADED_MODELS` values that are appropriate for your hardware and preferences. See the [Ollama documentation](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-does-ollama-handle-concurrent-requests).
-
-### Supported Models
-
-You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/library). At the time of writing, this includes `llava`, `llava-llama3`, `llava-phi3`, and `moondream`. Note that Frigate will not automatically download the model you specify in your config, you must download the model to your local instance of Ollama first i.e. by running `ollama pull llava:7b` on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.
-
-:::note
-
-You should have at least 8 GB of RAM available (or VRAM if running on GPU) to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
-
-:::
-
-### Configuration
-
-```yaml
-genai:
-  enabled: True
-  provider: ollama
-  base_url: http://localhost:11434
-  model: llava:7b
-```
-
-## Google Gemini
-
-Google Gemini has a free tier allowing [15 queries per minute](https://ai.google.dev/pricing) to the API, which is more than sufficient for standard Frigate usage.
-
-### Supported Models
-
-You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://ai.google.dev/gemini-api/docs/models/gemini). At the time of writing, this includes `gemini-1.5-pro` and `gemini-1.5-flash`.
-
-### Get API Key
-
-To start using Gemini, you must first get an API key from [Google AI Studio](https://aistudio.google.com).
-
-1. Accept the Terms of Service
-2. Click "Get API Key" from the right hand navigation
-3. Click "Create API key in new project"
-4. Copy the API key for use in your config
-
-### Configuration
-
-```yaml
-genai:
-  enabled: True
-  provider: gemini
-  api_key: "{FRIGATE_GEMINI_API_KEY}"
-  model: gemini-1.5-flash
-```
-
-## OpenAI
-
-OpenAI does not have a free tier for their API. With the release of gpt-4o, pricing has been reduced and each generation should cost fractions of a cent if you choose to go this route.
-
-### Supported Models
-
-You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://platform.openai.com/docs/models). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`.
-
-### Get API Key
-
-To start using OpenAI, you must first [create an API key](https://platform.openai.com/api-keys) and [configure billing](https://platform.openai.com/settings/organization/billing/overview).
-
-### Configuration
-
-```yaml
-genai:
-  enabled: True
-  provider: openai
-  api_key: "{FRIGATE_OPENAI_API_KEY}"
-  model: gpt-4o
-```
-
-:::note
-
-To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` environment variable to your provider's API URL.
-
-:::
-
-## Azure OpenAI
-
-Microsoft offers several vision models through Azure OpenAI. A subscription is required.
-
-### Supported Models
-
-You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`.
-
-### Create Resource and Get API Key
-
-To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key and resource URL, which must include the `api-version` parameter (see the example below). The model field is not required in your configuration as the model is part of the deployment name you chose when deploying the resource.
-
-### Configuration
-
-```yaml
-genai:
-  enabled: True
-  provider: azure_openai
-  base_url: https://example-endpoint.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2023-03-15-preview
-  api_key: "{FRIGATE_OPENAI_API_KEY}"
-```
-
-## Usage and Best Practices
-
-Frigate's thumbnail search excels at identifying specific details about tracked objects – for example, using an "image caption" approach to find a "person wearing a yellow vest," "a white dog running across the lawn," or "a red car on a residential street." To enhance this further, Frigate’s default prompts are designed to ask your AI provider about the intent behind the object's actions, rather than just describing its appearance.
-
-While generating simple descriptions of detected objects is useful, understanding intent provides a deeper layer of insight. Instead of just recognizing "what" is in a scene, Frigate’s default prompts aim to infer "why" it might be there or "what" it could do next. Descriptions tell you what’s happening, but intent gives context. For instance, a person walking toward a door might seem like a visitor, but if they’re moving quickly after hours, you can infer a potential break-in attempt. Detecting a person loitering near a door at night can trigger an alert sooner than simply noting "a person standing by the door," helping you respond based on the situation’s context.
-
-### Using GenAI for notifications
-
-Frigate provides an [MQTT topic](/integrations/mqtt), `frigate/tracked_object_update`, that is updated with a JSON payload containing `event_id` and `description` when your AI provider returns a description for a tracked object. This description could be used directly in notifications, such as sending alerts to your phone or making audio announcements. If additional details from the tracked object are needed, you can query the [HTTP API](/integrations/api/event-events-event-id-get) using the `event_id`, eg: `http://frigate_ip:5000/api/events/<event_id>`.
-
-## Custom Prompts
-
-Frigate sends multiple frames from the tracked object along with a prompt to your Generative AI provider asking it to generate a description. The default prompt is as follows:
-
-```
-Analyze the sequence of images containing the {label}. Focus on the likely intent or behavior of the {label} based on its actions and movement, rather than describing its appearance or the surroundings. Consider what the {label} is doing, why, and what it might do next.
-```
-
-:::tip
-
-Prompts can use variable replacements like `{label}`, `{sub_label}`, and `{camera}` to substitute information from the tracked object as part of the prompt.
-
-:::
-
-You are also able to define custom prompts in your configuration.
-
-```yaml
-genai:
-  enabled: True
-  provider: ollama
-  base_url: http://localhost:11434
-  model: llava
-  prompt: "Analyze the {label} in these images from the {camera} security camera. Focus on the actions, behavior, and potential intent of the {label}, rather than just describing its appearance."
-  object_prompts:
-    person: "Examine the main person in these images. What are they doing and what might their actions suggest about their intent (e.g., approaching a door, leaving an area, standing still)? Do not describe the surroundings or static details."
-    car: "Observe the primary vehicle in these images. Focus on its movement, direction, or purpose (e.g., parking, approaching, circling). If it's a delivery vehicle, mention the company."
-```
-
-Prompts can also be overriden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire. 
-
-```yaml
-cameras:
-  front_door:
-    genai:
-      use_snapshot: True
-      prompt: "Analyze the {label} in these images from the {camera} security camera at the front door. Focus on the actions and potential intent of the {label}."
-      object_prompts:
-        person: "Examine the person in these images. What are they doing, and how might their actions suggest their purpose (e.g., delivering something, approaching, leaving)? If they are carrying or interacting with a package, include details about its source or destination."
-        cat: "Observe the cat in these images. Focus on its movement and intent (e.g., wandering, hunting, interacting with objects). If the cat is near the flower pots or engaging in any specific actions, mention it."
-      objects:
-        - person
-        - cat
-      required_zones:
-        - steps
-```
-
-### Experiment with prompts
-
-Many providers also have a public facing chat interface for their models. Download a couple of different thumbnails or snapshots from Frigate and try new things in the playground to get descriptions to your liking before updating the prompt in Frigate.
-
-- OpenAI - [ChatGPT](https://chatgpt.com)
-- Gemini - [Google AI Studio](https://aistudio.google.com)
-- Ollama - [Open WebUI](https://docs.openwebui.com/)

docs/docs/configuration/hardware_acceleration.md

@@ -65,37 +65,24 @@ Or map in all the `/dev/video*` devices.
 
 ## Intel-based CPUs
 
-:::info
-
-**Recommended hwaccel Preset**
-
-| CPU Generation | Intel Driver | Recommended Preset | Notes                               |
-| -------------- | ------------ | ------------------ | ----------------------------------- |
-| gen1 - gen7    | i965         | preset-vaapi       | qsv is not supported                |
-| gen8 - gen12   | iHD          | preset-vaapi       | preset-intel-qsv-* can also be used |
-| gen13+         | iHD / Xe     | preset-intel-qsv-* |                                     |
-| Intel Arc GPU  | iHD / Xe     | preset-intel-qsv-* |                                     |
-
-:::
-
-:::note
-
-The default driver is `iHD`. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `frigate.yaml` for HA OS users](advanced.md#environment_vars).
-
-See [The Intel Docs](https://www.intel.com/content/www/us/en/support/articles/000005505/processors.html) to figure out what generation your CPU is.
-
-:::
-
 ### Via VAAPI
 
-VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.
+VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams. VAAPI is recommended for all generations of Intel-based CPUs.
 
 ```yaml
 ffmpeg:
   hwaccel_args: preset-vaapi
 ```
 
-### Via Quicksync
+:::note
+
+With some of the processors, like the J4125, the default driver `iHD` doesn't seem to work correctly for hardware acceleration. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `frigate.yaml` for HA OS users](advanced.md#environment_vars).
+
+:::
+
+### Via Quicksync (>=10th Generation only)
+
+If VAAPI does not work for you, you can try QSV if your processor supports it. QSV must be set specifically based on the video encoding of the stream.
 
 #### H.264 streams
 
@@ -231,11 +218,28 @@ docker run -d \
 
 ### Setup Decoder
 
-Using `preset-nvidia` ffmpeg will automatically select the necessary profile for the incoming video, and will log an error if the profile is not supported by your GPU.
+The decoder you need to pass in the `hwaccel_args` will depend on the input video.
+
+A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the container to get the ones your card supports)
+
+```
+ V..... h263_cuvid           Nvidia CUVID H263 decoder (codec h263)
+ V..... h264_cuvid           Nvidia CUVID H264 decoder (codec h264)
+ V..... hevc_cuvid           Nvidia CUVID HEVC decoder (codec hevc)
+ V..... mjpeg_cuvid          Nvidia CUVID MJPEG decoder (codec mjpeg)
+ V..... mpeg1_cuvid          Nvidia CUVID MPEG1VIDEO decoder (codec mpeg1video)
+ V..... mpeg2_cuvid          Nvidia CUVID MPEG2VIDEO decoder (codec mpeg2video)
+ V..... mpeg4_cuvid          Nvidia CUVID MPEG4 decoder (codec mpeg4)
+ V..... vc1_cuvid            Nvidia CUVID VC1 decoder (codec vc1)
+ V..... vp8_cuvid            Nvidia CUVID VP8 decoder (codec vp8)
+ V..... vp9_cuvid            Nvidia CUVID VP9 decoder (codec vp9)
+```
+
+For example, for H264 video, you'll select `preset-nvidia-h264`.
 
 ```yaml
 ffmpeg:
-  hwaccel_args: preset-nvidia
+  hwaccel_args: preset-nvidia-h264
 ```
 
 If everything is working correctly, you should see a significant improvement in performance.

docs/docs/configuration/index.md

@@ -56,11 +56,6 @@ go2rtc:
     password: "{FRIGATE_GO2RTC_RTSP_PASSWORD}"
 ```
 
-```yaml
-genai:
-  api_key: "{FRIGATE_GENAI_API_KEY}"
-```
-
 ## Common configuration examples
 
 Here are some common starter configuration examples. Refer to the [reference config](./reference.md) for detailed information about all the config values.
@@ -72,7 +67,7 @@ Here are some common starter configuration examples. Refer to the [reference con
 - Hardware acceleration for decoding video
 - USB Coral detector
 - Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
-- Continue to keep all video if it qualified as an alert or detection for 30 days
+- Continue to keep all video if it was during any event for 30 days
 - Save snapshots for 30 days
 - Motion mask for the camera timestamp
 
@@ -95,12 +90,10 @@ record:
   retain:
     days: 7
     mode: motion
-  alerts:
+  events:
     retain:
-      days: 30
-  detections:
-    retain:
-      days: 30
+      default: 30
+      mode: motion
 
 snapshots:
   enabled: True
@@ -130,7 +123,7 @@ cameras:
 - VAAPI hardware acceleration for decoding video
 - USB Coral detector
 - Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
-- Continue to keep all video if it qualified as an alert or detection for 30 days
+- Continue to keep all video if it was during any event for 30 days
 - Save snapshots for 30 days
 - Motion mask for the camera timestamp
 
@@ -151,12 +144,10 @@ record:
   retain:
     days: 7
     mode: motion
-  alerts:
+  events:
     retain:
-      days: 30
-  detections:
-    retain:
-      days: 30
+      default: 30
+      mode: motion
 
 snapshots:
   enabled: True
@@ -186,7 +177,7 @@ cameras:
 - VAAPI hardware acceleration for decoding video
 - OpenVino detector
 - Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
-- Continue to keep all video if it qualified as an alert or detection for 30 days
+- Continue to keep all video if it was during any event for 30 days
 - Save snapshots for 30 days
 - Motion mask for the camera timestamp
 
@@ -203,13 +194,14 @@ detectors:
   ov:
     type: openvino
     device: AUTO
+    model:
+      path: /openvino-model/ssdlite_mobilenet_v2.xml
 
 model:
   width: 300
   height: 300
   input_tensor: nhwc
   input_pixel_format: bgr
-  path: /openvino-model/ssdlite_mobilenet_v2.xml
   labelmap_path: /openvino-model/coco_91cl_bkgr.txt
 
 record:
@@ -217,12 +209,10 @@ record:
   retain:
     days: 7
     mode: motion
-  alerts:
+  events:
     retain:
-      days: 30
-  detections:
-    retain:
-      days: 30
+      default: 30
+      mode: motion
 
 snapshots:
   enabled: True

docs/docs/configuration/live.md

@@ -23,13 +23,13 @@ If you are using go2rtc, you should adjust the following settings in your camera
 
 - Video codec: **H.264** - provides the most compatible video codec with all Live view technologies and browsers. Avoid any kind of "smart codec" or "+" codec like _H.264+_ or _H.265+_. as these non-standard codecs remove keyframes (see below).
 - Audio codec: **AAC** - provides the most compatible audio codec with all Live view technologies and browsers that support audio.
-- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes. For many users this may not be an issue, but it should be noted that that a 1x i-frame interval will cause more storage utilization if you are using the stream for the `record` role as well.
+- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes.
 
 The default video and audio codec on your camera may not always be compatible with your browser, which is why setting them to H.264 and AAC is recommended. See the [go2rtc docs](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#codecs-madness) for codec support information.
 
 ### Audio Support
 
-MSE Requires PCMA/PCMU or AAC audio, WebRTC requires PCMA/PCMU or opus audio. If you want to support both MSE and WebRTC then your restream config needs to make sure both are enabled.
+MSE Requires AAC audio, WebRTC requires PCMU/PCMA, or opus audio. If you want to support both MSE and WebRTC then your restream config needs to make sure both are enabled.
 
 ```yaml
 go2rtc:
@@ -138,13 +138,3 @@ services:
 :::
 
 See [go2rtc WebRTC docs](https://github.com/AlexxIT/go2rtc/tree/v1.8.3#module-webrtc) for more information about this.
-
-### Two way talk
-
-For devices that support two way talk, Frigate can be configured to use the feature from the camera's Live view in the Web UI. You should:
-
-- Set up go2rtc with [WebRTC](#webrtc-extra-configuration).
-- 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.
-
-To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-doorbell)

docs/docs/configuration/motion_detection.md

@@ -13,11 +13,11 @@ Once motion is detected, it tries to group up nearby areas of motion together in
 
 The default motion settings should work well for the majority of cameras, however there are cases where tuning motion detection can lead to better and more optimal results. Each camera has its own environment with different variables that affect motion, this means that the same motion settings will not fit all of your cameras.
 
-Before tuning motion it is important to understand the goal. In an optimal configuration, motion from people and cars would be detected, but not grass moving, lighting changes, timestamps, etc. If your motion detection is too sensitive, you will experience higher CPU loads and greater false positives from the increased rate of object detection. If it is not sensitive enough, you will miss objects that you want to track.
+Before tuning motion it is important to understand the goal. In an optimal configuration, motion from people and cars would be detected, but not grass moving, lighting changes, timestamps, etc. If your motion detection is too sensitive, you will experience higher CPU loads and greater false positives from the increased rate of object detection. If it is not sensitive enough, you will miss events.
 
 ## Create Motion Masks
 
-First, mask areas with regular motion not caused by the objects you want to detect. The best way to find candidates for motion masks is by watching the debug stream with motion boxes enabled. Good use cases for motion masks are timestamps or tree limbs and large bushes that regularly move due to wind. When possible, avoid creating motion masks that would block motion detection for objects you want to track **even if they are in locations where you don't want alerts or detections**. Motion masks should not be used to avoid detecting objects in specific areas. More details can be found [in the masks docs.](/configuration/masks.md).
+First, mask areas with regular motion not caused by the objects you want to detect. The best way to find candidates for motion masks is by watching the debug stream with motion boxes enabled. Good use cases for motion masks are timestamps or tree limbs and large bushes that regularly move due to wind. When possible, avoid creating motion masks that would block motion detection for objects you want to track **even if they are in locations where you don't want events**. Motion masks should not be used to avoid detecting objects in specific areas. More details can be found [in the masks docs.](/configuration/masks.md).
 
 ## Prepare For Testing
 
@@ -29,7 +29,7 @@ Now that things are set up, find a time to tune that represents normal circumsta
 
 :::note
 
-Remember that motion detection is just used to determine when object detection should be used. You should aim to have motion detection sensitive enough that you won't miss objects you want to detect with object detection. The goal is to prevent object detection from running constantly for every small pixel change in the image. Windy days are still going to result in lots of motion being detected.
+Remember that motion detection is just used to determine when object detection should be used. You should aim to have motion detection sensitive enough that you won't miss events from objects you want to detect with object detection. The goal is to prevent object detection from running constantly for every small pixel change in the image. Windy days are still going to result in lots of motion being detected.
 
