Set ulimit with Python (#19105 )

* Set ulimit with python instead of in s6 startup * move to services and add env var * add comment
Fix ulimit script (#19095 )
2026-07-21 11:19:02 +03:00 · 2025-07-11 08:11:35 -05:00 · 2025-07-10 18:47:31 -05:00 · 2025-07-10 10:54:47 -06:00 · 2025-07-10 08:48:42 -06:00 · 2025-07-09 09:49:57 -06:00
210 changed files with 11171 additions and 4934 deletions
@@ -12,6 +12,7 @@ argmax
 argmin
 argpartition
 ascontiguousarray
+astype
 authelia
 authentik
 autodetected
@@ -195,6 +196,7 @@ poweroff
 preexec
 probesize
 protobuf
+pstate
 psutil
 pubkey
 putenv
@@ -278,6 +280,7 @@ uvicorn
 vaapi
 vainfo
 variations
+vbios
 vconcat
 vitb
 vstream
@@ -3,10 +3,12 @@
 set -euxo pipefail

 # Cleanup the old github host key
-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
+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

 # 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
@@ -33,9 +33,9 @@ runs:
      with:
        string: ${{ github.repository }}
    - name: Set up QEMU
-      uses: docker/setup-qemu-action@v2
+      uses: docker/setup-qemu-action@v3
    - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v2
+      uses: docker/setup-buildx-action@v3
    - name: Log in to the Container registry
      uses: docker/login-action@465a07811f14bebb1938fbed4728c6a1ff8901fc
      with:
@@ -13,6 +13,7 @@
 - [ ] New feature
 - [ ] Breaking change (fix/feature causing existing functionality to break)
 - [ ] Code quality improvements to existing code
+- [ ] Documentation Update

 ## Additional information

@@ -7,7 +7,7 @@ on:
      - dev
      - master
    paths-ignore:
-      - 'docs/**'      
+      - "docs/**"

 # only run the latest commit to avoid cache overwrites
 concurrency:
@@ -19,11 +19,13 @@ env:

 jobs:
  amd64_build:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
    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
@@ -40,11 +42,13 @@ jobs:
          tags: ${{ steps.setup.outputs.image-name }}-amd64
          cache-from: type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64
  arm64_build:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
    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
@@ -62,8 +66,9 @@ 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@v4
+        uses: docker/bake-action@v6
        with:
+          source: .
          push: true
          targets: rpi
          files: docker/rpi/rpi.hcl
@@ -71,21 +76,14 @@ 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-latest
+    runs-on: ubuntu-22.04
    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
@@ -97,8 +95,9 @@ 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@v4
+        uses: docker/bake-action@v6
        with:
+          source: .
          push: true
          targets: tensorrt
          files: docker/tensorrt/trt.hcl
@@ -107,11 +106,13 @@ 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-latest
+    runs-on: ubuntu-22.04
    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
@@ -123,8 +124,9 @@ 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@v4
+        uses: docker/bake-action@v6
        with:
+          source: .
          push: true
          targets: tensorrt
          files: docker/tensorrt/trt.hcl
@@ -133,13 +135,15 @@ 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-latest
+    runs-on: ubuntu-22.04
    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
@@ -148,8 +152,9 @@ jobs:
      - name: Build and push TensorRT (x86 GPU)
        env:
          COMPUTE_LEVEL: "50 60 70 80 90"
-        uses: docker/bake-action@v4
+        uses: docker/bake-action@v6
        with:
+          source: .
          push: true
          targets: tensorrt
          files: docker/tensorrt/trt.hcl
@@ -158,21 +163,24 @@ jobs:
            *.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-latest
+    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: ${{ secrets.GITHUB_TOKEN }}
      - name: Build and push Rockchip build
-        uses: docker/bake-action@v3
+        uses: docker/bake-action@v6
        with:
+          source: .
          push: true
          targets: rk
          files: docker/rockchip/rk.hcl
@@ -180,7 +188,7 @@ jobs:
            rk.tags=${{ steps.setup.outputs.image-name }}-rk
            *.cache-from=type=gha
  combined_extra_builds:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
    name: Combined Extra Builds
    needs:
      - amd64_build
@@ -188,14 +196,17 @@ jobs:
    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@v4
+        uses: docker/bake-action@v6
        with:
+          source: .
          push: true
          targets: h8l
          files: docker/hailo8l/h8l.hcl
@@ -207,8 +218,9 @@ jobs:
        env:
          AMDGPU: gfx
          HSA_OVERRIDE: 0
-        uses: docker/bake-action@v3
+        uses: docker/bake-action@v6
        with:
+          source: .
          push: true
          targets: rocm
          files: docker/rocm/rocm.hcl
@@ -218,7 +230,7 @@ jobs:
  # 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-latest
+    runs-on: ubuntu-22.04
    name: Assemble and push default build
    needs:
      - amd64_build
@@ -1,24 +0,0 @@
-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 }}
@@ -3,7 +3,7 @@ name: On pull request
 on:
  pull_request:
    paths-ignore:
-      - 'docs/**'
+      - "docs/**"

 env:
  DEFAULT_PYTHON: 3.9
@@ -19,6 +19,8 @@ jobs:
      DOCKER_BUILDKIT: "1"
    steps:
      - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
      - uses: actions/setup-node@master
        with:
          node-version: 16.x
@@ -38,6 +40,8 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
      - uses: actions/setup-node@master
        with:
          node-version: 16.x
@@ -52,6 +56,8 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
      - uses: actions/setup-node@master
        with:
          node-version: 20.x
@@ -67,8 +73,10 @@ 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@v5.1.0
+        uses: actions/setup-python@v5.3.0
        with:
          python-version: ${{ env.DEFAULT_PYTHON }}
      - name: Install requirements
@@ -88,6 +96,8 @@ jobs:
    steps:
      - name: Check out code
        uses: actions/checkout@v4
+        with:
+          persist-credentials: false
      - uses: actions/setup-node@master
        with:
          node-version: 16.x
@@ -11,6 +11,8 @@ jobs:

    steps:
      - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
      - id: lowercaseRepo
        uses: ASzc/change-string-case-action@v6
        with:
@@ -22,10 +24,13 @@ jobs:
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}
      - name: Create tag variables
+        env:
+          TAG: ${{ github.ref_name }}
+          LOWERCASE_REPO: ${{ steps.lowercaseRepo.outputs.lowercase }}
        run: |
-          BUILD_TYPE=$([[ "${{ github.ref_name }}" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]] && echo "stable" || echo "beta")
+          BUILD_TYPE=$([[ "${TAG}" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]] && echo "stable" || echo "beta")
          echo "BUILD_TYPE=${BUILD_TYPE}" >> $GITHUB_ENV
-          echo "BASE=ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}" >> $GITHUB_ENV
+          echo "BASE=ghcr.io/${LOWERCASE_REPO}" >> $GITHUB_ENV
          echo "BUILD_TAG=${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
@@ -23,7 +23,9 @@ jobs:
          exempt-pr-labels: "pinned,security,dependencies"
          operations-per-run: 120
      - name: Print outputs
-        run: echo ${{ join(steps.stale.outputs.*, ',') }}
+        env:
+          STALE_OUTPUT: ${{ join(steps.stale.outputs.*, ',') }}
+        run: echo "$STALE_OUTPUT"

  # clean_ghcr:
  #   name: Delete outdated dev container images
@@ -38,4 +40,3 @@ jobs:
  #         account-type: personal
  #         token: ${{ secrets.GITHUB_TOKEN }}
  #         token-type: github-token
-
@@ -1,7 +1,7 @@
 default_target: local

 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.15.0
+VERSION = 0.15.2
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty
@@ -61,7 +61,7 @@ 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")


 ######
@@ -23,7 +23,7 @@ services:
    #                     count: 1
    #                     capabilities: [gpu]
    environment:
-      YOLO_MODELS: yolov7-320
+      YOLO_MODELS: ""
    devices:
      - /dev/bus/usb:/dev/bus/usb
      # - /dev/dri:/dev/dri # for intel hwaccel, needs to be updated for your hardware
@@ -16,89 +16,25 @@ RUN mkdir /h8l-wheels
 # Build the wheels
 RUN pip3 wheel --wheel-dir=/h8l-wheels -c /requirements-wheels.txt -r /requirements-wheels-h8l.txt