 :::
 
@@ -92,15 +92,9 @@ motion:
   lightning_threshold: 0.8
 ```
 
-:::warning
+:::tip
 
-Some cameras like doorbell cameras may have missed detections when someone walks directly in front of the camera and the lightning_threshold causes motion detection to be re-calibrated. In this case, it may be desirable to increase the `lightning_threshold` to ensure these objects are not missed.
-
-:::
-
-:::note
-
-Lightning threshold does not stop motion based recordings from being saved.
+Some cameras like doorbell cameras may have missed detections when someone walks directly in front of the camera and the lightning_threshold causes motion detection to be re-calibrated. In this case, it may be desirable to increase the `lightning_threshold` to ensure these events are not missed.
 
 :::
 

docs/docs/configuration/notifications.md

@@ -1,42 +0,0 @@
----
-id: notifications
-title: Notifications
----
-
-# Notifications
-
-Frigate offers native notifications using the [WebPush Protocol](https://web.dev/articles/push-notifications-web-push-protocol) which uses the [VAPID spec](https://tools.ietf.org/html/draft-thomson-webpush-vapid) to deliver notifications to web apps using encryption.
-
-## Setting up Notifications
-
-In order to use notifications the following requirements must be met:
-
-- Frigate must be accessed via a secure https connection
-- A supported browser must be used. Currently Chrome, Firefox, and Safari are known to be supported.
-- In order for notifications to be usable externally, Frigate must be accessible externally
-
-### Configuration
-
-To configure notifications, go to the Frigate WebUI -> Settings -> Notifications and enable, then fill out the fields and save.
-
-### Registration
-
-Once notifications are enabled, press the `Register for Notifications` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
-
-## Supported Notifications
-
-Currently notifications are only supported for review alerts. More notifications will be supported in the future.
-
-:::note
-
-Currently, only Chrome supports images in notifications. Safari and Firefox will only show a title and message in the notification.
-
-:::
-
-## Reduce Notification Latency
-
-Different platforms handle notifications differently, some settings changes may be required to get optimal notification delivery.
-
-### Android
-
-Most Android phones have battery optimization settings. To get reliable Notification delivery the browser (Chrome, Firefox) should have battery optimizations disabled. If Frigate is running as a PWA then the Frigate app should have battery optimizations disabled as well.

docs/docs/configuration/object_detectors.md

@@ -3,47 +3,37 @@ id: object_detectors
 title: Object Detectors
 ---
 
-# Supported Hardware
-
-:::info
-
-Frigate supports multiple different detectors that work on different types of hardware:
-
-**Most Hardware**
-- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
-- [Hailo](#hailo-8l): The Hailo8 AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
-
-**AMD**
-- [ROCm](#amdrocm-gpu-detector): ROCm can run on AMD Discrete GPUs to provide efficient object detection.
-- [ONNX](#onnx): ROCm will automatically be detected and used as a detector in the `-rocm` Frigate image when a supported ONNX model is configured.
-
-**Intel**
-- [OpenVino](#openvino-detector): OpenVino can run on Intel Arc GPUs, Intel integrated GPUs, and Intel CPUs to provide efficient object detection.
-- [ONNX](#onnx): OpenVINO will automatically be detected and used as a detector in the default Frigate image when a supported ONNX model is configured.
-
-**Nvidia**
-- [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Nvidia GPUs and Jetson devices, using one of many default models.
-- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` or `-tensorrt-jp(4/5)` Frigate images when a supported ONNX model is configured.
-
-**Rockchip**
-- [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs.
-
-**For Testing**
-- [CPU Detector (not recommended for actual use](#cpu-detector-not-recommended): Use a CPU to run tflite model, this is not recommended and in most cases OpenVINO can be used in CPU mode with better results.
-
-:::
-
-:::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). 
-
-This does not affect using hardware for accelerating other tasks such as [semantic search](./semantic_search.md)
-
-:::
-
 # Officially Supported Detectors
 
-Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `onnx`, `openvino`, `rknn`, `rocm`, and `tensorrt`. 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 the following builtin detector types: `cpu`, `edgetpu`, `openvino`, `tensorrt`, and `rknn`. 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.
+
+## 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"`.
+
+:::tip
+
+If you do not have GPU or Edge TPU hardware, using the [OpenVINO Detector](#openvino-detector) is often more efficient than using the CPU detector.
+
+:::
+
+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`.
+
+```yaml
+detectors:
+  cpu1:
+    type: cpu
+    num_threads: 3
+    model:
+      path: "/custom_model.tflite"
+  cpu2:
+    type: cpu
+    num_threads: 3
+```
+
+When using CPU detectors, you can add one CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.
 
 ## Edge TPU Detector
 
@@ -124,30 +114,6 @@ detectors:
     device: pci
 ```
 
-## Hailo-8l
-
-This detector is available for use with Hailo-8 AI Acceleration Module.
-
-See the [installation docs](../frigate/installation.md#hailo-8l) for information on configuring the hailo8.
-
-### Configuration
-
-```yaml
-detectors:
-  hailo8l:
-    type: hailo8l
-    device: PCIe
-
-model:
-  width: 300
-  height: 300
-  input_tensor: nhwc
-  input_pixel_format: bgr
-  model_type: ssd
-  path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
-```
-
-
 ## OpenVINO Detector
 
 The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel VPU hardware. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
@@ -156,29 +122,11 @@ The OpenVINO device to be used is specified using the `"device"` attribute accor
 
 OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will also run on AMD CPUs despite having no official support for it. A supported Intel platform is required to use the `GPU` device with OpenVINO. For detailed system requirements, see [OpenVINO System Requirements](https://docs.openvino.ai/2024/about-openvino/release-notes-openvino/system-requirements.html)
 
-:::tip
-
-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:
-  ov_0:
-    type: openvino
-    device: GPU
-  ov_1:
-    type: openvino
-    device: GPU
-```
-
-:::
-
 ### Supported Models
 
 #### SSDLite MobileNet v2
 
-An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model.
-
-Use the model configuration shown below when using the OpenVINO detector with the default OpenVINO model:
+An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model. Use the model configuration shown below when using the OpenVINO detector with the default model.
 
 ```yaml
 detectors:
@@ -257,7 +205,7 @@ The model used for TensorRT must be preprocessed on the same hardware platform t
 
 The Frigate image will generate model files during startup if the specified model is not found. Processed models are stored in the `/config/model_cache` folder. Typically the `/config` path is mapped to a directory on the host already and the `model_cache` does not need to be mapped separately unless the user wants to store it in a different location on the host.
 
-By default, no models will be generated, but this can be overridden by specifying the `YOLO_MODELS` environment variable in Docker. One or more models may be listed in a comma-separated format, and each one will be generated. Models will only be generated if the corresponding `{model}.trt` file is not present in the `model_cache` folder, so you can force a model to be regenerated by deleting it from your Frigate data folder.
+By default, the `yolov7-320` model will be generated, but this can be overridden by specifying the `YOLO_MODELS` environment variable in Docker. One or more models may be listed in a comma-separated format, and each one will be generated. To select no model generation, set the variable to an empty string, `YOLO_MODELS=""`. Models will only be generated if the corresponding `{model}.trt` file is not present in the `model_cache` folder, so you can force a model to be regenerated by deleting it from your Frigate data folder.
 
 If you have a Jetson device with DLAs (Xavier or Orin), you can generate a model that will run on the DLA by appending `-dla` to your model name, e.g. specify `YOLO_MODELS=yolov7-320-dla`. The model will run on DLA0 (Frigate does not currently support DLA1). DLA-incompatible layers will fall back to running on the GPU.
 
@@ -288,7 +236,6 @@ yolov4x-mish-640
 yolov7-tiny-288
 yolov7-tiny-416
 yolov7-640
-yolov7-416
 yolov7-320
 yolov7x-640
 yolov7x-320
@@ -299,7 +246,7 @@ An example `docker-compose.yml` fragment that converts the `yolov4-608` and `yol
 ```yml
 frigate:
   environment:
-    - YOLO_MODELS=yolov7-320,yolov7x-640
+    - YOLO_MODELS=yolov4-608,yolov7x-640
     - USE_FP16=false
 ```
 
@@ -317,8 +264,6 @@ The TensorRT detector can be selected by specifying `tensorrt` as the model type
 
 The TensorRT detector uses `.trt` model files that are located in `/config/model_cache/tensorrt` by default. These model path and dimensions used will depend on which model you have generated.
 
-Use the config below to work with generated TRT models:
-
 ```yaml
 detectors:
   tensorrt:
@@ -327,228 +272,12 @@ detectors:
 
 model:
   path: /config/model_cache/tensorrt/yolov7-320.trt
-  labelmap_path: /labelmap/coco-80.txt
   input_tensor: nchw
   input_pixel_format: rgb
   width: 320
   height: 320
 ```
 
-## AMD/ROCm GPU detector
-
-### Setup
-
-The `rocm` detector supports running YOLO-NAS models on AMD GPUs. Use a frigate docker image with `-rocm` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-rocm`.
-
-### Docker settings for GPU access
-
-ROCm needs access to the `/dev/kfd` and `/dev/dri` devices. When docker or frigate is not run under root then also `video` (and possibly `render` and `ssl/_ssl`) groups should be added.
-
-When running docker directly the following flags should be added for device access:
-
-```bash
-$ docker run --device=/dev/kfd --device=/dev/dri  \
-    ...
-```
-
-When using docker compose:
-
-```yaml
-services:
-  frigate:
----
-devices:
-  - /dev/dri
-  - /dev/kfd
-```
-
-For reference on recommended settings see [running ROCm/pytorch in Docker](https://rocm.docs.amd.com/projects/install-on-linux/en/develop/how-to/3rd-party/pytorch-install.html#using-docker-with-pytorch-pre-installed).
-
-### Docker settings for overriding the GPU chipset
-
-Your GPU might work just fine without any special configuration but in many cases they need manual settings. AMD/ROCm software stack comes with a limited set of GPU drivers and for newer or missing models you will have to override the chipset version to an older/generic version to get things working.
-
-Also AMD/ROCm does not "officially" support integrated GPUs. It still does work with most of them just fine but requires special settings. One has to configure the `HSA_OVERRIDE_GFX_VERSION` environment variable. See the [ROCm bug report](https://github.com/ROCm/ROCm/issues/1743) for context and examples.
-
-For the rocm frigate build there is some automatic detection:
-
-- gfx90c -> 9.0.0
-- gfx1031 -> 10.3.0
-- gfx1103 -> 11.0.0
-
-If you have something else you might need to override the `HSA_OVERRIDE_GFX_VERSION` at Docker launch. Suppose the version you want is `9.0.0`, then you should configure it from command line as:
-
-```bash
-$ docker run -e HSA_OVERRIDE_GFX_VERSION=9.0.0 \
-    ...
-```
-
-When using docker compose:
-
-```yaml
-services:
-  frigate:
-...
-environment:
-  HSA_OVERRIDE_GFX_VERSION: "9.0.0"
-```
-
-Figuring out what version you need can be complicated as you can't tell the chipset name and driver from the AMD brand name.
-
-- first make sure that rocm environment is running properly by running `/opt/rocm/bin/rocminfo` in the frigate container -- it should list both the CPU and the GPU with their properties
-- find the chipset version you have (gfxNNN) from the output of the `rocminfo` (see below)
-- use a search engine to query what `HSA_OVERRIDE_GFX_VERSION` you need for the given gfx name ("gfxNNN ROCm HSA_OVERRIDE_GFX_VERSION")
-- override the `HSA_OVERRIDE_GFX_VERSION` with relevant value
-- if things are not working check the frigate docker logs
-
-#### Figuring out if AMD/ROCm is working and found your GPU
-
-```bash
-$ docker exec -it frigate /opt/rocm/bin/rocminfo
-```
-
-#### Figuring out your AMD GPU chipset version:
-
-We unset the `HSA_OVERRIDE_GFX_VERSION` to prevent an existing override from messing up the result:
-
-```bash
-$ docker exec -it frigate /bin/bash -c '(unset HSA_OVERRIDE_GFX_VERSION && /opt/rocm/bin/rocminfo |grep gfx)'
-```
-
-### Supported Models
-
-There is no default model provided, the following formats are supported:
-
-#### YOLO-NAS
-
-[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
-
-:::warning
-
-The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
-
-:::
-
-The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
-
-After placing the downloaded onnx model in your config folder, you can use the following configuration:
-
-```yaml
-detectors:
-  rocm:
-    type: rocm
-
-model:
-  model_type: yolonas
-  width: 320 # <--- should match whatever was set in notebook
-  height: 320 # <--- should match whatever was set in notebook
-  input_pixel_format: bgr
-  path: /config/yolo_nas_s.onnx
-  labelmap_path: /labelmap/coco-80.txt
-```
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
-
-## ONNX
-
-ONNX is an open format for building machine learning models, Frigate supports running ONNX models on CPU, OpenVINO, and TensorRT. On startup Frigate will automatically try to use a GPU if one is available.
-
-:::info
-
-If the correct build is used for your GPU then the GPU will be detected and used automatically.
-
-- **AMD**
-
-  - ROCm will automatically be detected and used with the ONNX detector in the `-rocm` Frigate image.
-
-- **Intel**
-
-  - OpenVINO will automatically be detected and used with the ONNX detector in the default Frigate image.
-
-- **Nvidia**
-  - Nvidia GPUs will automatically be detected and used with the ONNX detector in the `-tensorrt` Frigate image.
-  - Jetson devices will automatically be detected and used with the ONNX detector in the `-tensorrt-jp(4/5)` Frigate image.
-
-:::
-
-:::tip
-
-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
-```
-
-:::
-
-### Supported Models
-
-There is no default model provided, the following formats are supported:
-
-#### YOLO-NAS
-
-[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
-
-:::warning
-
-The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
-
-:::
-
-The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
-
-After placing the downloaded onnx model in your config folder, you can use the following configuration:
-
-```yaml
-detectors:
-  onnx:
-    type: onnx
-
-model:
-  model_type: yolonas
-  width: 320 # <--- should match whatever was set in notebook
-  height: 320 # <--- should match whatever was set in notebook
-  input_pixel_format: bgr
-  input_tensor: nchw
-  path: /config/yolo_nas_s.onnx
-  labelmap_path: /labelmap/coco-80.txt
-```
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
-
-## 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"`.
-
-:::danger
-
-The CPU detector is not recommended for general use. If you do not have GPU or Edge TPU hardware, using the [OpenVINO Detector](#openvino-detector) in CPU mode is often more efficient than using the CPU detector.
-
-:::
-
-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`.
-
-```yaml
-detectors:
-  cpu1:
-    type: cpu
-    num_threads: 3
-  cpu2:
-    type: cpu
-    num_threads: 3
-
-model:
-  path: "/custom_model.tflite"
-```
-
-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.

docs/docs/configuration/object_filters.md

@@ -20,13 +20,15 @@ For object filters in your configuration, any single detection below `min_score`
 
 In frame 2, the score is below the `min_score` value, so Frigate ignores it and it becomes a 0.0. The computed score is the median of the score history (padding to at least 3 values), and only when that computed score crosses the `threshold` is the object marked as a true positive. That happens in frame 4 in the example.
 
+show image of snapshot vs event with differing scores
+
 ### Minimum Score
 
-Any detection below `min_score` will be immediately thrown out and never tracked because it is considered a false positive. If `min_score` is too low then false positives may be detected and tracked which can confuse the object tracker and may lead to wasted resources. If `min_score` is too high then lower scoring true positives like objects that are further away or partially occluded may be thrown out which can also confuse the tracker and cause valid tracked objects to be lost or disjointed.
+Any detection below `min_score` will be immediately thrown out and never tracked because it is considered a false positive. If `min_score` is too low then false positives may be detected and tracked which can confuse the object tracker and may lead to wasted resources. If `min_score` is too high then lower scoring true positives like objects that are further away or partially occluded may be thrown out which can also confuse the tracker and cause valid events to be lost or disjointed.
 
 ### Threshold
 
-`threshold` is used to determine that the object is a true positive. Once an object is detected with a score >= `threshold` object is considered a true positive. If `threshold` is too low then some higher scoring false positives may create an tracked object. If `threshold` is too high then true positive tracked objects may be missed due to the object never scoring high enough.
+`threshold` is used to determine that the object is a true positive. Once an object is detected with a score >= `threshold` object is considered a true positive. If `threshold` is too low then some higher scoring false positives may create an event. If `threshold` is too high then true positive events may be missed due to the object never scoring high enough.
 
 ## Object Shape
 
@@ -50,7 +52,7 @@ Conceptually, a ratio of 1 is a square, 0.5 is a "tall skinny" box, and 2 is a "
 
 ### Zones
 
-[Required zones](/configuration/zones.md) can be a great tool to reduce false positives that may be detected in the sky or other areas that are not of interest. The required zones will only create tracked objects for objects that enter the zone.
+[Required zones](/configuration/zones.md) can be a great tool to reduce false positives that may be detected in the sky or other areas that are not of interest. The required zones will only create events for objects that enter the zone.
 
 ### Object Masks
 

docs/docs/configuration/pwa.md

@@ -20,5 +20,5 @@ In order to install Frigate as a PWA, the following requirements must be met:
 Installation varies slightly based on the device that is being used:
 
 - Desktop: Use the install button typically found in right edge of the address bar
-- Android: Use the `Install as App` button in the more options menu for Chrome, and the `Add app to Home screen` button for Firefox
-- iOS: Use the `Add to Homescreen` button in the share menu
+- Android: Use the `Install as App` button in the more options menu
+- iOS: Use the `Add to Homescreen` button in the share menu

docs/docs/configuration/record.md

@@ -3,7 +3,7 @@ id: record
 title: Recording
 ---
 
-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 in the config. Frigate chooses the largest matching retention value between the recording retention and the tracked object retention when determining if a recording should be removed.
+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 in the config. Frigate chooses the largest matching retention value between the recording retention and the event 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.
 
@@ -13,7 +13,7 @@ H265 recordings can be viewed in Chrome 108+, Edge and Safari only. All other br
 
 ### Most conservative: Ensure all video is saved
 
-For users deploying Frigate in environments where it is important to have contiguous video stored even if there was no detectable motion, the following config will store all video for 3 days. After 3 days, only video containing motion and overlapping with alerts or detections will be retained until 30 days have passed.
+For users deploying Frigate in environments where it is important to have contiguous video stored even if there was no detectable motion, the following config will store all video for 3 days. After 3 days, only video containing motion and overlapping with events will be retained until 30 days have passed.
 
 ```yaml
 record:
@@ -21,13 +21,9 @@ record:
   retain:
     days: 3
     mode: all
-  alerts:
+  events:
     retain:
-      days: 30
-      mode: motion
-  detections:
-    retain:
-      days: 30
+      default: 30
       mode: motion
 ```
 
@@ -41,28 +37,25 @@ record:
   retain:
     days: 3
     mode: motion
-  alerts:
+  events:
     retain:
-      days: 30
-      mode: motion
-  detections:
-    retain:
-      days: 30
+      default: 30
       mode: motion
 ```
 
-### Minimum: Alerts only
+### Minimum: Events only
 
-If you only want to retain video that occurs during a tracked object, this config will discard video unless an alert is ongoing.
+If you only want to retain video that occurs during an event, this config will discard video unless an event is ongoing.
 
 ```yaml
 record:
   enabled: True
   retain:
     days: 0
-  alerts:
+    mode: all
+  events:
     retain:
-      days: 30
+      default: 30
       mode: motion
 ```
 
@@ -72,7 +65,7 @@ As of Frigate 0.12 if there is less than an hour left of storage, the oldest 2 h
 
 ## Configuring Recording Retention
 
-Frigate supports both continuous and tracked object based recordings with separate retention modes and retention periods.
+Frigate supports both continuous and event based recordings with separate retention modes and retention periods.
 
 :::tip
 
@@ -93,28 +86,25 @@ record:
 
 Continuous recording supports different retention modes [which are described below](#what-do-the-different-retain-modes-mean)
 
-### Object Recording
+### Event Recording
 
-The number of days to record review items can be specified for review items classified as alerts as well as tracked objects.
+If you only used clips in previous versions with recordings disabled, you can use the following config to get the same behavior. This is also the default behavior when recordings are enabled.
 
 ```yaml
 record:
   enabled: True
-  alerts:
+  events:
     retain:
-      days: 10 # <- number of days to keep alert recordings
-  detections:
-    retain:
-      days: 10 # <- number of days to keep detections recordings
+      default: 10 # <- number of days to keep event recordings
 ```
 
-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.
+This configuration will retain recording segments that overlap with events and have active tracked objects for 10 days. Because multiple events can reference the same recording segments, this avoids storing duplicate footage for overlapping events and reduces overall storage needs.
 
 **WARNING**: Recordings still must be enabled in the config. If a camera has recordings disabled in the config, enabling via the methods listed above will have no effect.
 
 ## What do the different retain modes mean?
 
-Frigate saves from the stream with the `record` role in 10 second segments. These options determine which recording segments are kept for continuous recording (but can also affect tracked objects).
+Frigate saves from the stream with the `record` role in 10 second segments. These options determine which recording segments are kept for continuous recording (but can also affect events).
 
 Let's say you have Frigate configured so that your doorbell camera would retain the last **2** days of continuous recording.
 
@@ -122,7 +112,11 @@ Let's say you have Frigate configured so that your doorbell camera would retain
 - With the `motion` option the only parts of those 48 hours would be segments that Frigate detected motion. This is the middle ground option that won't keep all 48 hours, but will likely keep all segments of interest along with the potential for some extra segments.
 - With the `active_objects` option the only segments that would be kept are those where there was a true positive object that was not considered stationary.
 
-The same options are available with alerts and detections, except it will only save the recordings when it overlaps with a review item of that type.
+The same options are available with events. Let's consider a scenario where you drive up and park in your driveway, go inside, then come back out 4 hours later.
+
+- With the `all` option all segments for the duration of the event would be saved for the event. This event would have 4 hours of footage.
+- With the `motion` option all segments for the duration of the event with motion would be saved. This means any segment where a car drove by in the street, person walked by, lighting changed, etc. would be saved.
+- With the `active_objects` it would only keep segments where the object was active. In this case the only segments that would be saved would be the ones where the car was driving up, you going inside, you coming outside, and the car driving away. Essentially reducing the 4 hours to a minute or two of event footage.
 
 A configuration example of the above retain modes where all `motion` segments are stored for 7 days and `active objects` are stored for 14 days would be as follows:
 
@@ -132,18 +126,33 @@ record:
   retain:
     days: 7
     mode: motion
-  alerts:
+  events:
     retain:
-      days: 14
-      mode: active_objects
-  detections:
-    retain:
-      days: 14
+      default: 14
       mode: active_objects
 ```
 
 The above configuration example can be added globally or on a per camera basis.
 
+### Object Specific Retention
+
+You can also set specific retention length for an object type. The below configuration example builds on from above but also specifies that recordings of dogs only need to be kept for 2 days and recordings of cars should be kept for 7 days.
+
+```yaml
+record:
+  enabled: True
+  retain:
+    days: 7
+    mode: motion
+  events:
+    retain:
+      default: 14
+      mode: active_objects
+      objects:
+        dog: 2
+        car: 7
+```
+
 ## Can I have "continuous" recordings, but only at certain times?
 
 Using Frigate UI, HomeAssistant, or MQTT, cameras can be automated to only record in certain situations or at certain times.
@@ -154,7 +163,7 @@ Footage can be exported from Frigate by right-clicking (desktop) or long pressin
 
 ### Time-lapse export
 
-Time lapse exporting is available only via the [HTTP API](../integrations/api/export-recording-export-camera-name-start-start-time-end-end-time-post.api.mdx).
+Time lapse exporting is available only via the [HTTP API](../integrations/api.md#post-apiexportcamerastartstart-timestampendend-timestamp).
 
 When exporting a time-lapse the default speed-up is 25x with 30 FPS. This means that every 25 seconds of (real-time) recording is condensed into 1 second of time-lapse video (always without audio) with a smoothness of 30 FPS.
 

docs/docs/configuration/reference.md

@@ -52,7 +52,7 @@ detectors:
   # Required: name of the detector
   detector_name:
     # Required: type of the detector
-    # Frigate provides many types, see https://docs.frigate.video/configuration/object_detectors for more details (default: shown below)
+    # Frigate provided types include 'cpu', 'edgetpu', 'openvino' and 'tensorrt' (default: shown below)
     # Additional detector types can also be plugged in.
     # Detectors may require additional configuration.
     # Refer to the Detectors configuration page for more information.
@@ -117,39 +117,27 @@ auth:
   hash_iterations: 600000
 
 # Optional: model modifications
-# NOTE: The default values are for the EdgeTPU detector.
-# Other detectors will require the model config to be set.
 model:
-  # Required: path to the model (default: automatic based on detector)
+  # Optional: path to the model (default: automatic based on detector)
   path: /edgetpu_model.tflite
-  # Required: path to the labelmap (default: shown below)
+  # Optional: path to the labelmap (default: shown below)
   labelmap_path: /labelmap.txt
   # Required: Object detection model input width (default: shown below)
   width: 320
   # Required: Object detection model input height (default: shown below)
   height: 320
-  # Required: Object detection model input colorspace
+  # Optional: Object detection model input colorspace
   # Valid values are rgb, bgr, or yuv. (default: shown below)
   input_pixel_format: rgb
-  # Required: Object detection model input tensor format
+  # Optional: Object detection model input tensor format
   # Valid values are nhwc or nchw (default: shown below)
   input_tensor: nhwc
-  # Required: Object detection model type, currently only used with the OpenVINO detector
+  # Optional: Object detection model type, currently only used with the OpenVINO detector
   # Valid values are ssd, yolox, yolonas (default: shown below)
   model_type: ssd
-  # Required: Label name modifications. These are merged into the standard labelmap.
+  # Optional: 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
@@ -222,10 +210,6 @@ birdseye:
 # Optional: ffmpeg configuration
 # More information about presets at https://docs.frigate.video/configuration/ffmpeg_presets
 ffmpeg:
-  # Optional: ffmpeg binry path (default: shown below)
-  # can also be set to `7.0` or `5.0` to specify one of the included versions
-  # or can be set to any path that holds `bin/ffmpeg` & `bin/ffprobe`
-  path: "default"
   # Optional: global ffmpeg args (default: shown below)
   global_args: -hide_banner -loglevel warning -threads 2
   # Optional: global hwaccel args (default: auto detect)
@@ -287,13 +271,13 @@ detect:
   # especially when using separate streams for detect and record.
   # Use this setting to make the timeline bounding boxes more closely align
   # with the recording. The value can be positive or negative.
-  # TIP: Imagine there is an tracked object clip with a person walking from left to right.
-  #      If the tracked object lifecycle bounding box is consistently to the left of the person
+  # TIP: Imagine there is an event clip with a person walking from left to right.
+  #      If the event timeline bounding box is consistently to the left of the person
   #      then the value should be decreased. Similarly, if a person is walking from
   #      left to right and the bounding box is consistently ahead of the person
   #      then the value should be increased.
   # TIP: This offset is dynamic so you can change the value and it will update existing
-  #      tracked objects, this makes it easy to tune.
+  #      events, this makes it easy to tune.
   # WARNING: Fast moving objects will likely not have the bounding box align.
   annotation_offset: 0
 
@@ -399,14 +383,6 @@ motion:
   # Optional: Delay when updating camera motion through MQTT from ON -> OFF (default: shown below).
   mqtt_off_delay: 30
 
-# Optional: Notification Configuration
-notifications:
-  # Optional: Enable notification service (default: shown below)
-  enabled: False
-  # Optional: Email for push service to reach out to
-  # NOTE: This is required to use notifications
-  email: "admin@example.com"
-
 # Optional: Record configuration
 # NOTE: Can be overridden at the camera level
 record:
@@ -421,9 +397,9 @@ record:
   sync_recordings: False
   # Optional: Retention settings for recording
   retain:
-    # Optional: Number of days to retain recordings regardless of tracked objects (default: shown below)
-    # NOTE: This should be set to 0 and retention should be defined in alerts and detections section below
-    #       if you only want to retain recordings of alerts and detections.
+    # Optional: Number of days to retain recordings regardless of events (default: shown below)
+    # NOTE: This should be set to 0 and retention should be defined in events section below
+    #       if you only want to retain recordings of events.
     days: 0
     # Optional: Mode for retention. Available options are: all, motion, and active_objects
     #   all - save all recording segments regardless of activity
@@ -446,48 +422,34 @@ record:
     # Optional: Quality of recording preview (default: shown below).
     # Options are: very_low, low, medium, high, very_high
     quality: medium
-  # Optional: alert recording settings
-  alerts:
-    # Optional: Number of seconds before the alert to include (default: shown below)
+  # Optional: Event recording settings
+  events:
+    # Optional: Number of seconds before the event to include (default: shown below)
     pre_capture: 5
-    # Optional: Number of seconds after the alert to include (default: shown below)
+    # Optional: Number of seconds after the event to include (default: shown below)
     post_capture: 5
-    # Optional: Retention settings for recordings of alerts
+    # Optional: Objects to save recordings for. (default: all tracked objects)
+    objects:
+      - person
+    # Optional: Retention settings for recordings of events
     retain:
-      # Required: Retention days (default: shown below)
-      days: 14
+      # Required: Default retention days (default: shown below)
+      default: 10
       # Optional: Mode for retention. (default: shown below)
-      #   all - save all recording segments for alerts regardless of activity
-      #   motion - save all recordings segments for alerts with any detected motion
-      #   active_objects - save all recording segments for alerts with active/moving objects
-      #
-      # NOTE: If the retain mode for the camera is more restrictive than the mode configured
-      #       here, the segments will already be gone by the time this mode is applied.
-      #       For example, if the camera retain mode is "motion", the segments without motion are
-      #       never stored, so setting the mode to "all" here won't bring them back.
-      mode: motion
-  # Optional: detection recording settings
-  detections:
-    # Optional: Number of seconds before the detection to include (default: shown below)
-    pre_capture: 5
-    # Optional: Number of seconds after the detection to include (default: shown below)
-    post_capture: 5
-    # Optional: Retention settings for recordings of detections
-    retain:
-      # Required: Retention days (default: shown below)
-      days: 14
-      # Optional: Mode for retention. (default: shown below)
-      #   all - save all recording segments for detections regardless of activity
-      #   motion - save all recordings segments for detections with any detected motion
-      #   active_objects - save all recording segments for detections with active/moving objects
+      #   all - save all recording segments for events regardless of activity
+      #   motion - save all recordings segments for events with any detected motion
+      #   active_objects - save all recording segments for event with active/moving objects
       #
       # NOTE: If the retain mode for the camera is more restrictive than the mode configured
       #       here, the segments will already be gone by the time this mode is applied.
       #       For example, if the camera retain mode is "motion", the segments without motion are
       #       never stored, so setting the mode to "all" here won't bring them back.
       mode: motion
+      # Optional: Per object retention days
+      objects:
+        person: 15
 
-# Optional: Configuration for the jpg snapshots written to the clips directory for each tracked object
+# Optional: Configuration for the jpg snapshots written to the clips directory for each event
 # NOTE: Can be overridden at the camera level
 snapshots:
   # Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
@@ -514,42 +476,8 @@ snapshots:
   # Optional: quality of the encoded jpeg, 0-100 (default: shown below)
   quality: 70
 
-# Optional: Configuration for semantic search capability
-semantic_search:
-  # Optional: Enable semantic search (default: shown below)
-  enabled: False
-  # Optional: Re-index embeddings database from historical tracked objects (default: shown below)
-  reindex: False
-  # Optional: Set the model size used for embeddings. (default: shown below)
-  # NOTE: small model runs on CPU and large model runs on GPU
-  model_size: "small"
-
-# Optional: Configuration for AI generated tracked object descriptions
-# NOTE: Semantic Search must be enabled for this to do anything.
-# WARNING: Depending on the provider, this will send thumbnails over the internet
-# to Google or OpenAI's LLMs to generate descriptions. It can be overridden at
-# the camera level (enabled: False) to enhance privacy for indoor cameras.
-genai:
-  # Optional: Enable AI description generation (default: shown below)
-  enabled: False
-  # Required if enabled: Provider must be one of ollama, gemini, or openai
-  provider: ollama
-  # Required if provider is ollama. May also be used for an OpenAI API compatible backend with the openai provider.
-  base_url: http://localhost::11434
-  # Required if gemini or openai
-  api_key: "{FRIGATE_GENAI_API_KEY}"
-  # Optional: The default prompt for generating descriptions. Can use replacement
-  # variables like "label", "sub_label", "camera" to make more dynamic. (default: shown below)
-  prompt: "Describe the {label} in the sequence of images with as much detail as possible. Do not describe the background."
-  # Optional: Object specific prompts to customize description results
-  # Format: {label}: {prompt}
-  object_prompts:
-    person: "My special person prompt."
-
 # Optional: Restream configuration
 # Uses https://github.com/AlexxIT/go2rtc (v1.9.2)
-# NOTE: The default go2rtc API port (1984) must be used,
-#       changing this port for the integrated go2rtc instance is not supported. 
 go2rtc:
 
 # Optional: Live stream configuration for WebUI.
@@ -690,7 +618,6 @@ cameras:
     # to enable PTZ controls.
     onvif:
       # Required: host of the camera being connected to.
-      # NOTE: HTTP is assumed by default; HTTPS is supported if you specify the scheme, ex: "https://0.0.0.0".
       host: 0.0.0.0
       # Optional: ONVIF port for device (default: shown below).
       port: 8000
@@ -699,8 +626,6 @@ cameras:
       user: admin
       # Optional: password for login.
       password: admin
-      # Optional: Skip TLS verification from the ONVIF server (default: shown below)
-      tls_insecure: False
       # Optional: Ignores time synchronization mismatches between the camera and the server during authentication.
       # Using NTP on both ends is recommended and this should only be set to True in a "safe" environment due to the security risk it represents.
       ignore_time_mismatch: False
@@ -745,28 +670,6 @@ cameras:
       # By default the cameras are sorted alphabetically.
       order: 0
 
-    # Optional: Configuration for AI generated tracked object descriptions
-    genai:
-      # Optional: Enable AI description generation (default: shown below)
-      enabled: False
-      # Optional: Use the object snapshot instead of thumbnails for description generation (default: shown below)
-      use_snapshot: False
-      # Optional: The default prompt for generating descriptions. Can use replacement
-      # variables like "label", "sub_label", "camera" to make more dynamic. (default: shown below)
-      prompt: "Describe the {label} in the sequence of images with as much detail as possible. Do not describe the background."
-      # Optional: Object specific prompts to customize description results
-      # Format: {label}: {prompt}
-      object_prompts:
-        person: "My special person prompt."
-      # Optional: objects to generate descriptions for (default: all objects that are tracked)
-      objects:
-        - person
-        - cat
-      # Optional: Restrict generation to objects that entered any of the listed zones (default: none, all zones qualify)
-      required_zones: []
-      # Optional: Save thumbnails sent to generative AI for review/debugging purposes (default: shown below)
-      debug_save_thumbnails: False
-
 # Optional
 ui:
   # Optional: Set a timezone to use in the UI (default: use browser local time)

docs/docs/configuration/restream.md

@@ -7,7 +7,7 @@ title: Restream
 
 Frigate can restream your video feed as an RTSP feed for other applications such as Home Assistant to utilize it at `rtsp://<frigate_host>:8554/<camera_name>`. Port 8554 must be open. [This allows you to use a video feed for detection in Frigate and Home Assistant live view at the same time without having to make two separate connections to the camera](#reduce-connections-to-camera). The video feed is copied from the original video feed directly to avoid re-encoding. This feed does not include any annotation by Frigate.
 
-Frigate uses [go2rtc](https://github.com/AlexxIT/go2rtc/tree/v1.9.2) to provide its restream and MSE/WebRTC capabilities. The go2rtc config is hosted at the `go2rtc` in the config, see [go2rtc docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#configuration) for more advanced configurations and features.
+Frigate uses [go2rtc](https://github.com/AlexxIT/go2rtc/tree/v1.9.4) to provide its restream and MSE/WebRTC capabilities. The go2rtc config is hosted at the `go2rtc` in the config, see [go2rtc docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#configuration) for more advanced configurations and features.
 
 :::note
 
@@ -21,7 +21,7 @@ Birdseye RTSP restream can be accessed at `rtsp://<frigate_host>:8554/birdseye`.
 
 ```yaml
 birdseye:
-  restream: True
+  restream: true
 ```
 
 ### Securing Restream With Authentication
@@ -132,31 +132,9 @@ cameras:
             - detect
 ```
 
-## Handling Complex Passwords
-
-go2rtc expects URL-encoded passwords in the config, [urlencoder.org](https://urlencoder.org) can be used for this purpose. 
-
-For example:
-
-```yaml
-go2rtc:
-  streams:
-    my_camera: rtsp://username:$@foo%@192.168.1.100
-```
-
-becomes
-
-```yaml
-go2rtc:
-  streams:
-    my_camera: rtsp://username:$%40foo%25@192.168.1.100
-```
-
-See [this comment(https://github.com/AlexxIT/go2rtc/issues/1217#issuecomment-2242296489) for more information.
-
 ## Advanced Restream Configurations
 
-The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#source-exec) source in go2rtc can be used for custom ffmpeg commands. An example is below:
+The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#source-exec) source in go2rtc can be used for custom ffmpeg commands. An example is below:
 
 NOTE: The output will need to be passed with two curly braces `{{output}}`
 

docs/docs/configuration/review.md

@@ -7,13 +7,13 @@ The Review page of the Frigate UI is for quickly reviewing historical footage of
 
 Review items are filterable by date, object type, and camera.
 
-### Review items vs. tracked objects (formerly "events")
+### Review items vs. events
 
 In Frigate 0.13 and earlier versions, the UI presented "events". An event was synonymous with a tracked or detected object. In Frigate 0.14 and later, a review item is a time period where any number of tracked objects were active.
 
 For example, consider a situation where two people walked past your house. One was walking a dog. At the same time, a car drove by on the street behind them.
 
-In this scenario, Frigate 0.13 and earlier would show 4 "events" in the UI - one for each person, another for the dog, and yet another for the car. You would have had 4 separate videos to watch even though they would have all overlapped.
+In this scenario, Frigate 0.13 and earlier would show 4 events in the UI - one for each person, another for the dog, and yet another for the car. You would have had 4 separate videos to watch even though they would have all overlapped.
 
 In 0.14 and later, all of that is bundled into a single review item which starts and ends to capture all of that activity. Reviews for a single camera cannot overlap. Once you have watched that time period on that camera, it is marked as reviewed.
 