-# Build HailoRT and create wheel
-FROM wheels AS build-hailort
+FROM wget AS hailort
 ARG TARGETARCH
-
-SHELL ["/bin/bash", "-c"]
-
-# Install necessary APT packages
-RUN apt-get -qq update \
-    && apt-get -qq install -y \
-    apt-transport-https \
-    gnupg \
-    wget \
-    # the key fingerprint can be obtained from https://ftp-master.debian.org/keys.html
-    && wget -qO- "https://keyserver.ubuntu.com/pks/lookup?op=get&search=0xA4285295FC7B1A81600062A9605C66F00D6C9793" | \
-    gpg --dearmor > /usr/share/keyrings/debian-archive-bullseye-stable.gpg \
-    && echo "deb [signed-by=/usr/share/keyrings/debian-archive-bullseye-stable.gpg] http://deb.debian.org/debian bullseye main contrib non-free" | \
-    tee /etc/apt/sources.list.d/debian-bullseye-nonfree.list \
-    && apt-get -qq update \
-    && apt-get -qq install -y \
-    python3.9 \
-    python3.9-dev \
-    build-essential cmake git \
-    && rm -rf /var/lib/apt/lists/*
-
-# Extract Python version and set environment variables
-RUN PYTHON_VERSION=$(python3 --version 2>&1 | awk '{print $2}' | cut -d. -f1,2) && \
-    PYTHON_VERSION_NO_DOT=$(echo $PYTHON_VERSION | sed 's/\.//') && \
-    echo "PYTHON_VERSION=$PYTHON_VERSION" > /etc/environment && \
-    echo "PYTHON_VERSION_NO_DOT=$PYTHON_VERSION_NO_DOT" >> /etc/environment
-
-# Clone and build HailoRT
-RUN . /etc/environment && \
-    git clone https://github.com/hailo-ai/hailort.git /opt/hailort && \
-    cd /opt/hailort && \
-    git checkout v4.18.0 && \
-    cmake -H. -Bbuild -DCMAKE_BUILD_TYPE=Release -DHAILO_BUILD_PYBIND=1 -DPYBIND11_PYTHON_VERSION=${PYTHON_VERSION} && \
-    cmake --build build --config release --target libhailort && \
-    cmake --build build --config release --target _pyhailort && \
-    cp build/hailort/libhailort/bindings/python/src/_pyhailort.cpython-${PYTHON_VERSION_NO_DOT}-$(if [ $TARGETARCH == "amd64" ]; then echo 'x86_64'; else echo 'aarch64'; fi )-linux-gnu.so hailort/libhailort/bindings/python/platform/hailo_platform/pyhailort/ && \
-    cp build/hailort/libhailort/src/libhailort.so hailort/libhailort/bindings/python/platform/hailo_platform/pyhailort/
-
-RUN ls -ahl /opt/hailort/build/hailort/libhailort/src/
-RUN ls -ahl /opt/hailort/hailort/libhailort/bindings/python/platform/hailo_platform/pyhailort/
-
-# Remove the existing setup.py if it exists in the target directory
-RUN rm -f /opt/hailort/hailort/libhailort/bindings/python/platform/setup.py
-
-# Copy generate_wheel_conf.py and setup.py
-COPY docker/hailo8l/pyhailort_build_scripts/generate_wheel_conf.py /opt/hailort/hailort/libhailort/bindings/python/platform/generate_wheel_conf.py
-COPY docker/hailo8l/pyhailort_build_scripts/setup.py /opt/hailort/hailort/libhailort/bindings/python/platform/setup.py
-
-# Run the generate_wheel_conf.py script
-RUN python3 /opt/hailort/hailort/libhailort/bindings/python/platform/generate_wheel_conf.py
-
-# Create a wheel file using pip3 wheel
-RUN cd /opt/hailort/hailort/libhailort/bindings/python/platform && \
-    python3 setup.py bdist_wheel --dist-dir /hailo-wheels
+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=build-hailort /hailo-wheels /deps/hailo-wheels
-COPY --from=build-hailort /etc/environment /etc/environment
-RUN CC=$(python3 -c "import sysconfig; import shlex; cc = sysconfig.get_config_var('CC'); cc_cmd = shlex.split(cc)[0]; print(cc_cmd[:-4] if cc_cmd.endswith('-gcc') else cc_cmd)") && \
-    echo "CC=$CC" >> /etc/environment
+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

-RUN . /etc/environment && \
-    mv /usr/local/lib/python${PYTHON_VERSION}/dist-packages/hailo_platform/pyhailort/libhailort.so /usr/lib/${CC} && \
-    cd /usr/lib/${CC}/ && \
-    ln -s libhailort.so libhailort.so.4.18.0
-
 # Copy base files from the rootfs stage
 COPY --from=rootfs / /

-# Set environment variables for Hailo SDK
-ENV PATH="/opt/hailort/bin:${PATH}"
-ENV LD_LIBRARY_PATH="/usr/lib/$(if [ $TARGETARCH == "amd64" ]; then echo 'x86_64'; else echo 'aarch64'; fi )-linux-gnu:${LD_LIBRARY_PATH}"
-
 # Set workdir
 WORKDIR /opt/frigate/
@@ -1,3 +1,9 @@
+target wget {
+  dockerfile = "docker/main/Dockerfile"
+  platforms = ["linux/arm64","linux/amd64"]
+  target = "wget"
+}
+
 target wheels {
  dockerfile = "docker/main/Dockerfile"
  platforms = ["linux/arm64","linux/amd64"]
@@ -19,6 +25,7 @@ target rootfs {
 target h8l {
  dockerfile = "docker/hailo8l/Dockerfile"
  contexts = {
+    wget = "target:wget"
    wheels = "target:wheels"
    deps = "target:deps"
    rootfs = "target:rootfs"
@@ -0,0 +1,19 @@
+#!/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"
+
@@ -1,67 +0,0 @@
-import json
-import os
-import platform
-import sys
-import sysconfig
-
-
-def extract_toolchain_info(compiler):
-    # Remove the "-gcc" or "-g++" suffix if present
-    if compiler.endswith("-gcc") or compiler.endswith("-g++"):
-        compiler = compiler.rsplit("-", 1)[0]
-
-    # Extract the toolchain and ABI part (e.g., "gnu")
-    toolchain_parts = compiler.split("-")
-    abi_conventions = next(
-        (part for part in toolchain_parts if part in ["gnu", "musl", "eabi", "uclibc"]),
-        "",
-    )
-
-    return abi_conventions
-
-
-def generate_wheel_conf():
-    conf_file_path = os.path.join(
-        os.path.abspath(os.path.dirname(__file__)), "wheel_conf.json"
-    )
-
-    # Extract current system and Python version information
-    py_version = f"cp{sys.version_info.major}{sys.version_info.minor}"
-    arch = platform.machine()
-    system = platform.system().lower()
-    libc_version = platform.libc_ver()[1]
-
-    # Get the compiler information
-    compiler = sysconfig.get_config_var("CC")
-    abi_conventions = extract_toolchain_info(compiler)
-
-    # Create the new configuration data
-    new_conf_data = {
-        "py_version": py_version,
-        "arch": arch,
-        "system": system,
-        "libc_version": libc_version,
-        "abi": abi_conventions,
-        "extension": {
-            "posix": "so",
-            "nt": "pyd",  # Windows
-        }[os.name],
-    }
-
-    # If the file exists, load the existing data
-    if os.path.isfile(conf_file_path):
-        with open(conf_file_path, "r") as conf_file:
-            conf_data = json.load(conf_file)
-        # Update the existing data with the new data
-        conf_data.update(new_conf_data)
-    else:
-        # If the file does not exist, use the new data
-        conf_data = new_conf_data
-
-    # Write the updated data to the file
-    with open(conf_file_path, "w") as conf_file:
-        json.dump(conf_data, conf_file, indent=4)
-
-
-if __name__ == "__main__":
-    generate_wheel_conf()
@@ -1,111 +0,0 @@
-import json
-import os
-
-from setuptools import find_packages, setup
-from wheel.bdist_wheel import bdist_wheel as orig_bdist_wheel
-
-
-class NonPurePythonBDistWheel(orig_bdist_wheel):
-    """Makes the wheel platform-dependent so it can be based on the _pyhailort architecture"""
-
-    def finalize_options(self):
-        orig_bdist_wheel.finalize_options(self)
-        self.root_is_pure = False
-
-
-def _get_hailort_lib_path():
-    lib_filename = "libhailort.so"
-    lib_path = os.path.join(
-        os.path.abspath(os.path.dirname(__file__)),
-        f"hailo_platform/pyhailort/{lib_filename}",
-    )
-    if os.path.exists(lib_path):
-        print(f"Found libhailort shared library at: {lib_path}")
-    else:
-        print(f"Error: libhailort shared library not found at: {lib_path}")
-        raise FileNotFoundError(f"libhailort shared library not found at: {lib_path}")
-    return lib_path
-
-
-def _get_pyhailort_lib_path():
-    conf_file_path = os.path.join(
-        os.path.abspath(os.path.dirname(__file__)), "wheel_conf.json"
-    )
-    if not os.path.isfile(conf_file_path):
-        raise FileNotFoundError(f"Configuration file not found: {conf_file_path}")
-
-    with open(conf_file_path, "r") as conf_file:
-        content = json.load(conf_file)
-        py_version = content["py_version"]
-        arch = content["arch"]
-        system = content["system"]
-        extension = content["extension"]
-        abi = content["abi"]
-
-        # Construct the filename directly
-        lib_filename = f"_pyhailort.cpython-{py_version.split('cp')[1]}-{arch}-{system}-{abi}.{extension}"
-        lib_path = os.path.join(
-            os.path.abspath(os.path.dirname(__file__)),
-            f"hailo_platform/pyhailort/{lib_filename}",
-        )
-
-        if os.path.exists(lib_path):
-            print(f"Found _pyhailort shared library at: {lib_path}")
-        else:
-            print(f"Error: _pyhailort shared library not found at: {lib_path}")
-            raise FileNotFoundError(
-                f"_pyhailort shared library not found at: {lib_path}"
-            )
-
-        return lib_path
-
-
-def _get_package_paths():
-    packages = []
-    pyhailort_lib = _get_pyhailort_lib_path()
-    hailort_lib = _get_hailort_lib_path()
-    if pyhailort_lib:
-        packages.append(pyhailort_lib)
-    if hailort_lib:
-        packages.append(hailort_lib)
-    packages.append(os.path.abspath("hailo_tutorials/notebooks/*"))
-    packages.append(os.path.abspath("hailo_tutorials/hefs/*"))
-    return packages
-
-
-if __name__ == "__main__":
-    setup(
-        author="Hailo team",
-        author_email="contact@hailo.ai",
-        cmdclass={
-            "bdist_wheel": NonPurePythonBDistWheel,
-        },
-        description="HailoRT",
-        entry_points={
-            "console_scripts": [
-                "hailo=hailo_platform.tools.hailocli.main:main",
-            ]
-        },
-        install_requires=[
-            "argcomplete",
-            "contextlib2",
-            "future",
-            "netaddr",
-            "netifaces",
-            "verboselogs",
-            "numpy==1.23.3",
-        ],
-        name="hailort",
-        package_data={
-            "hailo_platform": _get_package_paths(),
-        },
-        packages=find_packages(),
-        platforms=[
-            "linux_x86_64",
-            "linux_aarch64",
-            "win_amd64",
-        ],
-        url="https://hailo.ai/",
-        version="4.17.0",
-        zip_safe=False,
-    )
@@ -1,12 +1,12 @@
-appdirs==1.4.4
-argcomplete==2.0.0
-contextlib2==0.6.0.post1
-distlib==0.3.6
-filelock==3.8.0
-future==0.18.2
-importlib-metadata==5.1.0
-importlib-resources==5.1.2
-netaddr==0.8.0
-netifaces==0.10.9
-verboselogs==1.7
-virtualenv==20.17.0
+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.*
@@ -13,7 +13,7 @@ else
 fi

 # Clone the HailoRT driver repository
-git clone --depth 1 --branch v4.18.0 https://github.com/hailo-ai/hailort-drivers.git
+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
@@ -38,7 +38,7 @@ cd ../../
 if [ ! -d /lib/firmware/hailo ]; then
  sudo mkdir /lib/firmware/hailo
 fi
-sudo mv hailo8_fw.4.18.0.bin /lib/firmware/hailo/hailo8_fw.bin
+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/
@@ -211,8 +211,10 @@ 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 LIBAVFORMAT_VERSION_MAJOR=60

 # Install dependencies
 RUN --mount=type=bind,source=docker/main/install_deps.sh,target=/deps/install_deps.sh \
@@ -87,8 +87,8 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
    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 intel-level-zero-gpu intel-media-va-driver-non-free \
-        libmfx1 libmfxgen1 libvpl2
+        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
@@ -1,5 +1,7 @@
 click == 8.1.*
 # FastAPI
+aiohttp == 3.11.2
+starlette == 0.41.2
 starlette-context == 0.3.6
 fastapi == 0.115.*
 uvicorn == 0.30.*
@@ -42,8 +42,14 @@ 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
 echo "[INFO] Starting Frigate..."

 cd /opt/frigate || echo "[ERROR] Failed to change working directory to /opt/frigate"
@@ -43,6 +43,11 @@ 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+")
+}
+
 if [[ -f "/dev/shm/go2rtc.yaml" ]]; then
    echo "[INFO] Removing stale config from last run..."
    rm /dev/shm/go2rtc.yaml
@@ -61,6 +66,8 @@ 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
@@ -0,0 +1,45 @@
+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")
@@ -165,7 +165,7 @@ 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(ffmpeg_path, config.get('ffmpeg', {}).get('hwaccel_args', ''), input, '-rtsp_transport tcp -f rtsp {output}')}"

    if go2rtc_config.get("streams"):
        go2rtc_config["streams"]["birdseye"] = ffmpeg_cmd
@@ -22,6 +22,6 @@ 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/6.1-5/ffmpeg /usr/lib/ffmpeg/6.0/bin/
-ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-5/ffprobe /usr/lib/ffmpeg/6.0/bin/
+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}"
@@ -12,7 +12,5 @@ RUN rm -rf /usr/lib/btbn-ffmpeg/
 RUN --mount=type=bind,source=docker/rpi/install_deps.sh,target=/deps/install_deps.sh \
    /deps/install_deps.sh

-ENV LIBAVFORMAT_VERSION_MAJOR=58
-
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
@@ -12,26 +12,11 @@ ARG TARGETARCH
 COPY docker/tensorrt/requirements-amd64.txt /requirements-tensorrt.txt
 RUN mkdir -p /trt-wheels && pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt

-# Build CuDNN
-FROM wget AS cudnn-deps
-
-ARG COMPUTE_LEVEL
-
-RUN apt-get update \
-    && apt-get install -y git build-essential
-
-RUN wget https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/cuda-keyring_1.1-1_all.deb \
-&& dpkg -i cuda-keyring_1.1-1_all.deb \
-&& apt-get update \
-&& apt-get -y install cuda-toolkit \
-&& rm -rf /var/lib/apt/lists/*
-
 FROM tensorrt-base AS frigate-tensorrt
 ENV TRT_VER=8.5.3
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
    pip3 install -U /deps/trt-wheels/*.whl && \
    ldconfig
-COPY --from=cudnn-deps /usr/local/cuda-12.6 /usr/local/cuda

 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/
@@ -42,7 +27,7 @@ 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=cudnn-deps /usr/local/cuda-12.6 /usr/local/cuda
+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 \
@@ -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
@@ -41,7 +41,11 @@ 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
-RUN pip3 wheel --wheel-dir=/trt-wheels -r /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

 FROM build-wheels AS trt-model-wheels
 ARG DEBIAN_FRONTEND
@@ -24,8 +24,9 @@ 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="yolov7-320"
+ENV YOLO_MODELS=""

 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
@@ -11,6 +11,7 @@ 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}
@@ -19,6 +20,11 @@ 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
@@ -1 +1 @@
-cuda-python == 11.7; platform_machine == 'aarch64'
+cuda-python == 11.7; platform_machine == 'aarch64'
@@ -4,7 +4,9 @@ title: Advanced Options
 sidebar_label: Advanced Options
 ---

-### `logger`
+### Logging
+
+#### Frigate `logger`

 Change the default log level for troubleshooting purposes.

@@ -28,6 +30,18 @@ Examples of available modules are:
 - `watchdog.<camera_name>`
 - `ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.

+#### Go2RTC Logging
+
+See [the go2rtc docs](for logging configuration)
+
+```yaml
+go2rtc:
+  streams:
+    ...
+  log:
+    exec: trace
+```
+
 ### `environment_vars`

 This section can be used to set environment variables for those unable to modify the environment of the container (ie. within HassOS)
@@ -174,7 +188,7 @@ NOTE: The folder that is set for the config needs to be the folder that contains

 ### Custom go2rtc version

-Frigate currently includes go2rtc v1.9.4, there may be certain cases where you want to run a different version of go2rtc.
+Frigate currently includes go2rtc v1.9.2, there may be certain cases where you want to run a different version of go2rtc.