@@ -21,21 +21,6 @@ In 0.14 and later, all of that is bundled into a single review item which starts
 
 Not every segment of video captured by Frigate may be of the same level of interest to you. Video of people who enter your property may be a different priority than those walking by on the sidewalk. For this reason, Frigate 0.14 categorizes review items as _alerts_ and _detections_. By default, all person and car objects are considered alerts. You can refine categorization of your review items by configuring required zones for them.
 
-:::note
-
-Alerts and detections categorize the tracked objects in review items, but Frigate must first detect those objects with your configured object detector (Coral, OpenVINO, etc). By default, the object tracker only detects `person`. Setting `labels` for `alerts` and `detections` does not automatically enable detection of new objects. To detect more than `person`, you should add the following to your config:
-
-```yaml
-objects:
-  track:
-    - person
-    - car
-    - ...
-```
-
-See the [objects documentation](objects.md) for the list of objects that Frigate's default model tracks.
-:::
-
 ## Restricting alerts to specific labels
 
 By default a review item will only be marked as an alert if a person or car is detected. This can be configured to include any object or audio label using the following config:

docs/docs/configuration/semantic_search.md

@@ -1,92 +0,0 @@
----
-id: semantic_search
-title: Using Semantic Search
----
-
-Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. This feature works by creating _embeddings_ — numerical vector representations — for both the images and text descriptions of your tracked objects. By comparing these embeddings, Frigate assesses their similarities to deliver relevant search results.
-
-Frigate uses [Jina AI's CLIP model](https://huggingface.co/jinaai/jina-clip-v1) to create and save embeddings to Frigate's database. All of this runs locally.
-
-Semantic Search is accessed via the _Explore_ view in the Frigate UI.
-
-## Minimum System Requirements
-
-Semantic Search works by running a large AI model locally on your system. Small or underpowered systems like a Raspberry Pi will not run Semantic Search reliably or at all.
-
-A minimum of 8GB of RAM is required to use Semantic Search. A GPU is not strictly required but will provide a significant performance increase over CPU-only systems.
-
-For best performance, 16GB or more of RAM and a dedicated GPU are recommended.
-
-## Configuration
-
-Semantic Search is disabled by default, and must be enabled in your config file or in the UI's Settings page before it can be used. Semantic Search is a global configuration setting.
-
-```yaml
-semantic_search:
-  enabled: True
-  reindex: False
-```
-
-:::tip
-
-The embeddings database can be re-indexed from the existing tracked objects in your database by adding `reindex: True` to your `semantic_search` configuration or by toggling the switch on the Search Settings page in the UI and restarting Frigate. Depending on the number of tracked objects you have, it can take a long while to complete and may max out your CPU while indexing. Make sure to turn the UI's switch off or set the config back to `False` before restarting Frigate again.
-
-If you are enabling Semantic Search for the first time, be advised that Frigate does not automatically index older tracked objects. You will need to enable the `reindex` feature in order to do that.
-
-:::
-
-### Jina AI CLIP
-
-The vision model is able to embed both images and text into the same vector space, which allows `image -> image` and `text -> image` similarity searches. Frigate uses this model on tracked objects to encode the thumbnail image and store it in the database. When searching for tracked objects via text in the search box, Frigate will perform a `text -> image` similarity search against this embedding. When clicking "Find Similar" in the tracked object detail pane, Frigate will perform an `image -> image` similarity search to retrieve the closest matching thumbnails.
-
-The text model is used to embed tracked object descriptions and perform searches against them. Descriptions can be created, viewed, and modified on the Explore page when clicking on thumbnail of a tracked object. See [the Generative AI docs](/configuration/genai.md) for more information on how to automatically generate tracked object descriptions.
-
-Differently weighted versions of the Jina model are available and can be selected by setting the `model_size` config option as `small` or `large`:
-
-```yaml
-semantic_search:
-  enabled: True
-  model_size: small
-```
-
-- Configuring the `large` model employs the full Jina model and will automatically run on the GPU if applicable.
-- Configuring the `small` model employs a quantized version of the Jina model that uses less RAM and runs on CPU with a very negligible difference in embedding quality.
-
-### GPU Acceleration
-
-The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU hardware, when available. This depends on the Docker build that is used.
-
-```yaml
-semantic_search:
-  enabled: True
-  model_size: large
-```
-
-:::info
-
-If the correct build is used for your GPU and the `large` model is configured, then the GPU will be detected and used automatically.
-
-**NOTE:** Object detection and Semantic Search are independent features. If you want to use your GPU with Semantic Search, you must choose the appropriate Frigate Docker image for your GPU.
-
-- **AMD**
-
-  - ROCm will automatically be detected and used for Semantic Search in the `-rocm` Frigate image.
-
-- **Intel**
-
-  - OpenVINO will automatically be detected and used for Semantic Search in the default Frigate image.
-
-- **Nvidia**
-  - Nvidia GPUs will automatically be detected and used for Semantic Search in the `-tensorrt` Frigate image.
-  - Jetson devices will automatically be detected and used for Semantic Search in the `-tensorrt-jp(4/5)` Frigate image.
-
-:::
-
-## Usage and Best Practices
-
-1. Semantic Search is used in conjunction with the other filters available on the Explore page. Use a combination of traditional filtering and Semantic Search for the best results.
-2. Use the thumbnail search type when searching for particular objects in the scene. Use the description search type when attempting to discern the intent of your object.
-3. Because of how the AI models Frigate uses have been trained, the comparison between text and image embedding distances generally means that with multi-modal (`thumbnail` and `description`) searches, results matching `description` will appear first, even if a `thumbnail` embedding may be a better match. Play with the "Search Type" setting to help find what you are looking for. Note that if you are generating descriptions for specific objects or zones only, this may cause search results to prioritize the objects with descriptions even if the the ones without them are more relevant.
-4. Make your search language and tone closely match exactly what you're looking for. If you are using thumbnail search, **phrase your query as an image caption**. Searching for "red car" may not work as well as "red sedan driving down a residential street on a sunny day".
-5. Semantic search on thumbnails tends to return better results when matching large subjects that take up most of the frame. Small things like "cat" tend to not work well.
-6. Experiment! Find a tracked object you want to test and start typing keywords and phrases to see what works for you.

docs/docs/configuration/snapshots.md

@@ -3,7 +3,7 @@ id: snapshots
 title: Snapshots
 ---
 
-Frigate can save a snapshot image to `/media/frigate/clips` for each object that is detected named as `<camera>-<id>.jpg`. They are also accessible [via the api](../integrations/api/event-snapshot-events-event-id-snapshot-jpg-get.api.mdx)
+Frigate can save a snapshot image to `/media/frigate/clips` for each object that is detected named as `<camera>-<id>.jpg`. They are also accessible [via the api](../integrations/api.md#get-apieventsidsnapshotjpg)
 
 For users with Frigate+ enabled, snapshots are accessible in the UI in the Frigate+ pane to allow for quick submission to the Frigate+ service.
 

docs/docs/configuration/tls.md

@@ -36,8 +36,8 @@ Note that certbot uses symlinks, and those can't be followed by the container un
 frigate:
   ...
   volumes:
-    - /etc/letsencrypt/live/your.fqdn.net:/etc/letsencrypt/live/frigate:ro
-    - /etc/letsencrypt/archive/your.fqdn.net:/etc/letsencrypt/archive/your.fqdn.net:ro
+    - /etc/letsencrypt/live/frigate:/etc/letsencrypt/live/frigate:ro
+    - /etc/letsencrypt/archive/frigate:/etc/letsencrypt/archive/frigate:ro
   ...
 
 ```

docs/docs/configuration/zones.md

@@ -64,7 +64,7 @@ cameras:
 
 ### Restricting zones to specific objects
 
-Sometimes you want to limit a zone to specific object types to have more granular control of when alerts, detections, and snapshots are saved. The following example will limit one zone to person objects and the other to cars.
+Sometimes you want to limit a zone to specific object types to have more granular control of when events/snapshots are saved. The following example will limit one zone to person objects and the other to cars.
 
 ```yaml
 cameras:
@@ -80,7 +80,7 @@ cameras:
           - car
 ```
 
-Only car objects can trigger the `front_yard_street` zone and only person can trigger the `entire_yard`. Objects will be tracked for any `person` that enter anywhere in the yard, and for cars only if they enter the street.
+Only car objects can trigger the `front_yard_street` zone and only person can trigger the `entire_yard`. You will get events for person objects that enter anywhere in the yard, and events for cars only if they enter the street.
 
 ### Zone Loitering
 

docs/docs/development/contributing.md

@@ -193,7 +193,7 @@ npm run test
 #### 1. Installation
 
 ```console
-cd docs && npm install
+npm install
 ```
 
 #### 2. Local Development

docs/docs/frigate/camera_setup.md

@@ -3,7 +3,7 @@ id: camera_setup
 title: Camera setup
 ---
 
-Cameras configured to output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. H.265 has better compression, but less compatibility. Firefox 134+/136+/137+ (Windows/Mac/Linux & Android), Chrome 108+, Safari and Edge are the only browsers able to play H.265 and only support a limited number of H.265 profiles. Ideally, cameras should be configured directly for the desired resolutions and frame rates you want to use in Frigate. Reducing frame rates within Frigate will waste CPU resources decoding extra frames that are discarded. There are three different goals that you want to tune your stream configurations around.
+Cameras configured to output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. H.265 has better compression, but less compatibility. Chrome 108+, Safari and Edge are the only browsers able to play H.265 and only support a limited number of H.265 profiles. Ideally, cameras should be configured directly for the desired resolutions and frame rates you want to use in Frigate. Reducing frame rates within Frigate will waste CPU resources decoding extra frames that are discarded. There are three different goals that you want to tune your stream configurations around.
 
 - **Detection**: This is the only stream that Frigate will decode for processing. Also, this is the stream where snapshots will be generated from. The resolution for detection should be tuned for the size of the objects you want to detect. See [Choosing a detect resolution](#choosing-a-detect-resolution) for more details. The recommended frame rate is 5fps, but may need to be higher (10fps is the recommended maximum for most users) for very fast moving objects. Higher resolutions and frame rates will drive higher CPU usage on your server.
 
@@ -28,7 +28,7 @@ For the Dahua/Loryta 5442 camera, I use the following settings:
 - Encode Mode: H.264
 - Resolution: 2688\*1520
 - Frame Rate(FPS): 15
-- I Frame Interval: 30 (15 can also be used to prioritize streaming performance - see the [camera settings recommendations](../configuration/live) for more info)
+- I Frame Interval: 30
 
 **Sub Stream (Detection)**
 

docs/docs/frigate/glossary.md

@@ -16,6 +16,10 @@ A box returned from the object detection model that outlines an object in the fr
 - A gray thin line indicates that object is detected as being stationary
 - A thick line indicates that object is the subject of autotracking (when enabled).
 
+## Event
+
+The time period starting when a tracked object entered the frame and ending when it left the frame, including any time that the object remained still. Events are saved when it is considered a [true positive](#threshold) and meets the requirements for a snapshot or recording to be saved.
+
 ## False Positive
 
 An incorrect detection of an object type. For example a dog being detected as a person, a chair being detected as a dog, etc. A person being detected in an area you want to ignore is not a false positive.
@@ -60,10 +64,6 @@ The threshold is the median score that an object must reach in order to be consi
 
 The top score for an object is the highest median score for an object.
 
-## Tracked Object ("event" in previous versions)
-
-The time period starting when a tracked object entered the frame and ending when it left the frame, including any time that the object remained still. Tracked objects are saved when it is considered a [true positive](#threshold) and meets the requirements for a snapshot or recording to be saved.
-
 ## Zone
 
-Zones are areas of interest, zones can be used for notifications and for limiting the areas where Frigate will create a [review item](#review-item). [See the zone docs for more info](/configuration/zones)
+Zones are areas of interest, zones can be used for notifications and for limiting the areas where Frigate will create an [event](#event). [See the zone docs for more info](/configuration/zones)

docs/docs/frigate/hardware.md

@@ -9,36 +9,23 @@ Cameras that output H.264 video and AAC audio will offer the most compatibility
 
 I recommend Dahua, Hikvision, and Amcrest in that order. Dahua edges out Hikvision because they are easier to find and order, not because they are better cameras. I personally use Dahua cameras because they are easier to purchase directly. In my experience Dahua and Hikvision both have multiple streams with configurable resolutions and frame rates and rock solid streams. They also both have models with large sensors well known for excellent image quality at night. Not all the models are equal. Larger sensors are better than higher resolutions; especially at night. Amcrest is the fallback recommendation because they are rebranded Dahuas. They are rebranding the lower end models with smaller sensors or less configuration options.
 
-WiFi cameras are not recommended as [their streams are less reliable and cause connection loss and/or lost video data](https://ipcamtalk.com/threads/camera-conflicts.68142/#post-738821), especially when more than a few WiFi cameras will be used at the same time.
+Many users have reported various issues with Reolink cameras, so I do not recommend them. If you are using Reolink, I suggest the [Reolink specific configuration](../configuration/camera_specific.md#reolink-cameras). Wifi cameras are also not recommended. Their streams are less reliable and cause connection loss and/or lost video data.
 
-Many users have reported various issues with 4K-plus Reolink cameras, it is best to stick with 5MP and lower for Reolink cameras. If you are using Reolink, I suggest the [Reolink specific configuration](../configuration/camera_specific.md#reolink-cameras).
-
-Here are some of the cameras I recommend:
+Here are some of the camera's I recommend:
 
 - <a href="https://amzn.to/4fwoNWA" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T549M-ALED-S3</a> (affiliate link)
 - <a href="https://amzn.to/3YXpcMw" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T54IR-AS</a> (affiliate link)
 - <a href="https://amzn.to/3AvBHoY" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-AI-V3</a> (affiliate link)
-- <a href="https://amzn.to/4ltOpaC" target="_blank" rel="nofollow noopener sponsored">HIKVISION DS-2CD2387G2P-LSU/SL ColorVu 8MP Panoramic Turret IP Camera</a> (affiliate link)
 
 I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
 
 ## Server
 
-My current favorite is the Beelink EQ13 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Google Coral, Hailo, or other AI accelerators.
-
-Note that many of these mini PCs come with Windows pre-installed, and you will need to install Linux according to the [getting started guide](../guides/getting_started.md).
-
-I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
-
-:::warning
-
-If the EQ13 is out of stock, the link below may take you to a suggested alternative on Amazon. The Beelink EQ14 has some known compatibility issues, so you should avoid that model for now.
-
-:::
+My current favorite is the Beelink EQ13 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Google Coral. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
 
 | Name                                                                                                          | Coral Inference Speed | Coral Compatibility | Notes                                                                                     |
 | ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | ----------------------------------------------------------------------------------------- |
-| Beelink EQ13 (<a href="https://amzn.to/4jn2qVr" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms                | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
+| Beelink EQ13 (<a href="https://amzn.to/4ejU0ew" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms                | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
 
 ## Detectors
 
@@ -64,26 +51,24 @@ The OpenVINO detector type is able to run on:
 
 More information is available [in the detector docs](/configuration/object_detectors#openvino-detector)
 
-Inference speeds vary greatly depending on the CPU or GPU used, some known examples of GPU inference times are below:
+Inference speeds vary greatly depending on the CPU, GPU, or VPU used, some known examples are below:
 
-| Name                  | MobileNetV2 Inference Time  | YOLO-NAS Inference Time     | Notes                                   |
-| --------------------- | --------------------------- | --------------------------- | --------------------------------------- |
-| Intel Arc A750        | ~ 4 ms                      | 320: ~ 8 ms                 |                                         |
-| Intel Arc A380        | ~ 6 ms                      | 320: ~ 10 ms                |                                         |
-| Intel Ultra 5 125H    |                             | 320: ~ 10 ms 640: ~ 22 ms   |                                         |
-| Intel i5 12600K       | ~ 15 ms                     | 320: ~ 20 ms 640: ~ 46 ms   |                                         |
-| Intel i3 12000        |                             | 320: ~ 19 ms 640: ~ 54 ms   |                                         |
-| Intel i5 1135G7       | 10 - 15 ms                  |                             |                                         |
-| Intel i5 7500         | ~ 15 ms                     |                             |                                         |
-| Intel i5 7200u        | 15 - 25 ms                  |                             |                                         |
-| Intel i5 6500         | ~ 15 ms                     |                             |                                         |
-| Intel i5 4590         | ~ 20 ms                     |                             |                                         |
-| Intel i3 8100         | ~ 15 ms                     |                             |                                         |
-| Intel i3 6100T        | 15 - 35 ms                  |                             | Can only run one detector instance      |
-| Intel Celeron N4020   | 50 - 200 ms                 |                             | Inference speed depends on other loads  |
-| Intel Celeron N3205U  | ~ 120 ms                    |                             | Can only run one detector instance      |
-| Intel Celeron N3060   | 130 - 150 ms                |                             | Can only run one detector instance      |
-| Intel Celeron J4105   | ~ 25 ms                     |                             | Can only run one                        |
+| Name                 | Inference Speed | Notes                                                                 |
+| -------------------- | --------------- | --------------------------------------------------------------------- |
+| Intel NCS2 VPU       | 60 - 65 ms      | May vary based on host device                                         |
+| Intel Celeron J4105  | ~ 25 ms         | Inference speeds on CPU were 150 - 200 ms                             |
+| Intel Celeron N3060  | 130 - 150 ms    | Inference speeds on CPU were ~ 550 ms                                 |
+| Intel Celeron N3205U | ~ 120 ms        | Inference speeds on CPU were ~ 380 ms                                 |
+| Intel Celeron N4020  | 50 - 200 ms     | Inference speeds on CPU were ~ 800 ms, greatly depends on other loads |
+| Intel i3 6100T       | 15 - 35 ms      | Inference speeds on CPU were 60 - 120 ms                              |
+| Intel i3 8100        | ~ 15 ms         | Inference speeds on CPU were ~ 65 ms                                  |
+| Intel i5 4590        | ~ 20 ms         | Inference speeds on CPU were ~ 230 ms                                 |
+| Intel i5 6500        | ~ 15 ms         | Inference speeds on CPU were ~ 150 ms                                 |
+| Intel i5 7200u       | 15 - 25 ms      | Inference speeds on CPU were ~ 150 ms                                 |
+| Intel i5 7500        | ~ 15 ms         | Inference speeds on CPU were ~ 260 ms                                 |
+| Intel i5 1135G7      | 10 - 15 ms      |                                                                       |
+| Intel i5 12600K      | ~ 15 ms         | Inference speeds on CPU were ~ 35 ms                                  |
+| Intel Arc A750       | ~ 4 ms          |                                                                       |
 
 ### TensorRT - Nvidia GPU
 
@@ -92,35 +77,25 @@ The TensortRT detector is able to run on x86 hosts that have an Nvidia GPU which
 Inference speeds will vary greatly depending on the GPU and the model used.
 `tiny` variants are faster than the equivalent non-tiny model, some known examples are below:
 
-| Name            | YoloV7 Inference Time  | YOLO-NAS Inference Time     |
-| --------------- | ---------------------- | --------------------------- |
-| Quadro P2000    | ~ 12 ms                |                             |
-| Quadro P400 2GB | 20 - 25 ms             |                             |
-| RTX 3070 Mobile | ~ 5 ms                 |                             |
-| RTX 3050        | 5 - 7 ms               | 320: ~ 10 ms 640: ~ 16 ms   |
-| GTX 1660 SUPER  | ~ 4 ms                 |                             |
-| GTX 1070        | ~ 6 ms                 |                             |
-| GTX 1060 6GB    | ~ 7 ms                 |                             |
+| Name            | Inference Speed |
+| --------------- | --------------- |
+| GTX 1060 6GB    | ~ 7 ms          |
+| GTX 1070        | ~ 6 ms          |
+| GTX 1660 SUPER  | ~ 4 ms          |
+| RTX 3050        | 5 - 7 ms        |
+| RTX 3070 Mobile | ~ 5 ms          |
+| Quadro P400 2GB | 20 - 25 ms      |
+| Quadro P2000    | ~ 12 ms         |
 
-### AMD GPUs
+### Community Supported:
 
-With the [rocm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many discrete AMD GPUs.
-
-### Hailo-8l PCIe
-
-Frigate supports the Hailo-8l M.2 card on any hardware but currently it is only tested on the Raspberry Pi5 PCIe hat from the AI kit.
-
-The inference time for the Hailo-8L chip at time of writing is around 17-21 ms for the SSD MobileNet Version 1 model.
-
-## Community Supported Detectors
-
-### Nvidia Jetson
+#### Nvidia Jetson
 
 Frigate supports all Jetson boards, from the inexpensive Jetson Nano to the powerful Jetson Orin AGX. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) 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).
 
 Inference speed will vary depending on the YOLO model, jetson platform and jetson nvpmodel (GPU/DLA/EMC clock speed). It is typically 20-40 ms for most models. The DLA is more efficient than the GPU, but not faster, so using the DLA will reduce power consumption but will slightly increase inference time.
 
-### Rockchip platform
+#### Rockchip platform
 
 Frigate supports hardware video processing on all Rockchip boards. However, hardware object detection is only supported on these boards:
 
@@ -152,4 +127,4 @@ Basically - When you increase the resolution and/or the frame rate of the stream
 
 YES! The Coral does not help with decoding video streams.
 
-Decompressing video streams takes a significant amount of CPU power. Video compression uses key frames (also known as I-frames) to send a full frame in the video stream. The following frames only include the difference from the key frame, and the CPU has to compile each frame by merging the differences with the key frame. [More detailed explanation](https://support.video.ibm.com/hc/en-us/articles/18106203580316-Keyframes-InterFrame-Video-Compression). Higher resolutions and frame rates mean more processing power is needed to decode the video stream, so try and set them on the camera to avoid unnecessary decoding work.
+Decompressing video streams takes a significant amount of CPU power. Video compression uses key frames (also known as I-frames) to send a full frame in the video stream. The following frames only include the difference from the key frame, and the CPU has to compile each frame by merging the differences with the key frame. [More detailed explanation](https://blog.video.ibm.com/streaming-video-tips/keyframes-interframe-video-compression/). Higher resolutions and frame rates mean more processing power is needed to decode the video stream, so try and set them on the camera to avoid unnecessary decoding work.

docs/docs/frigate/installation.md

@@ -73,23 +73,23 @@ Users of the Snapcraft build of Docker cannot use storage locations outside your
 
 Frigate utilizes shared memory to store frames during processing. The default `shm-size` provided by Docker is **64MB**.
 
-The default shm size of **128MB** is fine for setups with **2 cameras** detecting at **720p**. If Frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size, using [`--shm-size`](https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources) (or [`service.shm_size`](https://docs.docker.com/compose/compose-file/compose-file-v2/#shm_size) in docker-compose).
+The default shm size of **64MB** is fine for setups with **2 cameras** detecting at **720p**. If Frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size, using [`--shm-size`](https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources) (or [`service.shm_size`](https://docs.docker.com/compose/compose-file/compose-file-v2/#shm_size) in docker-compose).
 
-The Frigate container also stores logs in shm, which can take up to **40MB**, so make sure to take this into account in your math as well.
+The Frigate container also stores logs in shm, which can take up to **30MB**, so make sure to take this into account in your math as well.
 
-You can calculate the **minimum** shm size for each camera with the following formula using the resolution specified for detect:
+You can calculate the necessary shm size for each camera with the following formula using the resolution specified for detect:
 