 To do this:

@@ -189,16 +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 `/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 `/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.

 ```bash
-curl -X POST http://frigate_host:5000/config/save -d @config.json
+curl -X POST http://frigate_host:5000/api/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/config/save -d @-
+yq r -j config.yml | curl -X POST http://frigate_host:5000/api/config/save -d @-
 ```

 ### Via Command Line
@@ -24,6 +24,11 @@ On startup, an admin user and password are generated and printed in the logs. It

 In the event that you are locked out of your instance, you can tell Frigate to reset the admin password and print it in the logs on next startup using the `reset_admin_password` setting in your config file.

+```yaml
+auth:
+  reset_admin_password: true
+```
+
 ## 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).
@@ -41,6 +41,7 @@ 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
@@ -49,6 +50,8 @@ 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:
@@ -65,6 +65,18 @@ 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.
@@ -77,7 +89,7 @@ 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://user:password@camera-ip:554/H264/ch1/main/av_stream # <----- Update for your camera
+        - path: rtsp://USERNAME:PASSWORD@CAMERA-IP/H264/ch1/main/av_stream # <----- Update for your camera
          roles:
            - detect
            - record
@@ -95,6 +107,29 @@ 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.
@@ -156,7 +191,9 @@ cameras:

 #### Reolink Doorbell

-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.
+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).

 ```yaml
 go2rtc:
@@ -181,7 +218,7 @@ go2rtc:
      - rtspx://192.168.1.1:7441/abcdefghijk
 ```

-[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#source-rtsp)
+[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#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.

@@ -194,3 +231,38 @@ 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
+```
+
+
@@ -100,6 +100,8 @@ 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 |
@@ -109,7 +111,7 @@ 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               |      ✅      |      ❌      |                                                                                                                                                 |
+| Sunba 405-D20X               |      ✅      |      ❌      | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatable.                                            |
 | 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.                                           |
@@ -3,15 +3,21 @@ 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.
+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.

-Semantic Search must be enabled to use Generative AI. 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 providers available to integrate with Frigate.
+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.

-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_`.
+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:
@@ -21,25 +27,36 @@ genai:
  model: gemini-1.5-flash

 cameras:
-  front_camera: ...
+  front_camera: 
+    genai:
+      enabled: True # <- enable GenAI for your front camera
+      use_snapshot: True
+      objects:
+        - person
+      required_zones:
+        - steps
  indoor_camera:
-    genai: # <- disable GenAI for your indoor camera
-      enabled: False
+    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.
+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.
+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. See the [Ollama documentation](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-does-ollama-handle-concurrent-requests).
+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

@@ -110,6 +127,12 @@ genai:
  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.
@@ -138,6 +161,10 @@ Frigate's thumbnail search excels at identifying specific details about tracked

 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:
@@ -166,9 +193,7 @@ genai:
    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. 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 thumbnails collected over the object's lifetime to the model. Using a snapshot provides the AI with a higher-resolution image (typically downscaled by the AI itself), but the trade-off is that only a single image is used, which might limit the model's ability to determine object movement or direction.
+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:
@@ -231,28 +231,11 @@ docker run -d \

 ### Setup Decoder

-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`.
+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.

 ```yaml
 ffmpeg:
-  hwaccel_args: preset-nvidia-h264
+  hwaccel_args: preset-nvidia
 ```

 If everything is working correctly, you should see a significant improvement in performance.
@@ -203,14 +203,13 @@ 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:
@@ -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.
+- 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.

 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 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.
+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.

 ```yaml
 go2rtc:
@@ -138,3 +138,13 @@ 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)
@@ -22,14 +22,22 @@ Frigate supports multiple different detectors that work on different types of ha
 - [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, using one of many default models.
- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` Frigate image when a supported ONNX model is configured.
+- [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. 
+- [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)

 :::

@@ -116,6 +124,30 @@ 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"`.
@@ -144,7 +176,9 @@ detectors:

 #### 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 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 OpenVINO model:

 ```yaml
 detectors:
@@ -223,7 +257,7 @@ 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, 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.
+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.

 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.

@@ -254,6 +288,7 @@ yolov4x-mish-640
 yolov7-tiny-288
 yolov7-tiny-416
 yolov7-640
+yolov7-416
 yolov7-320
 yolov7x-640
 yolov7x-320
@@ -264,7 +299,7 @@ An example `docker-compose.yml` fragment that converts the `yolov4-608` and `yol
 ```yml
 frigate:
  environment:
-    - YOLO_MODELS=yolov4-608,yolov7x-640
+    - YOLO_MODELS=yolov7-320,yolov7x-640
    - USE_FP16=false
 ```

@@ -282,6 +317,8 @@ 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:
@@ -290,6 +327,7 @@ 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
@@ -415,6 +453,24 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl

 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:
@@ -457,6 +513,7 @@ model:
  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
 ```
@@ -482,11 +539,12 @@ detectors:
  cpu1:
    type: cpu
    num_threads: 3
-    model:
-      path: "/custom_model.tflite"
  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.
@@ -599,27 +657,3 @@ $ cat /sys/kernel/debug/rknpu/load

 - All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
 - You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
-
-## 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:
-      path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
-
-model:
-  width: 300
-  height: 300
-  input_tensor: nhwc
-  input_pixel_format: bgr
-  model_type: ssd
-```
@@ -5,7 +5,7 @@ title: Available Objects

 import labels from "../../../labelmap.txt";

-Frigate includes the object models listed below from the Google Coral test data.
+Frigate includes the object labels listed below from the Google Coral test data.

 Please note:

@@ -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
- iOS: Use the `Add to Homescreen` button in the share menu
+- 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
@@ -52,7 +52,7 @@ detectors:
  # Required: name of the detector
  detector_name:
    # Required: type of the detector
-    # Frigate provided types include 'cpu', 'edgetpu', 'openvino' and 'tensorrt' (default: shown below)
+    # Frigate provides many types, see https://docs.frigate.video/configuration/object_detectors for more details (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.
@@ -117,25 +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:
-  # Optional: path to the model (default: automatic based on detector)
+  # Required: path to the model (default: automatic based on detector)
  path: /edgetpu_model.tflite
-  # Optional: path to the labelmap (default: shown below)
+  # Required: 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
-  # Optional: Object detection model input colorspace
+  # Required: Object detection model input colorspace
  # Valid values are rgb, bgr, or yuv. (default: shown below)
  input_pixel_format: rgb
-  # Optional: Object detection model input tensor format
+  # Required: Object detection model input tensor format
  # Valid values are nhwc or nchw (default: shown below)
  input_tensor: nhwc
-  # Optional: Object detection model type, currently only used with the OpenVINO detector
+  # Required: Object detection model type, currently only used with the OpenVINO detector
  # Valid values are ssd, yolox, yolonas (default: shown below)
  model_type: ssd
-  # Optional: Label name modifications. These are merged into the standard labelmap.
+  # Required: Label name modifications. These are merged into the standard labelmap.
  labelmap:
    2: vehicle
  # Optional: Map of object labels to their attribute labels (default: depends on model)
@@ -546,12 +548,16 @@ genai:

 # 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: jsmpeg stream configuration for WebUI
+# Optional: Live stream configuration for WebUI.
+# NOTE: Can be overridden at the camera level
 live:
-  # Optional: Set the name of the stream that should be used for live view
-  # in frigate WebUI. (default: name of camera)
+  # Optional: Set the name of the stream configured in go2rtc
+  # that should be used for live view in frigate WebUI. (default: name of camera)
+  # NOTE: In most cases this should be set at the camera level only.
  stream_name: camera_name
  # Optional: Set the height of the jsmpeg stream. (default: 720)
  # This must be less than or equal to the height of the detect stream. Lower resolutions
@@ -684,6 +690,7 @@ 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
@@ -692,6 +699,8 @@ 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
@@ -755,6 +764,8 @@ cameras:
        - 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:
@@ -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/v1.9.4) 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.
+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.

 :::note

@@ -132,9 +132,31 @@ 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/v1.9.4#source-exec) source in go2rtc can be used for custom ffmpeg commands. An example is below:
+The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#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}}`

@@ -21,6 +21,21 @@ 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:
@@ -5,7 +5,7 @@ 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 has support for [Jina AI's CLIP model](https://huggingface.co/jinaai/jina-clip-v1) to create embeddings, which runs locally. Embeddings are then saved to Frigate's database.
+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.

@@ -19,7 +19,7 @@ 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 before it can be used. Semantic Search is a global configuration setting.
+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:
@@ -29,9 +29,9 @@ semantic_search:

 :::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. 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 set the config back to `False` before restarting Frigate again.
+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 the Search feature 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.
+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.

 :::

@@ -39,15 +39,9 @@ If you are enabling the Search feature for the first time, be advised that Friga

 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 Search page when clicking on the gray tracked object chip at the top left of each review item. See [the Generative AI docs](/configuration/genai.md) for more information on how to automatically generate tracked object descriptions.
+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 CLIP models are available and can be selected by setting the `model_size` config option:
-
-:::tip
-
-The CLIP models are downloaded in ONNX format, which means they will be accelerated using GPU hardware when available. This depends on the Docker build that is used. See [the object detector docs](../configuration/object_detectors.md) for more information.
-
-:::
+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:
@@ -56,11 +50,41 @@ semantic_search:
 ```

 - 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 model that uses much less RAM and runs faster on CPU with a very negligible difference in embedding quality.
+- 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 Search page. Use a combination of traditional filtering and semantic search for the best results.
+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".
@@ -36,8 +36,8 @@ Note that certbot uses symlinks, and those can't be followed by the container un
 frigate:
  ...
  volumes:
-    - /etc/letsencrypt/live/frigate:/etc/letsencrypt/live/frigate:ro
-    - /etc/letsencrypt/archive/frigate:/etc/letsencrypt/archive/frigate:ro
+    - /etc/letsencrypt/live/your.fqdn.net:/etc/letsencrypt/live/frigate:ro
+    - /etc/letsencrypt/archive/your.fqdn.net:/etc/letsencrypt/archive/your.fqdn.net:ro
  ...

 ```
@@ -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. 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. 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.

 - **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
+- I Frame Interval: 30 (15 can also be used to prioritize streaming performance - see the [camera settings recommendations](../configuration/live) for more info)

 **Sub Stream (Detection)**

@@ -66,4 +66,4 @@ The time period starting when a tracked object entered the frame and ending when

 ## Zone

-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)
+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)
@@ -9,24 +9,36 @@ 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.

-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.
+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.

-Here are some of the camera's I recommend:
+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).

- <a href="https://amzn.to/3uFLtxB" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) T5442TM-AS-LED</a> (affiliate link)
- <a href="https://amzn.to/3isJ3gU" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T5442TM-AS</a> (affiliate link)
- <a href="https://amzn.to/2ZWNWIA" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-28MM</a> (affiliate link)
+Here are some of the cameras 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 EQ12 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.
+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.

-| Name                                                                                                          | Coral Inference Speed | Coral Compatibility | Notes                                                                                                                                   |
-| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
-| Beelink EQ12 (<a href="https://amzn.to/3OlTMJY" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms                | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras.                                               |
-| Intel NUC (<a href="https://amzn.to/3psFlHi" target="_blank" rel="nofollow noopener sponsored">Amazon</a>)    | 5-10ms                | USB                 | Overkill for most, but great performance. Can handle many cameras at 5fps depending on typical amounts of motion. Requires extra parts. |
+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.
+
+:::
+
+| 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. |

 ## Detectors

@@ -52,24 +64,26 @@ 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, GPU, or VPU used, some known examples are below:
+Inference speeds vary greatly depending on the CPU or GPU used, some known examples of GPU inference times are below:

-| 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          |                                                                       |
+| 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                        |

 ### TensorRT - Nvidia GPU

@@ -78,29 +92,35 @@ 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            | 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         |
+| 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                 |                             |

-#### AMD GPUs
+### AMD GPUs

-With the [rocm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many AMD GPUs.
+With the [rocm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many discrete AMD GPUs.

-### Community Supported:
+### Hailo-8l PCIe

-#### Nvidia Jetson
+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

 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:

@@ -112,12 +132,6 @@ Frigate supports hardware video processing on all Rockchip boards. However, hard

 The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms for yolo-nas s.

-#### 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.
-
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)

 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.
@@ -138,4 +152,4 @@ 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://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.
+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.
@@ -81,15 +81,15 @@ You can calculate the **minimum** shm size for each camera with the following fo

 ```console
 # Replace <width> and <height>
-$ python -c 'print("{:.2f}MB".format((<width> * <height> * 1.5 * 10 + 270480) / 1048576))'
+$ python -c 'print("{:.2f}MB".format((<width> * <height> * 1.5 * 20 + 270480) / 1048576))'

-# Example for 1280x720
-$ python -c 'print("{:.2f}MB".format((1280 * 720 * 1.5 * 10 + 270480) / 1048576))'
-13.44MB
+# Example for 1280x720, including logs
+$ python -c 'print("{:.2f}MB".format((1280 * 720 * 1.5 * 20 + 270480) / 1048576)) + 40'
+46.63MB

 # Example for eight cameras detecting at 1280x720, including logs
-$ python -c 'print("{:.2f}MB".format(((1280 * 720 * 1.5 * 10 + 270480) / 1048576) * 8 + 40))'
-136.99MB
+$ python -c 'print("{:.2f}MB".format(((1280 * 720 * 1.5 * 20 + 270480) / 1048576) * 8 + 40))'
+253MB
 ```

 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.
@@ -111,7 +111,7 @@ For Raspberry Pi 5 users with the AI Kit, installation is straightforward. Simpl
 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/41c9b13d2fffce508b32dfc971fa529b49295fbd/docker/hailo8l/user_installation.sh).
+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`

@@ -193,8 +193,9 @@ 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: "64mb" # update for your cameras based on calculation above
+    shm_size: "512mb" # 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
@@ -224,6 +225,7 @@ 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 \
@@ -303,8 +305,15 @@ To install make sure you have the [community app plugin here](https://forums.unr

 ## Proxmox

-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:
+[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.

+:::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`
@@ -0,0 +1,119 @@
+---
+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.
@@ -7,13 +7,21 @@ 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 higher resolutions and frame rates than the jsmpeg stream which is limited to the detect stream
+- 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
 - 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. For the best experience, you should set the stream name under go2rtc to match the name of your camera so that Frigate will automatically map it and be able to use better live view options for the camera. 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.
+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.
+
+:::tip
+
+For the best experience, you should set the stream name under `go2rtc` to match the name of your camera so that Frigate will automatically map it and be able to use better live view options for the camera.
+
+See [the live view docs](../configuration/live.md#setting-stream-for-live-ui) for more information.
+
+:::

 ```yaml
 go2rtc:
@@ -39,8 +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/v1.9.4#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.
+    - 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.
        ```yaml
        go2rtc:
          streams:
@@ -115,6 +115,7 @@ services:
  frigate:
    container_name: frigate
    restart: unless-stopped
+    stop_grace_period: 30s
    image: ghcr.io/blakeblackshear/frigate:stable
    volumes:
      - ./config:/config
@@ -306,7 +307,9 @@ By default, Frigate will retain video of all tracked objects for 10 days. The fu

 ### Step 7: Complete config

-At this point you have a complete config with basic functionality. You can see the [full config reference](../configuration/reference.md) for a complete list of configuration options.
+At this point you have a complete config with basic functionality. 
+- View [common configuration examples](../configuration/index.md#common-configuration-examples) for a list of common configuration examples.
+- View [full config reference](../configuration/reference.md) for a complete list of configuration options.

 ### Follow up

@@ -35,6 +35,7 @@ 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

@@ -117,7 +118,8 @@ server {
  set $port           8971;

  listen 80;
-  listen 443 ssl http2;
+  listen 443 ssl;
+  http2 on;

  server_name frigate.domain.com;
 }
@@ -177,3 +179,33 @@ 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
+}
+
+```
@@ -47,7 +47,7 @@ that card.

 ## Configuration

-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/`.
+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/`.