 ```console
 # Replace <width> and <height>
-$ python -c 'print("{:.2f}MB".format((<width> * <height> * 1.5 * 20 + 270480) / 1048576))'
+$ python -c 'print("{:.2f}MB".format((<width> * <height> * 1.5 * 9 + 270480) / 1048576))'
 
-# Example for 1280x720, including logs
-$ python -c 'print("{:.2f}MB".format((1280 * 720 * 1.5 * 20 + 270480) / 1048576)) + 40'
-46.63MB
+# Example for 1280x720
+$ python -c 'print("{:.2f}MB".format((1280 * 720 * 1.5 * 9 + 270480) / 1048576))'
+12.12MB
 
 # Example for eight cameras detecting at 1280x720, including logs
-$ python -c 'print("{:.2f}MB".format(((1280 * 720 * 1.5 * 20 + 270480) / 1048576) * 8 + 40))'
-253MB
+$ python -c 'print("{:.2f}MB".format(((1280 * 720 * 1.5 * 9 + 270480) / 1048576) * 8 + 30))'
+126.99MB
 ```
 
 The shm size cannot be set per container for Home Assistant add-ons. However, this is probably not required since by default Home Assistant Supervisor allocates `/dev/shm` with half the size of your total memory. If your machine has 8GB of memory, chances are that Frigate will have access to up to 4GB without any additional configuration.
@@ -100,38 +100,6 @@ By default, the Raspberry Pi limits the amount of memory available to the GPU. I
 
 Additionally, the USB Coral draws a considerable amount of power. If using any other USB devices such as an SSD, you will experience instability due to the Pi not providing enough power to USB devices. You will need to purchase an external USB hub with it's own power supply. Some have reported success with <a href="https://amzn.to/3a2mH0P" target="_blank" rel="nofollow noopener sponsored">this</a> (affiliate link).
 
-### Hailo-8L
-
-The Hailo-8L is an M.2 card typically connected to a carrier board for PCIe, which then connects to the Raspberry Pi 5 as part of the AI Kit. However, it can also be used on other boards equipped with an M.2 M key edge connector.
-
-#### Installation
-
-For Raspberry Pi 5 users with the AI Kit, installation is straightforward. Simply follow this [guide](https://www.raspberrypi.com/documentation/accessories/ai-kit.html#ai-kit-installation) to install the driver and software.
-
-For other installations, follow these steps for installation:
-
-1. Install the driver from the [Hailo GitHub repository](https://github.com/hailo-ai/hailort-drivers). A convenient script for Linux is available to clone the repository, build the driver, and install it.
-2. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/dev/docker/hailo8l/user_installation.sh).
-3. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
-4. Run the script with `./user_installation.sh`
-
-#### Setup
-
-To set up Frigate, follow the default installation instructions, but use a Docker image with the `-h8l` suffix, for example: `ghcr.io/blakeblackshear/frigate:stable-h8l`
-
-Next, grant Docker permissions to access your hardware by adding the following lines to your `docker-compose.yml` file:
-
-```yaml
-devices:
-  - /dev/hailo0
-```
-
-If you are using `docker run`, add this option to your command `--device /dev/hailo0`
-
-#### Configuration
-
-Finally, configure [hardware object detection](/configuration/object_detectors#hailo-8l) 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:
@@ -193,9 +161,8 @@ services:
     container_name: frigate
     privileged: true # this may not be necessary for all setups
     restart: unless-stopped
-    stop_grace_period: 30s # allow enough time to shut down the various services
     image: ghcr.io/blakeblackshear/frigate:stable
-    shm_size: "512mb" # update for your cameras based on calculation above
+    shm_size: "64mb" # update for your cameras based on calculation above
     devices:
       - /dev/bus/usb:/dev/bus/usb # Passes the USB Coral, needs to be modified for other versions
       - /dev/apex_0:/dev/apex_0 # Passes a PCIe Coral, follow driver instructions here https://coral.ai/docs/m2/get-started/#2a-on-linux
@@ -225,7 +192,6 @@ If you can't use docker compose, you can run the container with something simila
 docker run -d \
   --name frigate \
   --restart=unless-stopped \
-  --stop-timeout 30 \
   --mount type=tmpfs,target=/tmp/cache,tmpfs-size=1000000000 \
   --device /dev/bus/usb:/dev/bus/usb \
   --device /dev/dri/renderD128 \
@@ -252,8 +218,10 @@ The community supported docker image tags for the current stable version are:
 - `stable-tensorrt-jp5` - Frigate build optimized for nvidia Jetson devices running Jetpack 5
 - `stable-tensorrt-jp4` - Frigate build optimized for nvidia Jetson devices running Jetpack 4.6
 - `stable-rk` - Frigate build for SBCs with Rockchip SoC
-- `stable-rocm` - Frigate build for [AMD GPUs](../configuration/object_detectors.md#amdrocm-gpu-detector)
-  - `stable-h8l` - Frigate build for the Hailo-8L M.2 PICe Raspberry Pi 5 hat
+- `stable-rocm` - Frigate build for [AMD GPUs and iGPUs](../configuration/object_detectors.md#amdrocm-gpu-detector), all drivers
+  - `stable-rocm-gfx900` - AMD gfx900 driver only
+  - `stable-rocm-gfx1030` - AMD gfx1030 driver only
+  - `stable-rocm-gfx1100` - AMD gfx1100 driver only
 
 ## Home Assistant Addon
 
@@ -305,15 +273,8 @@ To install make sure you have the [community app plugin here](https://forums.unr
 
 ## Proxmox
 
-[According to Proxmox documentation](https://pve.proxmox.com/pve-docs/pve-admin-guide.html#chapter_pct) it is recommended that you run application containers like Frigate inside a Proxmox QEMU VM. This will give you all the advantages of application containerization, while also providing the benefits that VMs offer, such as strong isolation from the host and the ability to live-migrate, which otherwise isn’t possible with containers.
+It is recommended to run Frigate in LXC, rather than in a VM, for maximum performance. The setup can be complex so be prepared to read the Proxmox and LXC documentation. Suggestions include:
 
-:::warning
-
-If you choose to run Frigate via LXC in Proxmox the setup can be complex so be prepared to read the Proxmox and LXC documentation, Frigate does not officially support running inside of an LXC.
-
-:::
-
- Suggestions include:
 - For Intel-based hardware acceleration, to allow access to the `/dev/dri/renderD128` device with major number 226 and minor number 128, add the following lines to the `/etc/pve/lxc/<id>.conf` LXC configuration:
   - `lxc.cgroup2.devices.allow: c 226:128 rwm`
   - `lxc.mount.entry: /dev/dri/renderD128 dev/dri/renderD128 none bind,optional,create=file`

docs/docs/frigate/updating.md

@@ -1,119 +0,0 @@
----
-id: updating
-title: Updating
----
-
-# Updating Frigate
-
-The current stable version of Frigate is **0.15.0**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.15.0).
-
-Keeping Frigate up to date ensures you benefit from the latest features, performance improvements, and bug fixes. The update process varies slightly depending on your installation method (Docker, Home Assistant Addon, etc.). Below are instructions for the most common setups.
-
-## Before You Begin
-
-- **Stop Frigate**: For most methods, you’ll need to stop the running Frigate instance before backing up and updating.
-- **Backup Your Configuration**: Always back up your `/config` directory (e.g., `config.yml` and `frigate.db`, the SQLite database) before updating. This ensures you can roll back if something goes wrong.
-- **Check Release Notes**: Carefully review the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases) for breaking changes or configuration updates that might affect your setup.
-
-## Updating with Docker
-
-If you’re running Frigate via Docker (recommended method), follow these steps:
-
-1. **Stop the Container**:
-
-   - If using Docker Compose:
-     ```bash
-     docker compose down frigate
-     ```
-   - If using `docker run`:
-     ```bash
-     docker stop frigate
-     ```
-
-2. **Update and Pull the Latest Image**:
-
-   - If using Docker Compose:
-     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.15.0` instead of `0.14.1`). For example:
-       ```yaml
-       services:
-         frigate:
-           image: ghcr.io/blakeblackshear/frigate:0.15.0
-       ```
-     - Then pull the image:
-       ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.15.0
-       ```
-     - **Note for `stable` Tag Users**: If your `docker-compose.yml` uses the `stable` tag (e.g., `ghcr.io/blakeblackshear/frigate:stable`), you don’t need to update the tag manually. The `stable` tag always points to the latest stable release after pulling.
-   - If using `docker run`:
-     - Pull the image with the appropriate tag (e.g., `0.15.0`, `0.15.0-tensorrt`, or `stable`):
-       ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.15.0
-       ```
-
-3. **Start the Container**:
-
-   - If using Docker Compose:
-     ```bash
-     docker compose up -d
-     ```
-   - If using `docker run`, re-run your original command (e.g., from the [Installation](./installation.md#docker) section) with the updated image tag.
-
-4. **Verify the Update**:
-   - Check the container logs to ensure Frigate starts successfully:
-     ```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.
-
-### Notes
-
-- If you’ve customized other settings (e.g., `shm-size`), ensure they’re still appropriate after the update.
-- Docker will automatically use the updated image when you restart the container, as long as you pulled the correct version.
-
-## Updating the Home Assistant Addon
-
-For users running Frigate as a Home Assistant Addon:
-
-1. **Check for Updates**:
-
-   - Navigate to **Settings > Add-ons** in Home Assistant.
-   - Find your installed Frigate addon (e.g., "Frigate NVR" or "Frigate NVR (Full Access)").
-   - If an update is available, you’ll see an "Update" button.
-
-2. **Update the Addon**:
-
-   - Click the "Update" button next to the Frigate addon.
-   - Wait for the process to complete. Home Assistant will handle downloading and installing the new version.
-
-3. **Restart the Addon**:
-
-   - After updating, go to the addon’s page and click "Restart" to apply the changes.
-
-4. **Verify the Update**:
-   - Check the addon logs (under the "Log" tab) to ensure Frigate starts without errors.
-   - Access the Frigate Web UI to confirm the new version is running.
-
-### Notes
-
-- Ensure your `/config/frigate.yml` is compatible with the new version by reviewing the [Release notes](https://github.com/blakeblackshear/frigate/releases).
-- If using custom hardware (e.g., Coral or GPU), verify that configurations still work, as addon updates don’t modify your hardware settings.
-
-## Rolling Back
-
-If an update causes issues:
-
-1. Stop Frigate.
-2. Restore your backed-up config file and database.
-3. Revert to the previous image version:
-   - For Docker: Specify an older tag (e.g., `ghcr.io/blakeblackshear/frigate:0.14.1`) in your `docker run` command.
-   - For Docker Compose: Edit your `docker-compose.yml`, specify the older version tag (e.g., `ghcr.io/blakeblackshear/frigate:0.14.1`), and re-run `docker compose up -d`.
-   - For Home Assistant: Reinstall the previous addon version manually via the repository if needed and restart the addon.
-4. Verify the old version is running again.
-
-## Troubleshooting
-
-- **Container Fails to Start**: Check logs (`docker logs frigate`) for errors.
-- **UI Not Loading**: Ensure ports (e.g., 5000, 8971) are still mapped correctly and the service is running.
-- **Hardware Issues**: Revisit hardware-specific setup (e.g., Coral, GPU) if detection or decoding fails post-update.
-
-Common questions are often answered in the [FAQ](https://github.com/blakeblackshear/frigate/discussions), pinned at the top of the support discussions.

docs/docs/guides/configuring_go2rtc.md

@@ -7,13 +7,13 @@ title: Configuring go2rtc
 
 Use of the bundled go2rtc is optional. You can still configure FFmpeg to connect directly to your cameras. However, adding go2rtc to your configuration is required for the following features:
 
-- WebRTC or MSE for live viewing with audio, higher resolutions and frame rates than the jsmpeg stream which is limited to the detect stream and does not support audio
+- WebRTC or MSE for live viewing with higher resolutions and frame rates than the jsmpeg stream which is limited to the detect stream
 - Live stream support for cameras in Home Assistant Integration
 - RTSP relay for use with other consumers to reduce the number of connections to your camera streams
 
 # Setup a go2rtc stream
 
-First, you will want to configure go2rtc to connect to your camera stream by adding the stream you want to use for live view in your Frigate config file. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#module-streams), not just rtsp.
+First, you will want to configure go2rtc to connect to your camera stream by adding the stream you want to use for live view in your Frigate config file. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#module-streams), not just rtsp.
 
 :::tip
 
@@ -47,8 +47,8 @@ After adding this to the config, restart Frigate and try to watch the live strea
 
 - Check Video Codec:
     - If the camera stream works in go2rtc but not in your browser, the video codec might be unsupported.
-    - If using H265, switch to H264. Refer to [video codec compatibility](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#codecs-madness) in go2rtc documentation.
-    - If unable to switch from H265 to H264, or if the stream format is different (e.g., MJPEG), re-encode the video using [FFmpeg parameters](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#source-ffmpeg). It supports rotating and resizing video feeds and hardware acceleration. Keep in mind that transcoding video from one format to another is a resource intensive task and you may be better off using the built-in jsmpeg view.
+    - If using H265, switch to H264. Refer to [video codec compatibility](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#codecs-madness) in go2rtc documentation.
+    - If unable to switch from H265 to H264, or if the stream format is different (e.g., MJPEG), re-encode the video using [FFmpeg parameters](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#source-ffmpeg). It supports rotating and resizing video feeds and hardware acceleration. Keep in mind that transcoding video from one format to another is a resource intensive task and you may be better off using the built-in jsmpeg view.
         ```yaml
         go2rtc:
           streams:

docs/docs/guides/getting_started.md

@@ -115,7 +115,6 @@ services:
   frigate:
     container_name: frigate
     restart: unless-stopped
-    stop_grace_period: 30s
     image: ghcr.io/blakeblackshear/frigate:stable
     volumes:
       - ./config:/config
@@ -239,7 +238,7 @@ Now that you know where you need to mask, use the "Mask & Zone creator" in the o
 
 :::warning
 
-Note that motion masks should not be used to mark out areas where you do not want objects to be detected or to reduce false positives. They do not alter the image sent to object detection, so you can still get tracked objects, alerts, and detections in areas with motion masks. These only prevent motion in these areas from initiating object detection.
+Note that motion masks should not be used to mark out areas where you do not want objects to be detected or to reduce false positives. They do not alter the image sent to object detection, so you can still get events and detections in areas with motion masks. These only prevent motion in these areas from initiating object detection.
 