 ### 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` 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` or `http://172.17.0.1:8971` 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, you would use `http://frigate:5000` when configuring the integration. There is no need to map the port for the Frigate container.
+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.

 ```yaml
 services:
@@ -97,20 +97,29 @@ 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` |
+| 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` |

 ### 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 5000.
+If you run Frigate on a separate device within your local network, Home Assistant will need access to port 8971.

 #### Local network

-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.
+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.
+
+:::

 ```yaml
 services:
@@ -118,7 +127,7 @@ services:
    image: ghcr.io/blakeblackshear/frigate:stable
    ...
    ports:
-      - "5000:5000"
+      - "8971:8971"
      ...
 ```

@@ -195,12 +204,30 @@ To load a snapshot for a tracked object:
 https://HA_URL/api/frigate/notifications/<event-id>/snapshot.jpg
 ```

-To load a video clip of a tracked object:
+To load a video clip of a tracked object using an Android device:

 ```
 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
@@ -282,3 +309,7 @@ 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.
@@ -28,7 +28,14 @@ Message published for each changed tracked object. The first message is publishe
    "id": "1607123955.475377-mxklsc",
    "camera": "front_door",
    "frame_time": 1607123961.837752,
-    "snapshot_time": 1607123961.837752,
+    "snapshot": {
+        "frame_time": 1607123965.975463,
+        "box": [415, 489, 528, 700],
+        "area": 12728,
+        "region": [260, 446, 660, 846],
+        "score": 0.77546,
+        "attributes": [],
+    },
    "label": "person",
    "sub_label": null,
    "top_score": 0.958984375,
@@ -58,7 +65,14 @@ Message published for each changed tracked object. The first message is publishe
    "id": "1607123955.475377-mxklsc",
    "camera": "front_door",
    "frame_time": 1607123962.082975,
-    "snapshot_time": 1607123961.837752,
+    "snapshot": {
+        "frame_time": 1607123965.975463,
+        "box": [415, 489, 528, 700],
+        "area": 12728,
+        "region": [260, 446, 660, 846],
+        "score": 0.77546,
+        "attributes": [],
+    },
    "label": "person",
    "sub_label": ["John Smith", 0.79],
    "top_score": 0.958984375,
@@ -94,6 +108,18 @@ 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.
@@ -29,7 +29,9 @@ 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 using the `Frigate+` button.
+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.

 :::note

@@ -37,13 +39,11 @@ 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 [improving your model](../plus/improving_model.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 [annotating](../plus/annotating.md).

 ![Annotate](/img/annotate.png)

@@ -13,12 +13,20 @@ 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.
@@ -0,0 +1,52 @@
+---
+id: annotating
+title: Annotating your images
+---
+
+For the best results, follow these guidelines. You may also want to review the documentation on [improving your model](./index.md#improving-your-model).
+
+**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.
+
+**Make tight bounding boxes**: Tighter bounding boxes improve the recognition and ensure that accurate bounding boxes are predicted at runtime.
+
+**Label the full object even when occluded**: If you have a person standing behind a car, label the full person even though a portion of their body may be hidden behind the car. This helps predict accurate bounding boxes and improves zone accuracy and filters at runtime. If an object is partly out of frame, label it only when a person would reasonably be able to recognize the object from the visible parts.
+
+**Label objects hard to identify as difficult**: When objects are truly difficult to make out, such as a car barely visible through a bush, or a dog that is hard to distinguish from the background at night, flag it as 'difficult'. This is not used in the model training as of now, but will in the future.
+
+**Delivery logos such as `amazon`, `ups`, and `fedex` should label the logo**: For a Fedex truck, label the truck as a `car` and make a different bounding box just for the Fedex logo. If there are multiple logos, label each of them.
+
+![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 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.
+
+![add image](/img/plus/false-positive-overlap.jpg)
+
+## Shortcuts for a faster workflow
+
+| Shortcut Key      | Description                   |
+| ----------------- | ----------------------------- |
+| `?`               | Show all keyboard shortcuts   |
+| `w`               | Add box                       |
+| `d`               | Toggle difficult              |
+| `s`               | Switch to the next label      |
+| `tab`             | Select next largest box       |
+| `del`             | Delete current box            |
+| `esc`             | Deselect/Cancel               |
+| `← ↑ → ↓`         | Move box                      |
+| `Shift + ← ↑ → ↓` | Resize box                    |
+| `scrollwheel`     | Zoom in/out                   |
+| `f`               | Hide/show all but current box |
+| `spacebar`        | Verify and save               |
@@ -5,15 +5,15 @@ title: Requesting your first model

 ## Step 1: Upload and annotate your images

-Before requesting your first model, you will need to upload at least 10 images to Frigate+. But 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. Refer to the [integration docs](../integrations/plus.md#generate-an-api-key) for instructions on how to easily submit images to Frigate+ directly from Frigate.
+Before requesting your first model, you will need to upload and verify at least 10 images to Frigate+. The more images you upload, annotate, and verify the better your results will be. Most users start to see very good results once they have 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. Refer to the [integration docs](../integrations/plus.md#generate-an-api-key) for instructions on how to easily submit images to Frigate+ directly from Frigate.

 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 [improving your model](./improving_model.md).
+For more detailed recommendations, you can refer to the docs on [annotating](./annotating.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. 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). 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.
 ![Plus Models Page](/img/plus/plus-models.jpg)

 ## Step 3: Set your model id in the config
@@ -1,53 +0,0 @@
---
-id: improving_model
-title: 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. Because a limited number of users submitted images to Frigate+ prior to this launch, you may need to submit several hundred images per camera to see good results. With all the new images now being submitted, future base models will improve as more and more users (including you) submit examples to Frigate+. Note that only verified images 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.
-
-**Make tight bounding boxes**: Tighter bounding boxes improve the recognition and ensure that accurate bounding boxes are predicted at runtime.
-
-**Label the full object even when occluded**: If you have a person standing behind a car, label the full person even though a portion of their body may be hidden behind the car. This helps predict accurate bounding boxes and improves zone accuracy and filters at runtime. If an object is partly out of frame, label it only when a person would reasonably be able to recognize the object from the visible parts.
-
-**Label objects hard to identify as difficult**: When objects are truly difficult to make out, such as a car barely visible through a bush, or a dog that is hard to distinguish from the background at night, flag it as 'difficult'. This is not used in the model training as of now, but will in the future.
-
-**`amazon`, `ups`, and `fedex` should label the logo**: For a Fedex truck, label the truck as a `car` and make a different bounding box just for the Fedex logo. If there are multiple logos, label each of them.
-
-![Fedex Logo](/img/plus/fedex-logo.jpg)
-
-## False positive labels
-
-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.
-
-![add image](/img/plus/false-positive-overlap.jpg)
-
-## Shortcuts for a faster workflow
-
-|Shortcut Key|Description|
-|-----|--------|
-|`?`|Show all keyboard shortcuts|
-|`w`|Add box|
-|`d`|Toggle difficult|
-|`s`|Switch to the next label|
-|`tab`|Select next largest box|
-|`del`|Delete current box|
-|`esc`|Deselect/Cancel|
-|`← ↑ → ↓`|Move box|
-|`Shift + ← ↑ → ↓`|Resize box|
-|`-`|Zoom out|
-|`=`|Zoom in|
-|`f`|Hide/show all but current box|
-|`spacebar`|Verify and save|
@@ -3,37 +3,76 @@ 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 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.
+<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.