 :::
 
@@ -299,11 +298,11 @@ If you don't have separate streams for detect and record, you would just add the
 
 If you only define one stream in your `inputs` and do not assign a `detect` role to it, Frigate will automatically assign it the `detect` role. Frigate will always decode a stream to support motion detection, Birdseye, the API image endpoints, and other features, even if you have disabled object detection with `enabled: False` in your config's `detect` section.
 
-If you only plan to use Frigate for recording, it is still recommended to define a `detect` role for a low resolution stream to minimize resource usage from the required stream decoding.
+If you plan to use Frigate for recording only, it is still recommended to define a `detect` role for a low resolution stream to minimize resource usage from the required stream decoding.
 
 :::
 
-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/reference.md).
+By default, Frigate will retain video of all events for 10 days. The full set of options for recording can be found [here](../configuration/reference.md).
 
 ### Step 7: Complete config
 

docs/docs/guides/ha_notifications.md

@@ -7,11 +7,11 @@ The best way to get started with notifications for Frigate is to use the [Bluepr
 
 It is generally recommended to trigger notifications based on the `frigate/reviews` mqtt topic. This provides the event_id(s) needed to fetch [thumbnails/snapshots/clips](../integrations/home-assistant.md#notification-api) and other useful information to customize when and where you want to receive alerts. The data is published in the form of a change feed, which means you can reference the "previous state" of the object in the `before` section and the "current state" of the object in the `after` section. You can see an example [here](../integrations/mqtt.md#frigateevents).
 
-Here is a simple example of a notification automation of tracked objects which will update the existing notification for each change. This means the image you see in the notification will update as Frigate finds a "better" image.
+Here is a simple example of a notification automation of events which will update the existing notification for each change. This means the image you see in the notification will update as Frigate finds a "better" image.
 
 ```yaml
 automation:
-  - alias: Notify of tracked object
+  - alias: Notify of events
     trigger:
       platform: mqtt
       topic: frigate/events

docs/docs/guides/reverse_proxy.md

@@ -35,7 +35,6 @@ There are many solutions available to implement reverse proxies and the communit
 * [Apache2](#apache2-reverse-proxy)
 * [Nginx](#nginx-reverse-proxy)
 * [Traefik](#traefik-reverse-proxy)
-* [Caddy](#caddy-reverse-proxy)
 
 ## Apache2 Reverse Proxy
 
@@ -118,8 +117,7 @@ server {
   set $port           8971;
 
   listen 80;
-  listen 443 ssl;
-  http2 on;
+  listen 443 ssl http2;
 
   server_name frigate.domain.com;
 }
@@ -179,33 +177,3 @@ The above configuration will create a "service" in Traefik, automatically adding
 It will also add a router, routing requests to "traefik.example.com" to your local container.
 
 Note that with this approach, you don't need to expose any ports for the Frigate instance since all traffic will be routed over the internal Docker network.
-
-## Caddy Reverse Proxy
-
-This example shows Frigate running under a subdomain with logging and a tls cert (in this case a wildcard domain cert obtained independently of caddy) handled via imports
-
-```caddy
-(logging) {
-        log {
-                output file /var/log/caddy/{args[0]}.log {
-                        roll_size 10MiB
-                        roll_keep 5
-                        roll_keep_for 10d
-                }
-                format json
-                level INFO
-        }
-}
-
-
-(tls) {
-        tls /var/lib/caddy/wildcard.YOUR_DOMAIN.TLD.fullchain.pem /var/lib/caddy/wildcard.YOUR_DOMAIN.TLD.privkey.pem
-}
-
-frigate.YOUR_DOMAIN.TLD {
-        reverse_proxy http://localhost:8971 
-        import tls
-        import logging frigate.YOUR_DOMAIN.TLD
-}
-
-```

docs/docs/integrations/api.md

@@ -0,0 +1,562 @@
+---
+id: api
+title: HTTP API
+---
+
+A web server is available on port 5000 with the following endpoints.
+
+## Management & Information
+
+### `GET /api/config`
+
+A json representation of your configuration
+
+### `POST /api/restart`
+
+Restarts Frigate process.
+
+### `GET /api/stats`
+
+Contains some granular debug info that can be used for sensors in Home Assistant.
+
+Sample response:
+
+```json
+{
+  /* Per Camera Stats */
+  "cameras": {
+    "back": {
+      /***************
+       * Frames per second being consumed from your camera. If this is higher
+       * than it is supposed to be, you should set -r FPS in your input_args.
+       * camera_fps = process_fps + skipped_fps
+       ***************/
+      "camera_fps": 5.0,
+      /***************
+       * Number of times detection is run per second. This can be higher than
+       * your camera FPS because Frigate often looks at the same frame multiple times
+       * or in multiple locations
+       ***************/
+      "detection_fps": 1.5,
+      /***************
+       * PID for the ffmpeg process that consumes this camera
+       ***************/
+      "capture_pid": 27,
+      /***************
+       * PID for the process that runs detection for this camera
+       ***************/
+      "pid": 34,
+      /***************
+       * Frames per second being processed by Frigate.
+       ***************/
+      "process_fps": 5.1,
+      /***************
+       * Frames per second skip for processing by Frigate.
+       ***************/
+      "skipped_fps": 0.0
+    }
+  },
+  /***************
+   * Sum of detection_fps across all cameras and detectors.
+   * This should be the sum of all detection_fps values from cameras.
+   ***************/
+  "detection_fps": 5.0,
+  /* Detectors Stats */
+  "detectors": {
+    "coral": {
+      /***************
+       * Timestamp when object detection started. If this value stays non-zero and constant
+       * for a long time, that means the detection process is stuck.
+       ***************/
+      "detection_start": 0.0,
+      /***************
+       * Time spent running object detection in milliseconds.
+       ***************/
+      "inference_speed": 10.48,
+      /***************
+       * PID for the shared process that runs object detection on the Coral.
+       ***************/
+      "pid": 25321
+    }
+  },
+  "service": {
+    /* Uptime in seconds */
+    "uptime": 10,
+    "version": "0.10.1-8883709",
+    "latest_version": "0.10.1",
+    /* Storage data in MB for important locations */
+    "storage": {
+      "/media/frigate/clips": {
+        "total": 1000,
+        "used": 700,
+        "free": 300,
+        "mnt_type": "ext4"
+      },
+      "/media/frigate/recordings": {
+        "total": 1000,
+        "used": 700,
+        "free": 300,
+        "mnt_type": "ext4"
+      },
+      "/tmp/cache": {
+        "total": 256,
+        "used": 100,
+        "free": 156,
+        "mnt_type": "tmpfs"
+      },
+      "/dev/shm": {
+        "total": 256,
+        "used": 100,
+        "free": 156,
+        "mnt_type": "tmpfs"
+      }
+    }
+  },
+  "cpu_usages": {
+    "pid": {
+      "cmdline": "ffmpeg...",
+      "cpu": "5.0",
+      "cpu_average": "3.0",
+      "mem": "0.5"
+    }
+  },
+  "gpu_usages": {
+    "gpu-type": {
+      "gpu": "17%",
+      "mem": "18%"
+    }
+  }
+}
+```
+
+### `GET /api/version`
+
+Version info
+
+### `GET /api/ffprobe`
+
+Get ffprobe output for camera feed paths.
+
+| param   | Type   | Description                        |
+| ------- | ------ | ---------------------------------- |
+| `paths` | string | `,` separated list of camera paths |
+
+### `GET /api/<camera_name>/ptz/info`
+
+Get PTZ info for the camera.
+
+## Camera Media
+
+### `GET /api/<camera_name>`
+
+An mjpeg stream for debugging. Keep in mind the mjpeg endpoint is for debugging only and will put additional load on the system when in use.
+
+Accepts the following query string parameters:
+
+| param       | Type | Description                                                        |
+| ----------- | ---- | ------------------------------------------------------------------ |
+| `fps`       | int  | Frame rate                                                         |
+| `h`         | int  | Height in pixels                                                   |
+| `bbox`      | int  | Show bounding boxes for detected objects (0 or 1)                  |
+| `timestamp` | int  | Print the timestamp in the upper left (0 or 1)                     |
+| `zones`     | int  | Draw the zones on the image (0 or 1)                               |
+| `mask`      | int  | Overlay the mask on the image (0 or 1)                             |
+| `motion`    | int  | Draw blue boxes for areas with detected motion (0 or 1)            |
+| `regions`   | int  | Draw green boxes for areas where object detection was run (0 or 1) |
+
+You can access a higher resolution mjpeg stream by appending `h=height-in-pixels` to the endpoint. For example `/api/back?h=1080`. You can also increase the FPS by appending `fps=frame-rate` to the URL such as `/api/back?fps=10` or both with `?fps=10&h=1000`.
+
+### `GET /api/<camera_name>/latest.jpg[?h=300]`
+
+The most recent frame that Frigate has finished processing. It is a full resolution image by default.
+
+Accepts the following query string parameters:
+
+| param       | Type | Description                                                        |
+| ----------- | ---- | ------------------------------------------------------------------ |
+| `h`         | int  | Height in pixels                                                   |
+| `bbox`      | int  | Show bounding boxes for detected objects (0 or 1)                  |
+| `timestamp` | int  | Print the timestamp in the upper left (0 or 1)                     |
+| `zones`     | int  | Draw the zones on the image (0 or 1)                               |
+| `mask`      | int  | Overlay the mask on the image (0 or 1)                             |
+| `motion`    | int  | Draw blue boxes for areas with detected motion (0 or 1)            |
+| `regions`   | int  | Draw green boxes for areas where object detection was run (0 or 1) |
+| `quality`   | int  | Jpeg encoding quality (0-100). Defaults to 70.                     |
+
+Example parameters:
+
+- `h=300`: resizes the image to 300 pixels tall
+
+### `GET /api/<camera_name>/<label>/thumbnail.jpg`
+
+Returns the thumbnail from the latest event for the given camera and label combo. Using `any` as the label will return the latest thumbnail regardless of type.
+
+### `GET /api/<camera_name>/<label>/clip.mp4`
+
+Returns the clip from the latest event for the given camera and label combo. Using `any` as the label will return the latest clip regardless of type.
+
+### `GET /api/<camera_name>/<label>/snapshot.jpg`
+
+Returns the snapshot image from the latest event for the given camera and label combo. Using `any` as the label will return the latest thumbnail regardless of type.
+
+### `GET /api/<camera_name>/grid.jpg`
+
+Returns the latest camera image with the regions grid overlaid.
+
+| param        | Type  | Description                                                                                |
+| ------------ | ----- | ------------------------------------------------------------------------------------------ |
+| `color`      | str   | The color of the grid (red,green,blue,black,white). Defaults to "green".                   |
+| `font_scale` | float | Font scale. Can be used to increase font size on high resolution cameras. Defaults to 0.5. |
+
+### `GET /clips/<camera>-<id>.jpg`
+
+JPG snapshot for the given camera and event id.
+
+## Events
+
+### `GET /api/events`
+
+Events from the database. Accepts the following query string parameters:
+
+| param                | Type  | Description                                           |
+| -------------------- | ----- | ----------------------------------------------------- |
+| `before`             | int   | Epoch time                                            |
+| `after`              | int   | Epoch time                                            |
+| `cameras`            | str   | , separated list of cameras                           |
+| `labels`             | str   | , separated list of labels                            |
+| `zones`              | str   | , separated list of zones                             |
+| `limit`              | int   | Limit the number of events returned                   |
+| `has_snapshot`       | int   | Filter to events that have snapshots (0 or 1)         |
+| `has_clip`           | int   | Filter to events that have clips (0 or 1)             |
+| `include_thumbnails` | int   | Include thumbnails in the response (0 or 1)           |
+| `in_progress`        | int   | Limit to events in progress (0 or 1)                  |
+| `time_range`         | str   | Time range in format after,before (00:00,24:00)       |
+| `timezone`           | str   | Timezone to use for time range                        |
+| `min_score`          | float | Minimum score of the event                            |
+| `max_score`          | float | Maximum score of the event                            |
+| `is_submitted`       | int   | Filter events that are submitted to Frigate+ (0 or 1) |
+| `min_length`         | float | Minimum length of the event                           |
+| `max_length`         | float | Maximum length of the event                           |
+
+### `GET /api/events/summary`
+
+Returns summary data for events in the database. Used by the Home Assistant integration.
+
+### `GET /api/events/<id>`
+
+Returns data for a single event.
+
+### `DELETE /api/events/<id>`
+
+Permanently deletes the event along with any clips/snapshots.
+
+### `POST /api/events/<id>/retain`
+
+Sets retain to true for the event id.
+
+### `POST /api/events/<id>/plus`
+
+Submits the snapshot of the event to Frigate+ for labeling.
+
+| param                | Type | Description                        |
+| -------------------- | ---- | ---------------------------------- |
+| `include_annotation` | int  | Submit annotation to Frigate+ too. |
+
+### `PUT /api/events/<id>/false_positive`
+
+Submits the snapshot of the event to Frigate+ for labeling and adds the detection as a false positive.
+
+### `DELETE /api/events/<id>/retain`
+
+Sets retain to false for the event id (event may be deleted quickly after removing).
+
+### `POST /api/events/<id>/sub_label`
+
+Set a sub label for an event. For example to update `person` -> `person's name` if they were recognized with facial recognition.
+Sub labels must be 100 characters or shorter.
+
+```json
+{
+  "subLabel": "some_string",
+  "subLabelScore": 0.79
+}
+```
+
+### `GET /api/events/<id>/thumbnail.jpg`
+
+Returns a thumbnail for the event id optimized for notifications. Works while the event is in progress and after completion. Passing `?format=android` will convert the thumbnail to 2:1 aspect ratio.
+
+### `GET /api/events/<id>/clip.mp4`
+
+Returns the clip for the event id. Works after the event has ended.
+
+### `GET /api/events/<id>/snapshot-clean.png`
+
+Returns the clean snapshot image for the event id. Only works if `snapshots` and `clean_copy` are enabled in the config.
+
+| param      | Type | Description        |
+| ---------- | ---- | ------------------ |
+| `download` | bool | Download the image |
+
+### `GET /api/events/<id>/snapshot.jpg`
+
+Returns the snapshot image for the event id. Works while the event is in progress and after completion.
+
+Accepts the following query string parameters, but they are only applied when an event is in progress. After the event is completed, the saved snapshot is returned from disk without modification:
+
+| param       | Type | Description                                       |
+| ----------- | ---- | ------------------------------------------------- |
+| `h`         | int  | Height in pixels                                  |
+| `bbox`      | int  | Show bounding boxes for detected objects (0 or 1) |
+| `timestamp` | int  | Print the timestamp in the upper left (0 or 1)    |
+| `crop`      | int  | Crop the snapshot to the (0 or 1)                 |
+| `quality`   | int  | Jpeg encoding quality (0-100). Defaults to 70.    |
+| `download`  | bool | Download the image                                |
+
+### `POST /api/events/<camera_name>/<label>/create`
+
+Create a manual event with a given `label` (ex: doorbell press) to capture a specific event besides an object being detected.
+
+:::warning
+
+Recording retention config still applies to manual events, if frigate is configured with `mode: motion` then the manual event will only keep recording segments when motion occurred.
+
+:::
+
+**Optional Body:**
+
+```json
+{
+  "sub_label": "some_string", // add sub label to event
+  "duration": 30, // predetermined length of event (default: 30 seconds) or can be to null for indeterminate length event
+  "include_recording": true, // whether the event should save recordings along with the snapshot that is taken
+  "draw": {
+    // optional annotations that will be drawn on the snapshot
+    "boxes": [
+      {
+        "box": [0.5, 0.5, 0.25, 0.25], // box consists of x, y, width, height which are on a scale between 0 - 1
+        "color": [255, 0, 0], // color of the box, default is red
+        "score": 100 // optional score associated with the box
+      }
+    ]
+  }
+}
+```
+
+**Success Response:**
+
+```json
+{
+  "event_id": "1682970645.13116-1ug7ns",
+  "message": "Successfully created event.",
+  "success": true
+}
+```
+
+### `PUT /api/events/<event_id>/end`
+
+End a specific manual event without a predetermined length.
+
+### `GET /api/events/<id>/preview.gif`
+
+Gif covering the first 20 seconds of a specific event.
+
+## Previews
+
+Previews are low res / fps videos that are quickly scrubbable and can be used for notifications or time-lapses.
+
+### `GET /api/preview/<camera>/start/<start-timestamp>/end/<end-timestamp>`
+
+Metadata about previews for this time range.
+
+### `GET /api/preview/<year>-<month>/<day>/<hour>/<camera>/<timezone>`
+
+Metadata about previews for this hour
+
+### `GET /api/preview/<camera>/start/<start-timestamp>/end/<end-timestamp>/frames`
+
+List of frames in the preview cache for the time range. Previews are only kept in the cache until they are combined into an mp4 at the end of the hour.
+
+### `GET /api/preview/<file_name>/thumbnail.jpg`
+
+Specific preview frame from preview cache.
+
+### `GET /review/<review_id>/preview`
+
+Looping image made from preview video / frames during this review item.
+
+| param     | Type | Description                      |
+| --------- | ---- | -------------------------------- |
+| `format`  | str  | Format of preview [`gif`, `mp4`] |
+
+### `GET /<camera>/start/<start-timestamp>/end/<end-timestamp>/preview`
+
+Looping image made from preview video / frames during this time range.
+
+| param     | Type | Description                      |
+| --------- | ---- | -------------------------------- |
+| `format`  | str  | Format of preview [`gif`, `mp4`] |
+
+## Recordings
+
+### `GET /vod/<year>-<month>/<day>/<hour>/<camera>/master.m3u8`
+
+HTTP Live Streaming Video on Demand URL for the specified hour and camera. Can be viewed in an application like VLC.
+
+### `GET /vod/event/<event-id>/index.m3u8`
+
+HTTP Live Streaming Video on Demand URL for the specified event. Can be viewed in an application like VLC.
+
+### `GET /vod/<camera>/start/<start-timestamp>/end/<end-timestamp>/index.m3u8`
+
+HTTP Live Streaming Video on Demand URL for the camera with the specified time range. Can be viewed in an application like VLC.
+
+### `GET /api/exports`
+
+Fetch a list of all export recordings
+
+Sample response:
+```json
+[
+  {
+    "camera": "doorbell",
+    "date": 12800057,
+    "id": "doorbell_pjis54",
+    "in_progress": false,
+    "name": "2024-10-04 fox visit",
+    "thumb_path": "/media/frigate/clips/export/doorbell_pjis54.webp",
+    "video_path": "/media/frigate/exports/doorbell_pjis54.mp4"
+  }
+]
+```
+
+### `POST /api/export/<camera>/start/<start-timestamp>/end/<end-timestamp>`
+
+Export recordings from `start-timestamp` to `end-timestamp` for `camera` as a single mp4 file. These recordings will be exported to the `/media/frigate/exports` folder.
+
+It is also possible to export this recording as a time-lapse using the "playback" key in the json body, or specify a custom export filename, using the "name" key.
+
+**Optional Body:**
+
+```json
+{
+  "playback": "realtime", // playback factor: realtime or timelapse_25x
+  "name": "custom export name" // override the default export filename with a custom name
+}
+```
+
+### `DELETE /api/export/<export_name>`
+
+Delete an export from disk.
+
+### `PATCH /api/export/<export_name_current>/<export_name_new>`
+
+Renames an export.
+
+### `GET /api/<camera_name>/recordings/summary`
+
+Hourly summary of recordings data for a camera.
+
+### `GET /api/<camera_name>/recordings`
+
+Get recording segment details for the given timestamp range.
+
+| param    | Type | Description                           |
+| -------- | ---- | ------------------------------------- |
+| `after`  | int  | Unix timestamp for beginning of range |
+| `before` | int  | Unix timestamp for end of range       |
+
+### `GET /api/<camera_name>/recordings/<frame_time>/snapshot.png`
+
+Returns the snapshot image from the specific point in that cameras recordings.
+
+## Reviews
+
+### `GET /api/review`
+
+Reviews from the database. Accepts the following query string parameters:
+
+| param      | Type | Description                                                    |
+| ---------- | ---- | -------------------------------------------------------------- |
+| `before`   | int  | Epoch time                                                     |
+| `after`    | int  | Epoch time                                                     |
+| `cameras`  | str  | , separated list of cameras                                    |
+| `labels`   | str  | , separated list of labels                                     |
+| `zones`    | str  | , separated list of zones                                      |
+| `reviewed` | int  | Include items that have been reviewed (0 or 1)                 |
+| `limit`    | int  | Limit the number of events returned                            |
+| `severity` | str  | Limit items to severity (alert, detection, significant_motion) |
+
+### `GET /api/review/<id>`
+
+Get review with `id` from the database.
+
+### `GET /api/review/summary`
+
+Summary of reviews for the last 30 days. Accepts the following query string parameters:
+
+| param      | Type | Description                 |
+| ---------- | ---- | --------------------------- |
+| `cameras`  | str  | , separated list of cameras |
+| `labels`   | str  | , separated list of labels  |
+| `timezone` | str  | Timezone name               |
+
+### `POST /api/reviews/viewed`
+
+Mark item(s) as reviewed.
+
+**Required Body:**
+
+```json
+{
+  "ids": ["123", "456"] // , separated list of review IDs
+}
+```
+
+### `DELETE /api/review/<id>/viewed`
+
+Mark an item as not reviewed.
+
+### `POST /api/reviews/delete`
+
+Delete review items.
+
+**Required Body:**
+
+```json
+{
+  "ids": ["123", "456"] // , separated list of review IDs
+}
+```
+
+### `GET /review/activity/motion`
+
+Get the motion activity for camera(s) during a specified time period.
+
+| param     | Type | Description                 |
+| --------- | ---- | --------------------------- |
+| `before`  | int  | Epoch time                  |
+| `after`   | int  | Epoch time                  |
+| `cameras` | str  | , separated list of cameras |
+
+### `GET /review/activity/audio`
+
+Get the audio activity for camera(s) during a specified time period.
+
+| param     | Type | Description                 |
+| --------- | ---- | --------------------------- |
+| `before`  | int  | Epoch time                  |
+| `after`   | int  | Epoch time                  |
+| `cameras` | str  | , separated list of cameras |
+
+## Timeline
+
+### `GET /api/timeline`
+
+Timeline of key moments of an event(s) from the database. Accepts the following query string parameters:
+
+| param       | Type | Description                         |
+| ----------- | ---- | ----------------------------------- |
+| `camera`    | str  | Name of camera                      |
+| `source_id` | str  | ID of tracked object                |
+| `limit`     | int  | Limit the number of events returned |

docs/docs/integrations/home-assistant.md

@@ -47,7 +47,7 @@ that card.
 
 ## Configuration
 
-When configuring the integration, you will be asked for the `URL` of your Frigate instance which can be pointed at the internal unauthenticated port (`5000`) or the authenticated port (`8971`) for your instance. This may look like `http://<host>:5000/`.
+When configuring the integration, you will be asked for the `URL` of your Frigate instance which needs to be pointed at the internal unauthenticated port (`5000`) for your instance. This may look like `http://<host>:5000/`.
 
 ### Docker Compose Examples
 
@@ -55,7 +55,7 @@ If you are running Home Assistant Core and Frigate with Docker Compose on the sa
 
 #### Home Assistant running with host networking
 
-It is not recommended to run Frigate in host networking mode. In this example, you would use `http://172.17.0.1:5000` or `http://172.17.0.1:8971` when configuring the integration.
+It is not recommended to run Frigate in host networking mode. In this example, you would use `http://172.17.0.1:5000` when configuring the integration.
 
 ```yaml
 services:
@@ -75,7 +75,7 @@ services:
 
 #### Home Assistant _not_ running with host networking or in a separate compose file
 
-In this example, it is recommended to connect to the authenticated port, for example, `http://frigate:8971` when configuring the integration. There is no need to map the port for the Frigate container.
+In this example, you would use `http://frigate:5000` when configuring the integration. There is no need to map the port for the Frigate container.
 
 ```yaml
 services:
@@ -97,29 +97,20 @@ services:
 
 If you are using HassOS with the addon, the URL should be one of the following depending on which addon version you are using. Note that if you are using the Proxy Addon, you do NOT point the integration at the proxy URL. Just enter the URL used to access Frigate directly from your network.
 
-| Addon Version                  | URL                                       |
-| ------------------------------ | ----------------------------------------- |
-| Frigate NVR                    | `http://ccab4aaf-frigate:5000`            |
-| Frigate NVR (Full Access)      | `http://ccab4aaf-frigate-fa:5000`         |
-| Frigate NVR Beta               | `http://ccab4aaf-frigate-beta:5000`       |
-| Frigate NVR Beta (Full Access) | `http://ccab4aaf-frigate-fa-beta:5000`    |
-| Frigate NVR HailoRT Beta       | `http://ccab4aaf-frigate-hailo-beta:5000` |
+| Addon Version                  | URL                                    |
+| ------------------------------ | -------------------------------------- |
+| Frigate NVR                    | `http://ccab4aaf-frigate:5000`         |
+| Frigate NVR (Full Access)      | `http://ccab4aaf-frigate-fa:5000`      |
+| Frigate NVR Beta               | `http://ccab4aaf-frigate-beta:5000`    |
+| Frigate NVR Beta (Full Access) | `http://ccab4aaf-frigate-fa-beta:5000` |
 
 ### Frigate running on a separate machine
 
-If you run Frigate on a separate device within your local network, Home Assistant will need access to port 8971.
+If you run Frigate on a separate device within your local network, Home Assistant will need access to port 5000.
 
 #### Local network
 
-Use `http://<frigate_device_ip>:8971` as the URL for the integration so that authentication is required.
-
-:::tip
-
-The above URL assumes you have [disabled TLS](../configuration/tls).
-By default, TLS is enabled and Frigate will be using a self-signed certificate. HomeAssistant will fail to connect HTTPS to port 8971 since it fails to verify the self-signed certificate.
-Either disable TLS and use HTTP from HomeAssistant, or configure Frigate to be acessible with a valid certificate.
-
-:::
+Use `http://<frigate_device_ip>:5000` as the URL for the integration. If you want to protect access to port 5000, you can use firewall rules to limit access to the device running Home Assistant.
 
 ```yaml
 services:
@@ -127,7 +118,7 @@ services:
     image: ghcr.io/blakeblackshear/frigate:stable
     ...
     ports:
-      - "8971:8971"
+      - "5000:5000"
       ...
 ```
 
@@ -157,19 +148,19 @@ Home Assistant > Configuration > Integrations > Frigate > Options
 
 ## Entities Provided
 
-| Platform        | Description                                                                     |
-| --------------- | ------------------------------------------------------------------------------- |
-| `camera`        | Live camera stream (requires RTSP).                                             |
-| `image`         | Image of the latest detected object for each camera.                            |
-| `sensor`        | States to monitor Frigate performance, object counts for all zones and cameras. |
-| `switch`        | Switch entities to toggle detection, recordings and snapshots.                  |
-| `binary_sensor` | A "motion" binary sensor entity per camera/zone/object.                         |
+| Platform        | Description                                                                       |
+| --------------- | --------------------------------------------------------------------------------- |
+| `camera`        | Live camera stream (requires RTSP).                                               |
+| `image`         | Image of the latest detected object for each camera.                              |
+| `sensor`        | States to monitor Frigate performance, object counts for all zones and cameras.   |
+| `switch`        | Switch entities to toggle detection, recordings and snapshots.                    |
+| `binary_sensor` | A "motion" binary sensor entity per camera/zone/object.                           |
 
 ## Media Browser Support
 
 The integration provides:
 
-- Browsing tracked object recordings with thumbnails
+- Browsing event recordings with thumbnails
 - Browsing snapshots
 - Browsing recordings by month, day, camera, time
 
@@ -192,42 +183,24 @@ For clips to be castable to media devices, audio is required and may need to be
 
 Many people do not want to expose Frigate to the web, so the integration creates some public API endpoints that can be used for notifications.
 
-To load a thumbnail for a tracked object:
+To load a thumbnail for an event:
 
 ```
 https://HA_URL/api/frigate/notifications/<event-id>/thumbnail.jpg
 ```
 
-To load a snapshot for a tracked object:
+To load a snapshot for an event:
 
 ```
 https://HA_URL/api/frigate/notifications/<event-id>/snapshot.jpg
 ```
 
-To load a video clip of a tracked object using an Android device:
+To load a video clip of an event:
 
 ```
 https://HA_URL/api/frigate/notifications/<event-id>/clip.mp4
 ```
 
-To load a video clip of a tracked object using an iOS device:
-
-```
-https://HA_URL/api/frigate/notifications/<event-id>/master.m3u8
-```
-
-To load a preview gif of a tracked object:
-
-```
-https://HA_URL/api/frigate/notifications/<event-id>/event_preview.gif
-```
-
-To load a preview gif of a review item:
-
-```
-https://HA_URL/api/frigate/notifications/<review-id>/review_preview.gif
-```
-
 <a name="streams"></a>
 
 ## RTSP stream
@@ -242,7 +215,7 @@ For advanced usecases, this behavior can be changed with the [RTSP URL
 template](#options) option. When set, this string will override the default stream
 address that is derived from the default behavior described above. This option supports
 [jinja2 templates](https://jinja.palletsprojects.com/) and has the `camera` dict
-variables from [Frigate API](../integrations/api)
+variables from [Frigate API](api.md)
 available for the template. Note that no Home Assistant state is available to the
 template, only the camera dict from Frigate.
 
@@ -309,7 +282,3 @@ which server they are referring to.
 #### If I am detecting multiple objects, how do I assign the correct `binary_sensor` to the camera in HomeKit?
 
 The [HomeKit integration](https://www.home-assistant.io/integrations/homekit/) randomly links one of the binary sensors (motion sensor entities) grouped with the camera device in Home Assistant. You can specify a `linked_motion_sensor` in the Home Assistant [HomeKit configuration](https://www.home-assistant.io/integrations/homekit/#linked_motion_sensor) for each camera.
-
-#### I have set up automations based on the occupancy sensors. Sometimes the automation runs because the sensors are turned on, but then I look at Frigate I can't find the object that triggered the sensor. Is this a bug?
-
-No. The occupancy sensors have fewer checks in place because they are often used for things like turning the lights on where latency needs to be as low as possible. So false positives can sometimes trigger these sensors. If you want false positive filtering, you should use an mqtt sensor on the `frigate/events` or `frigate/reviews` topic.

docs/docs/integrations/mqtt.md

@@ -19,7 +19,7 @@ Causes Frigate to exit. Docker should be configured to automatically restart the
 
 ### `frigate/events`
 
-Message published for each changed tracked object. The first message is published when the tracked object is no longer marked as a false_positive. When Frigate finds a better snapshot of the tracked object or when a zone change occurs, it will publish a message with the same id. When the tracked object ends, a final message is published with `end_time` set.
+Message published for each changed event. The first message is published when the tracked object is no longer marked as a false_positive. When Frigate finds a better snapshot of the tracked object or when a zone change occurs, it will publish a message with the same id. When the event ends, a final message is published with `end_time` set.
 
 ```json
 {
@@ -28,14 +28,7 @@ Message published for each changed tracked object. The first message is publishe
     "id": "1607123955.475377-mxklsc",
     "camera": "front_door",
     "frame_time": 1607123961.837752,
-    "snapshot": {
-        "frame_time": 1607123965.975463,
-        "box": [415, 489, 528, 700],
-        "area": 12728,
-        "region": [260, 446, 660, 846],
-        "score": 0.77546,
-        "attributes": [],
-    },
+    "snapshot_time": 1607123961.837752,
     "label": "person",
     "sub_label": null,
     "top_score": 0.958984375,
@@ -52,7 +45,6 @@ Message published for each changed tracked object. The first message is publishe
     "thumbnail": null,
     "has_snapshot": false,
     "has_clip": false,
-    "active": true, // convenience attribute, this is strictly opposite of "stationary"
     "stationary": false, // whether or not the object is considered stationary
     "motionless_count": 0, // number of frames the object has been motionless
     "position_changes": 2, // number of times the object has moved from a stationary position
@@ -65,14 +57,7 @@ Message published for each changed tracked object. The first message is publishe
     "id": "1607123955.475377-mxklsc",
     "camera": "front_door",
     "frame_time": 1607123962.082975,
-    "snapshot": {
-        "frame_time": 1607123965.975463,
-        "box": [415, 489, 528, 700],
-        "area": 12728,
-        "region": [260, 446, 660, 846],
-        "score": 0.77546,
-        "attributes": [],
-    },
+    "snapshot_time": 1607123961.837752,
     "label": "person",
     "sub_label": ["John Smith", 0.79],
     "top_score": 0.958984375,
@@ -89,7 +74,6 @@ Message published for each changed tracked object. The first message is publishe
     "thumbnail": null,
     "has_snapshot": false,
     "has_clip": false,
-    "active": true, // convenience attribute, this is strictly opposite of "stationary"
     "stationary": false, // whether or not the object is considered stationary
     "motionless_count": 0, // number of frames the object has been motionless
     "position_changes": 2, // number of times the object has changed position
@@ -108,40 +92,30 @@ Message published for each changed tracked object. The first message is publishe
 }
 ```
 
-### `frigate/tracked_object_update`
-
-Message published for updates to tracked object metadata, for example when GenAI runs and returns a tracked object description.
-
-```json
-{
-  "type": "description",
-  "id": "1607123955.475377-mxklsc",
-  "description": "The car is a red sedan moving away from the camera."
-}
-```
-
 ### `frigate/reviews`
 
 Message published for each changed review item. The first message is published when the `detection` or `alert` is initiated. When additional objects are detected or when a zone change occurs, it will publish a, `update` message with the same id. When the review activity has ended a final `end` message is published.
 
 ```json
 {
-  "type": "update", // new, update, end
+  "type": "update",  // new, update, end
   "before": {
-    "id": "1718987129.308396-fqk5ka", // review_id
+    "id": "1718987129.308396-fqk5ka",  // review_id
     "camera": "front_cam",
     "start_time": 1718987129.308396,
     "end_time": null,
     "severity": "detection",
     "thumb_path": "/media/frigate/clips/review/thumb-front_cam-1718987129.308396-fqk5ka.webp",
     "data": {
-      "detections": [
-        // list of event IDs
+      "detections": [  // list of event IDs
         "1718987128.947436-g92ztx",
         "1718987148.879516-d7oq7r",
         "1718987126.934663-q5ywpt"
       ],
-      "objects": ["person", "car"],
+      "objects": [
+        "person",
+        "car"
+      ],
       "sub_labels": [],
       "zones": [],
       "audio": []
@@ -160,9 +134,14 @@ Message published for each changed review item. The first message is published w
         "1718987148.879516-d7oq7r",
         "1718987126.934663-q5ywpt"
       ],
-      "objects": ["person", "car"],
+      "objects": [
+        "person",
+        "car"
+      ],
       "sub_labels": ["Bob"],
-      "zones": ["front_yard"],
+      "zones": [
+        "front_yard"
+      ],
       "audio": []
     }
   }
@@ -173,18 +152,6 @@ Message published for each changed review item. The first message is published w
 
 Same data available at `/api/stats` published at a configurable interval.
 
-### `frigate/camera_activity`
-
-Returns data about each camera, its current features, and if it is detecting motion, objects, etc. Can be triggered by publising to `frigate/onConnect`
-
-### `frigate/notifications/set`
-
-Topic to turn notifications on and off. Expected values are `ON` and `OFF`.
-
-### `frigate/notifications/state`
-
-Topic with current state of notifications. Published values are `ON` and `OFF`.
-
 ## Frigate Camera Topics
 
 ### `frigate/<camera_name>/<object_name>`
@@ -192,23 +159,11 @@ Topic with current state of notifications. Published values are `ON` and `OFF`.
 Publishes the count of objects for the camera for use as a sensor in Home Assistant.
 `all` can be used as the object_name for the count of all objects for the camera.
 
-### `frigate/<camera_name>/<object_name>/active`
-
-Publishes the count of active objects for the camera for use as a sensor in Home
-Assistant. `all` can be used as the object_name for the count of all active objects
-for the camera.
-
 ### `frigate/<zone_name>/<object_name>`
 
 Publishes the count of objects for the zone for use as a sensor in Home Assistant.
 `all` can be used as the object_name for the count of all objects for the zone.
 
-### `frigate/<zone_name>/<object_name>/active`
-
-Publishes the count of active objects for the zone for use as a sensor in Home
-Assistant. `all` can be used as the object_name for the count of all objects for the
-zone.
-
 ### `frigate/<camera_name>/<object_name>/snapshot`
 
 Publishes a jpeg encoded frame of the detected object type. When the object is no longer detected, the highest confidence image is published or the original image

docs/docs/integrations/plus.md

@@ -19,7 +19,7 @@ Once logged in, you can generate an API key for Frigate in Settings.
 