-:::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.
+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.

 Information on how to integrate Frigate+ with Frigate can be found in the [integration docs](../integrations/plus.md).

+## Available model types
+
+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.
+
+| Model Type  | Description                                                                                                                                  |
+| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
+| `mobiledet` | Based on the same architecture as the default model included with Frigate. Runs on Google Coral devices and CPUs.                            |
+| `yolonas`   | A newer architecture that offers slightly higher accuracy and improved detection of small objects. Runs on Intel, NVidia GPUs, and AMD GPUs. |
+
 ## Supported detector types

+Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), ONNX (`onnx`), and ROCm (`rocm`) detectors.
+
 :::warning

-Frigate+ models are not supported for TensorRT or OpenVino yet.
+Using Frigate+ models with `onnx` and `rocm` is only available with Frigate 0.15 and later.

 :::

-Currently, Frigate+ models only support CPU (`cpu`) and Coral (`edgetpu`) models. OpenVino is next in line to gain support.
+| 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`              |

-The models are created using the same MobileDet architecture as the default model. Additional architectures will be added in future releases as needed.
+_\* 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. Currently, only the following objects are supported: `person`, `face`, `car`, `license_plate`, `amazon`, `ups`, `fedex`, `package`, `dog`, `cat`, `deer`. Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
+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:
+
+- **People**: `person`, `face`
+- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `license_plate`
+- **Delivery Logos**: `amazon`, `usps`, `ups`, `fedex`, `dhl`, `an_post`, `purolator`, `postnl`, `nzpost`, `postnord`, `gls`, `dpd`
+- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`
+- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`
+
+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`, `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 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.

 In order to have Frigate start using these attribute labels, you will need to add them to the list of objects to track:

@@ -56,6 +95,6 @@ When using Frigate+ models, Frigate will choose the snapshot of a person object

 ![Face Attribute](/img/plus/attribute-example-face.jpg)

-`amazon`, `ups`, and `fedex` labels are used to automatically assign a sub label to car objects.
+Delivery logos such as `amazon`, `ups`, and `fedex` labels are used to automatically assign a sub label to car objects.

 ![Fedex Attribute](/img/plus/attribute-example-fedex.jpg)
@@ -40,6 +40,17 @@ 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:
@@ -49,7 +60,21 @@ The USB Coral can become stuck and need to be restarted, this can happen for a n

 ## PCIe Coral Not Detected

-The most common reason for the PCIe coral not being detected is that the driver has not been installed. See [the coral docs](https://coral.ai/docs/m2/get-started/#2-install-the-pcie-driver-and-edge-tpu-runtime) for how to install the driver for the PCIe based coral.
+The most common reason for the PCIe Coral not being detected is that the driver has not been installed. This process varies based on what OS and kernel that is being run. 
+
+- In most cases [the Coral docs](https://coral.ai/docs/m2/get-started/#2-install-the-pcie-driver-and-edge-tpu-runtime) show how to install the driver for the PCIe based Coral.
+- For Ubuntu 22.04+ https://github.com/jnicolson/gasket-builder can be used to build and install the latest version of the driver.
+
+### Not detected on Raspberry Pi5
+
+A kernel update to the RPi5 means an upate to config.txt is required, see [the raspberry pi forum for more info](https://forums.raspberrypi.com/viewtopic.php?t=363682&sid=cb59b026a412f0dc041595951273a9ca&start=25)
+
+Specifically, add the following to config.txt
+
+```
+dtoverlay=pciex1-compat-pi5,no-mip
+dtoverlay=pcie-32bit-dma-pi5
+```

 ## Only One PCIe Coral Is Detected With Coral Dual EdgeTPU

@@ -17,6 +17,10 @@ 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.

 Ensure your cameras send h264 encoded video, or [transcode them](/configuration/restream.md).
@@ -98,3 +102,11 @@ 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.
@@ -3,7 +3,15 @@ id: recordings
 title: Troubleshooting Recordings
 ---

-### WARNING : Unable to keep up with recording segments in cache for camera. Keeping the 5 most recent segments out of 6 and discarding the rest...
+## I have Frigate configured for motion recording only, but it still seems to be recording even with no motion. Why?
+
+You'll want to:
+
+- Make sure your camera's timestamp is masked out with a motion mask. Even if there is no motion occurring in your scene, your motion settings may be sensitive enough to count your timestamp as motion.
+- If you have audio detection enabled, keep in mind that audio that is heard above `min_volume` is considered motion.
+- [Tune your motion detection settings](/configuration/motion_detection) either by editing your config file or by using the UI's Motion Tuner.
+
+## I see the message: WARNING : Unable to keep up with recording segments in cache for camera. Keeping the 5 most recent segments out of 6 and discarding the rest...

 This error can be caused by a number of different issues. The first step in troubleshooting is to enable debug logging for recording. This will enable logging showing how long it takes for recordings to be moved from RAM cache to the disk.

@@ -40,6 +48,7 @@ On linux, some helpful tools/commands in diagnosing would be:
 On modern linux kernels, the system will utilize some swap if enabled. Setting vm.swappiness=1 no longer means that the kernel will only swap in order to avoid OOM. To prevent any swapping inside a container, set allocations memory and memory+swap to be the same and disable swapping by setting the following docker/podman run parameters:

 **Compose example**
+
 ```yaml
 version: "3.9"
 services:
@@ -54,6 +63,7 @@ services:
 ```

 **Run command example**
+
 ```
 --memory=<MAXRAM> --memory-swap=<MAXSWAP> --memory-swappiness=0
 ```
@@ -1,56 +1,101 @@
-import type * as Preset from '@docusaurus/preset-classic';
-import * as path from 'node:path';
-import type { Config, PluginConfig } from '@docusaurus/types';
-import type * as OpenApiPlugin from 'docusaurus-plugin-openapi-docs';
+import type * as Preset from "@docusaurus/preset-classic";
+import * as path from "node:path";
+import type { Config, PluginConfig } from "@docusaurus/types";
+import type * as OpenApiPlugin from "docusaurus-plugin-openapi-docs";

 const config: Config = {
-  title: 'Frigate',
-  tagline: 'NVR With Realtime Object Detection for IP Cameras',
-  url: 'https://docs.frigate.video',
-  baseUrl: '/',
-  onBrokenLinks: 'throw',
-  onBrokenMarkdownLinks: 'warn',
-  favicon: 'img/favicon.ico',
-  organizationName: 'blakeblackshear',
-  projectName: 'frigate',
-  themes: ['@docusaurus/theme-mermaid', 'docusaurus-theme-openapi-docs'],
+  title: "Frigate",
+  tagline: "NVR With Realtime Object Detection for IP Cameras",
+  url: "https://docs.frigate.video",
+  baseUrl: "/",
+  onBrokenLinks: "throw",
+  onBrokenMarkdownLinks: "warn",
+  favicon: "img/favicon.ico",
+  organizationName: "blakeblackshear",
+  projectName: "frigate",
+  themes: [
+    "@docusaurus/theme-mermaid",
+    "docusaurus-theme-openapi-docs",
+    "@inkeep/docusaurus/chatButton",
+    "@inkeep/docusaurus/searchBar",
+  ],
  markdown: {
    mermaid: true,
  },
  themeConfig: {
-    algolia: {
-      appId: 'WIURGBNBPY',
-      apiKey: 'd02cc0a6a61178b25da550212925226b',
-      indexName: 'frigate',
+    announcementBar: {
+      id: 'frigate_plus',
+      content: `
+        <span style="margin-right: 8px; display: inline-block; animation: pulse 2s infinite;">🚀</span>
+        Get more relevant and accurate detections with Frigate+ models.
+        <a style="margin-left: 12px; padding: 3px 10px; background: #94d2bd; color: #001219; text-decoration: none; border-radius: 4px; font-weight: 500; " target="_blank" rel="noopener noreferrer" href="https://frigate.video/plus/">Learn more</a>
+        <span style="margin-left: 8px; display: inline-block; animation: pulse 2s infinite;">✨</span>
+        <style>
+          @keyframes pulse {
+            0%, 100% { transform: scale(1); }
+            50%       { transform: scale(1.1); }
+          }
+        </style>`,
+      backgroundColor: '#005f73',
+      textColor: '#e0fbfc',
+      isCloseable: false,
    },
    docs: {
      sidebar: {
        hideable: true,
      },
    },
+    inkeepConfig: {
+      baseSettings: {
+        apiKey: "b1a4c4d73c9b48aa5b3cdae6e4c81f0bb3d1134eeb5a7100",
+        integrationId: "cm6xmhn9h000gs601495fkkdx",
+        organizationId: "org_map2JQEOco8U1ZYY",
+        primaryBrandColor: "#010101",
+      },
+      aiChatSettings: {
+        chatSubjectName: "Frigate",