 ### Set your API key
 
-In Frigate, you can use an environment variable or a docker secret named `PLUS_API_KEY` to enable the `Frigate+` buttons on the Explore page. Home Assistant Addon users can set it under Settings > Addons > Frigate NVR > Configuration > Options (be sure to toggle the "Show unused optional configuration options" switch).
+In Frigate, you can use an environment variable or a docker secret named `PLUS_API_KEY` to enable the Frigate+ page. Home Assistant Addon users can set it under Settings > Addons > Frigate NVR > Configuration > Options (be sure to toggle the "Show unused optional configuration options" switch).
 
 :::warning
 
@@ -29,9 +29,7 @@ You cannot use the `environment_vars` section of your Frigate configuration file
 
 ## Submit examples
 
-Once your API key is configured, you can submit examples directly from the Explore page in Frigate. From the More Filters menu, select "Has a Snapshot - Yes" and "Submitted to Frigate+ - No", and press Apply at the bottom of the pane. Then, click on a thumbnail and select the Snapshot tab.
-
-You can use your keyboard's left and right arrow keys to quickly navigate between the tracked object snapshots.
+Once your API key is configured, you can submit examples directly from the Frigate+ page.
 
 :::note
 
@@ -39,11 +37,13 @@ Snapshots must be enabled to be able to submit examples to Frigate+
 
 :::
 
+![Send To Plus](/img/plus/send-to-plus.jpg)
+
 ![Submit To Plus](/img/plus/submit-to-plus.jpg)
 
 ### Annotate and verify
 
-You can view all of your submitted images at [https://plus.frigate.video](https://plus.frigate.video). Annotations can be added by clicking an image. For more detailed information about labeling, see the documentation on [annotating](../plus/annotating.md).
+You can view all of your submitted images at [https://plus.frigate.video](https://plus.frigate.video). Annotations can be added by clicking an image. For more detailed information about labeling, see the documentation on [improving your model](../plus/improving_model.md).
 
 ![Annotate](/img/annotate.png)
 

docs/docs/integrations/third_party_extensions.md

@@ -13,20 +13,12 @@ Please use your own knowledge to assess and vet them before you install anything
 
 :::
 
-## [Advanced Camera Card (formerly known as Frigate Card](https://card.camera/#/README)
-
-The [Advanced Camera Card](https://card.camera/#/README) is a Home Assistant dashboard card with deep Frigate integration.
-
 ## [Double Take](https://github.com/skrashevich/double-take)
 
 [Double Take](https://github.com/skrashevich/double-take) provides an unified UI and API for processing and training images for facial recognition.
 It supports automatically setting the sub labels in Frigate for person objects that are detected and recognized.
 This is a fork (with fixed errors and new features) of [original Double Take](https://github.com/jakowenko/double-take) project which, unfortunately, isn't being maintained by author.
 
-## [Frigate Notify](https://github.com/0x2142/frigate-notify)
-
-[Frigate Notify](https://github.com/0x2142/frigate-notify) is a simple app designed to send notifications from Frigate NVR to your favorite platforms. Intended to be used with standalone Frigate installations - Home Assistant not required, MQTT is optional but recommended.
-
 ## [Frigate telegram](https://github.com/OldTyT/frigate-telegram)
 
 [Frigate telegram](https://github.com/OldTyT/frigate-telegram) makes it possible to send events from Frigate to Telegram. Events are sent as a message with a text description, video, and thumbnail.

docs/docs/plus/first_model.md

@@ -9,11 +9,11 @@ Before requesting your first model, you will need to upload and verify at least
 
 It is recommended to submit **both** true positives and false positives. This will help the model differentiate between what is and isn't correct. You should aim for a target of 80% true positive submissions and 20% false positives across all of your images. If you are experiencing false positives in a specific area, submitting true positives for any object type near that area in similar lighting conditions will help teach the model what that area looks like when no objects are present.
 
-For more detailed recommendations, you can refer to the docs on [annotating](./annotating.md).
+For more detailed recommendations, you can refer to the docs on [improving your model](./improving_model.md).
 
 ## Step 2: Submit a model request
 
-Once you have an initial set of verified images, you can request a model on the Models page. For guidance on choosing a model type, refer to [this part of the documentation](./index.md#available-model-types). If you are unsure which type to request, you can test the base model for each version from the "Base Models" tab. Each model request requires 1 of the 12 trainings that you receive with your annual subscription. This model will support all [label types available](./index.md#available-label-types) even if you do not submit any examples for those labels. Model creation can take up to 36 hours.
+Once you have an initial set of verified images, you can request a model on the Models page. For guidance on choosing a model type, refer to [this part of the documentation](./index.md#available-model-types). Each model request requires 1 of the 12 trainings that you receive with your annual subscription. This model will support all [label types available](./index.md#available-label-types) even if you do not submit any examples for those labels. Model creation can take up to 36 hours.
 ![Plus Models Page](/img/plus/plus-models.jpg)
 
 ## Step 3: Set your model id in the config

docs/docs/plus/improving_model.md

@@ -1,9 +1,17 @@
 ---
-id: annotating
-title: Annotating your images
+id: improving_model
+title: Improving your model
 ---
 
-For the best results, follow these guidelines. You may also want to review the documentation on [improving your model](./index.md#improving-your-model).
+You may find that Frigate+ models result in more false positives initially, but by submitting true and false positives, the model will improve. With all the new images now being submitted by subscribers, future base models will improve as more and more examples are incorporated. Note that only images with at least one verified label will be used when training your model. Submitting an image from Frigate as a true or false positive will not verify the image. You still must verify the image in Frigate+ in order for it to be used in training.
+
+- **Submit both true positives and false positives**. This will help the model differentiate between what is and isn't correct. You should aim for a target of 80% true positive submissions and 20% false positives across all of your images. If you are experiencing false positives in a specific area, submitting true positives for any object type near that area in similar lighting conditions will help teach the model what that area looks like when no objects are present.
+- **Lower your thresholds a little in order to generate more false/true positives near the threshold value**. For example, if you have some false positives that are scoring at 68% and some true positives scoring at 72%, you can try lowering your threshold to 65% and submitting both true and false positives within that range. This will help the model learn and widen the gap between true and false positive scores.
+- **Submit diverse images**. For the best results, you should provide at least 100 verified images per camera. Keep in mind that varying conditions should be included. You will want images from cloudy days, sunny days, dawn, dusk, and night. As circumstances change, you may need to submit new examples to address new types of false positives. For example, the change from summer days to snowy winter days or other changes such as a new grill or patio furniture may require additional examples and training.
+
+## Properly labeling images
+
+For the best results, follow the following guidelines.
 
 **Label every object in the image**: It is important that you label all objects in each image before verifying. If you don't label a car for example, the model will be taught that part of the image is _not_ a car and it will start to get confused. You can exclude labels that you don't want detected on any of your cameras.
 
@@ -17,17 +25,9 @@ For the best results, follow these guidelines. You may also want to review the d
 
 ![Fedex Logo](/img/plus/fedex-logo.jpg)
 
-## AI suggested labels
-
-If you have an active Frigate+ subscription, new uploads will be scanned for the objects configured for you camera and you will see suggested labels as light blue boxes when annotating in Frigate+. These suggestions are processed via a queue and typically complete within a minute after uploading, but processing times can be longer.
-
-![Suggestions](/img/plus/suggestions.webp)
-
-Suggestions are converted to labels when saving, so you should remove any errant suggestions. There is already some logic designed to avoid duplicate labels, but you may still occasionally see some duplicate suggestions. You should keep the most accurate bounding box and delete any duplicates so that you have just one label per object remaining.
-
 ## False positive labels
 
-False positives will be shown with a read box and the label will have a strike through. These can't be adjusted, but they can be deleted if you accidentally submit a true positive as a false positive from Frigate.
+False positives will be shown with a read box and the label will have a strike through.
 ![false positive](/img/plus/false-positive.jpg)
 
 Misidentified objects should have a correct label added. For example, if a person was mistakenly detected as a cat, you should submit it as a false positive in Frigate and add a label for the person. The boxes will overlap.

docs/docs/plus/index.md

@@ -3,9 +3,15 @@ id: index
 title: Models
 ---
 
-<a href="https://frigate.video/plus" target="_blank" rel="nofollow">Frigate+</a> offers models trained on images submitted by Frigate+ users from their security cameras and is specifically designed for the way Frigate NVR analyzes video footage. These models offer higher accuracy with less resources. The images you upload are used to fine tune a base model trained from images uploaded by all Frigate+ users. This fine tuning process results in a model that is optimized for accuracy in your specific conditions.
+<a href="https://frigate.video/plus" target="_blank" rel="nofollow">Frigate+</a> offers models trained on images submitted by Frigate+ users from their security cameras and is specifically designed for the way Frigate NVR analyzes video footage. These models offer higher accuracy with less resources. The images you upload are used to fine tune a baseline model trained from images uploaded by all Frigate+ users. This fine tuning process results in a model that is optimized for accuracy in your specific conditions.
 
-With a subscription, 12 model trainings to fine tune your model per year are included. In addition, you will have access to any base models published while your subscription is active. If you cancel your subscription, you will retain access to any trained and base models in your account. An active subscription is required to submit model requests or purchase additional trainings. New base models are published quarterly with target dates of January 15th, April 15th, July 15th, and October 15th.
+:::info
+
+The baseline model isn't directly available after subscribing. This may change in the future, but for now you will need to submit a model request with the minimum number of images.
+
+:::
+
+With a subscription, 12 model trainings per year are included. If you cancel your subscription, you will retain access to any trained models. An active subscription is required to submit model requests or purchase additional trainings.
 
 Information on how to integrate Frigate+ with Frigate can be found in the [integration docs](../integrations/plus.md).
 
@@ -13,7 +19,7 @@ Information on how to integrate Frigate+ with Frigate can be found in the [integ
 
 There are two model types offered in Frigate+, `mobiledet` and `yolonas`. Both of these models are object detection models and are trained to detect the same set of labels [listed below](#available-label-types).
 
-Not all model types are supported by all detectors, so it's important to choose a model type to match your detector as shown in the table under [supported detector types](#supported-detector-types). You can test model types for compatibility and speed on your hardware by using the base models.
+Not all model types are supported by all detectors, so it's important to choose a model type to match your detector as shown in the table under [supported detector types](#supported-detector-types).
 
 | Model Type  | Description                                                                                                                                  |
 | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
@@ -26,31 +32,23 @@ Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVi
 
 :::warning
 
-Using Frigate+ models with `onnx` and `rocm` is only available with Frigate 0.15 and later.
+Using Frigate+ models with `onnx` and `rocm` is only available with Frigate 0.15, which is still under development.
 
 :::
 
-| Hardware                                                                         | Recommended Detector Type | Recommended Model Type |
-| -------------------------------------------------------------------------------- | ------------------------- | ---------------------- |
-| [CPU](/configuration/object_detectors.md#cpu-detector-not-recommended)           | `cpu`                     | `mobiledet`            |
-| [Coral (all form factors)](/configuration/object_detectors.md#edge-tpu-detector) | `edgetpu`                 | `mobiledet`            |
-| [Intel](/configuration/object_detectors.md#openvino-detector)                    | `openvino`                | `yolonas`              |
-| [NVidia GPU](/configuration/object_detectors#onnx)\*                             | `onnx`                    | `yolonas`              |
-| [AMD ROCm GPU](/configuration/object_detectors#amdrocm-gpu-detector)\*           | `rocm`                    | `yolonas`              |
+| Hardware                                                                                                                     | Recommended Detector Type | Recommended Model Type |
+| ---------------------------------------------------------------------------------------------------------------------------- | ------------------------- | ---------------------- |
+| [CPU](/configuration/object_detectors.md#cpu-detector-not-recommended)                                                       | `cpu`                     | `mobiledet`            |
+| [Coral (all form factors)](/configuration/object_detectors.md#edge-tpu-detector)                                             | `edgetpu`                 | `mobiledet`            |
+| [Intel](/configuration/object_detectors.md#openvino-detector)                                                                | `openvino`                | `yolonas`              |
+| [NVidia GPU](https://deploy-preview-13787--frigate-docs.netlify.app/configuration/object_detectors#onnx)\*                   | `onnx`                    | `yolonas`              |
+| [AMD ROCm GPU](https://deploy-preview-13787--frigate-docs.netlify.app/configuration/object_detectors#amdrocm-gpu-detector)\* | `rocm`                    | `yolonas`              |
 
 _\* Requires Frigate 0.15_
 
-## Improving your model
-
-Some users may find that Frigate+ models result in more false positives initially, but by submitting true and false positives, the model will improve. With all the new images now being submitted by subscribers, future base models will improve as more and more examples are incorporated. Note that only images with at least one verified label will be used when training your model. Submitting an image from Frigate as a true or false positive will not verify the image. You still must verify the image in Frigate+ in order for it to be used in training.
-
-- **Submit both true positives and false positives**. This will help the model differentiate between what is and isn't correct. You should aim for a target of 80% true positive submissions and 20% false positives across all of your images. If you are experiencing false positives in a specific area, submitting true positives for any object type near that area in similar lighting conditions will help teach the model what that area looks like when no objects are present.
-- **Lower your thresholds a little in order to generate more false/true positives near the threshold value**. For example, if you have some false positives that are scoring at 68% and some true positives scoring at 72%, you can try lowering your threshold to 65% and submitting both true and false positives within that range. This will help the model learn and widen the gap between true and false positive scores.
-- **Submit diverse images**. For the best results, you should provide at least 100 verified images per camera. Keep in mind that varying conditions should be included. You will want images from cloudy days, sunny days, dawn, dusk, and night. As circumstances change, you may need to submit new examples to address new types of false positives. For example, the change from summer days to snowy winter days or other changes such as a new grill or patio furniture may require additional examples and training.
-
 ## Available label types
 
-Frigate+ models support a more relevant set of objects for security cameras. The labels for annotation in Frigate+ are configurable by editing the camera in the Cameras section of Frigate+. Currently, the following objects are supported:
+Frigate+ models support a more relevant set of objects for security cameras. Currently, the following objects are supported:
 
 - **People**: `person`, `face`
 - **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `license_plate`
@@ -60,19 +58,9 @@ Frigate+ models support a more relevant set of objects for security cameras. The
 
 Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
 
-### Candidate labels
-
-Candidate labels are also available for annotation. These labels don't have enough data to be included in the model yet, but using them will help add support sooner. You can enable these labels by editing the camera settings.
-
-Where possible, these labels are mapped to existing labels during training. For example, any `baby` labels are mapped to `person` until support for new labels is added.
-
-The candidate labels are: `baby`, `royal mail`, `canada post`, `bpost`, `skunk`, `badger`, `possum`, `rodent`, `kangaroo`, `chicken`, `groundhog`, `boar`, `hedgehog`, `school bus`, `tractor`, `golf cart`, `garbage truck`, `bus`, `sports ball`
-
-Candidate labels are not available for automatic suggestions.
-
 ### Label attributes
 
-Frigate has special handling for some labels when using Frigate+ models. `face`, `license_plate`, and delivery logos such as `amazon`, `ups`, and `fedex` are considered attribute labels which are not tracked like regular objects and do not generate review items directly. In addition, the `threshold` filter will have no effect on these labels. You should adjust the `min_score` and other filter values as needed.
+Frigate has special handling for some labels when using Frigate+ models. `face`, `license_plate`, and delivery logos such as `amazon`, `ups`, and `fedex` are considered attribute labels which are not tracked like regular objects and do not generate events. In addition, the `threshold` filter will have no effect on these labels. You should adjust the `min_score` and other filter values as needed.
 
 In order to have Frigate start using these attribute labels, you will need to add them to the list of objects to track:
 

docs/docs/troubleshooting/edgetpu.md

@@ -40,17 +40,6 @@ Some users have reported that this older device runs an older kernel causing iss
 6. Open the control panel - info scree. The coral TPU will now be recognised as a USB Device - google inc
 7. Start the frigate container. Everything should work now!
 
-### QNAP NAS
-
-QNAP NAS devices, such as the TS-253A, may use connected Coral TPU devices if [QuMagie](https://www.qnap.com/en/software/qumagie) is installed along with its QNAP AI Core extension. If any of the features—`facial recognition`, `object recognition`, or `similar photo recognition`—are enabled, Container Station applications such as `Frigate` or `CodeProject.AI Server` will be unable to initialize the TPU device in use.
-To allow the Coral TPU device to be discovered, the you must either:
-
-1. [Disable the AI recognition features in QuMagie](https://docs.qnap.com/application/qumagie/2.x/en-us/configuring-qnap-ai-core-settings-FB13CE03.html),
-2. Remove the QNAP AI Core extension or
-3. Manually start the QNAP AI Core extension after Frigate has fully started (not recommended).
-
-It is also recommended to restart the NAS once the changes have been made.
-
 ## USB Coral Detection Appears to be Stuck
 
 The USB Coral can become stuck and need to be restarted, this can happen for a number of reasons depending on hardware and software setup. Some common reasons are:

docs/docs/troubleshooting/faqs.md

@@ -17,11 +17,7 @@ ffmpeg:
     record: preset-record-generic-audio-aac
 ```
 
-### How can I get sound in live view?
-
-Audio is only supported for live view when go2rtc is configured, see [the live docs](../configuration/live.md) for more information.
-
-### I can't view recordings in the Web UI.
+### I can't view events or recordings in the Web UI.
 
 Ensure your cameras send h264 encoded video, or [transcode them](/configuration/restream.md).
 
@@ -102,11 +98,3 @@ docker run -d \
   -p 8555:8555/udp \
   ghcr.io/blakeblackshear/frigate:stable
 ```
-
-### My RTSP stream works fine in VLC, but it does not work when I put the same URL in my Frigate config. Is this a bug?
-
-No. Frigate uses the TCP protocol to connect to your camera's RTSP URL. VLC automatically switches between UDP and TCP depending on network conditions and stream availability. So a stream that works in VLC but not in Frigate is likely due to VLC selecting UDP as the transfer protocol.
-
-TCP ensures that all data packets arrive in the correct order. This is crucial for video recording, decoding, and stream processing, which is why Frigate enforces a TCP connection. UDP is faster but less reliable, as it does not guarantee packet delivery or order, and VLC does not have the same requirements as Frigate.
-
-You can still configure Frigate to use UDP by using ffmpeg input args or the preset `preset-rtsp-udp`. See the [ffmpeg presets](/configuration/ffmpeg_presets) documentation.