+        botAvatarSrcUrl: "https://frigate.video/images/favicon.png",
+        getHelpCallToActions: [
+          {
+            name: "GitHub",
+            url: "https://github.com/blakeblackshear/frigate",
+            icon: {
+              builtIn: "FaGithub",
+            },
+          },
+        ],
+        quickQuestions: [
+          "How to configure and setup camera settings?",
+          "How to setup notifications?",
+          "Supported builtin detectors?",
+          "How to restream video feed?",
+          "How can I get sound or audio in my recordings?",
+        ],
+      },
+    },
    prism: {
-      additionalLanguages: ['bash', 'json'],
+      additionalLanguages: ["bash", "json"],
    },
    languageTabs: [
      {
-        highlight: 'python',
-        language: 'python',
-        logoClass: 'python',
+        highlight: "python",
+        language: "python",
+        logoClass: "python",
      },
      {
-        highlight: 'javascript',
-        language: 'nodejs',
-        logoClass: 'nodejs',
+        highlight: "javascript",
+        language: "nodejs",
+        logoClass: "nodejs",
      },
      {
-        highlight: 'javascript',
-        language: 'javascript',
-        logoClass: 'javascript',
+        highlight: "javascript",
+        language: "javascript",
+        logoClass: "javascript",
      },
      {
-        highlight: 'bash',
-        language: 'curl',
-        logoClass: 'curl',
+        highlight: "bash",
+        language: "curl",
+        logoClass: "curl",
      },
      {
        highlight: "rust",
@@ -59,49 +104,49 @@ const config: Config = {
      },
    ],
    navbar: {
-      title: 'Frigate',
+      title: "Frigate",
      logo: {
-        alt: 'Frigate',
-        src: 'img/logo.svg',
-        srcDark: 'img/logo-dark.svg',
+        alt: "Frigate",
+        src: "img/logo.svg",
+        srcDark: "img/logo-dark.svg",
      },
      items: [
        {
-          to: '/',
-          activeBasePath: 'docs',
-          label: 'Docs',
-          position: 'left',
+          to: "/",
+          activeBasePath: "docs",
+          label: "Docs",
+          position: "left",
        },
        {
-          href: 'https://frigate.video',
-          label: 'Website',
-          position: 'right',
+          href: "https://frigate.video",
+          label: "Website",
+          position: "right",
        },
        {
-          href: 'http://demo.frigate.video',
-          label: 'Demo',
-          position: 'right',
+          href: "http://demo.frigate.video",
+          label: "Demo",
+          position: "right",
        },
        {
-          href: 'https://github.com/blakeblackshear/frigate',
-          label: 'GitHub',
-          position: 'right',
+          href: "https://github.com/blakeblackshear/frigate",
+          label: "GitHub",
+          position: "right",
        },
      ],
    },
    footer: {
-      style: 'dark',
+      style: "dark",
      links: [
        {
-          title: 'Community',
+          title: "Community",
          items: [
            {
-              label: 'GitHub',
-              href: 'https://github.com/blakeblackshear/frigate',
+              label: "GitHub",
+              href: "https://github.com/blakeblackshear/frigate",
            },
            {
-              label: 'Discussions',
-              href: 'https://github.com/blakeblackshear/frigate/discussions',
+              label: "Discussions",
+              href: "https://github.com/blakeblackshear/frigate/discussions",
            },
          ],
        },
@@ -110,19 +155,19 @@ const config: Config = {
    },
  },
  plugins: [
-    path.resolve(__dirname, 'plugins', 'raw-loader'),
+    path.resolve(__dirname, "plugins", "raw-loader"),
    [
-      'docusaurus-plugin-openapi-docs',
+      "docusaurus-plugin-openapi-docs",
      {
-        id: 'openapi',
-        docsPluginId: 'classic', // configured for preset-classic
+        id: "openapi",
+        docsPluginId: "classic", // configured for preset-classic
        config: {
          frigateApi: {
-            specPath: 'static/frigate-api.yaml',
-            outputDir: 'docs/integrations/api',
+            specPath: "static/frigate-api.yaml",
+            outputDir: "docs/integrations/api",
            sidebarOptions: {
-              groupPathsBy: 'tag',
-              categoryLinkSource: 'tag',
+              groupPathsBy: "tag",
+              categoryLinkSource: "tag",
              sidebarCollapsible: true,
              sidebarCollapsed: true,
            },
@@ -130,23 +175,24 @@ const config: Config = {
          } satisfies OpenApiPlugin.Options,
        },
      },
-    ]
+    ],
  ] as PluginConfig[],
  presets: [
    [
-      'classic',
+      "classic",
      {
        docs: {
-          routeBasePath: '/',
-          sidebarPath: './sidebars.ts',
+          routeBasePath: "/",
+          sidebarPath: "./sidebars.ts",
          // Please change this to your repo.
-          editUrl: 'https://github.com/blakeblackshear/frigate/edit/master/docs/',
+          editUrl:
+            "https://github.com/blakeblackshear/frigate/edit/master/docs/",
          sidebarCollapsible: false,
-          docItemComponent: '@theme/ApiItem', // Derived from docusaurus-theme-openapi
+          docItemComponent: "@theme/ApiItem", // Derived from docusaurus-theme-openapi
        },

        theme: {
-          customCss: './src/css/custom.css',
+          customCss: "./src/css/custom.css",
        },
      } satisfies Preset.Options,
    ],
@@ -17,15 +17,16 @@
    "write-heading-ids": "docusaurus write-heading-ids"
  },
  "dependencies": {
-    "@docusaurus/core": "^3.5.2",
-    "@docusaurus/preset-classic": "^3.5.2",
-    "@docusaurus/theme-mermaid": "^3.5.2",
-    "@docusaurus/plugin-content-docs": "^3.5.2",
-    "@mdx-js/react": "^3.0.1",
+    "@docusaurus/core": "^3.6.3",
+    "@docusaurus/plugin-content-docs": "^3.6.3",
+    "@docusaurus/preset-classic": "^3.6.3",
+    "@docusaurus/theme-mermaid": "^3.6.3",
+    "@inkeep/docusaurus": "^2.0.16",
+    "@mdx-js/react": "^3.1.0",
    "clsx": "^2.1.1",
-    "docusaurus-plugin-openapi-docs": "^4.1.0",
-    "docusaurus-theme-openapi-docs": "^4.1.0",
-    "prism-react-renderer": "^2.4.0",
+    "docusaurus-plugin-openapi-docs": "^4.3.1",
+    "docusaurus-theme-openapi-docs": "^4.3.1",
+    "prism-react-renderer": "^2.4.1",
    "raw-loader": "^4.0.2",
    "react": "^18.3.1",
    "react-dom": "^18.3.1"
@@ -8,6 +8,7 @@ const sidebars: SidebarsConfig = {
      'frigate/index',
      'frigate/hardware',
      'frigate/installation',
+      'frigate/updating',
      'frigate/camera_setup',
      'frigate/video_pipeline',
      'frigate/glossary',
@@ -26,7 +27,7 @@ const sidebars: SidebarsConfig = {
        {
          type: 'link',
          label: 'Go2RTC Configuration Reference',
-          href: 'https://github.com/AlexxIT/go2rtc/tree/v1.9.4#configuration',
+          href: 'https://github.com/AlexxIT/go2rtc/tree/v1.9.2#configuration',
        } as PropSidebarItemLink,
      ],
      Detectors: [
@@ -86,8 +87,8 @@ const sidebars: SidebarsConfig = {
    ],
    'Frigate+': [
      'plus/index',
+      'plus/annotating',
      'plus/first_model',
-      'plus/improving_model',
      'plus/faq',
    ],
    Troubleshooting: [
@@ -17,17 +17,17 @@ from fastapi.responses import JSONResponse, PlainTextResponse
 from markupsafe import escape
 from peewee import operator

-from frigate.api.defs.app_body import AppConfigSetBody
-from frigate.api.defs.app_query_parameters import AppTimelineHourlyQueryParameters
+from frigate.api.defs.query.app_query_parameters import AppTimelineHourlyQueryParameters
+from frigate.api.defs.request.app_body import AppConfigSetBody
 from frigate.api.defs.tags import Tags
 from frigate.config import FrigateConfig
-from frigate.const import CONFIG_DIR
 from frigate.models import Event, Timeline
 from frigate.util.builtin import (
    clean_camera_user_pass,
    get_tz_modifiers,
    update_yaml_from_url,
 )
+from frigate.util.config import find_config_file
 from frigate.util.services import (
    ffprobe_stream,
    get_nvidia_driver_info,
@@ -134,9 +134,28 @@ def config(request: Request):
        for zone_name, zone in config_obj.cameras[camera_name].zones.items():
            camera_dict["zones"][zone_name]["color"] = zone.color

+    # remove go2rtc stream passwords
+    go2rtc: dict[str, any] = config_obj.go2rtc.model_dump(
+        mode="json", warnings="none", exclude_none=True
+    )
+    for stream_name, stream in go2rtc.get("streams", {}).items():
+        if stream is None:
+            continue
+        if isinstance(stream, str):
+            cleaned = clean_camera_user_pass(stream)
+        else:
+            cleaned = []
+
+            for item in stream:
+                cleaned.append(clean_camera_user_pass(item))
+
+        config["go2rtc"]["streams"][stream_name] = cleaned
+
    config["plus"] = {"enabled": request.app.frigate_config.plus_api.is_active()}
    config["model"]["colormap"] = config_obj.model.colormap
+    config["model"]["all_attributes"] = config_obj.model.all_attributes

+    # use merged labelamp
    for detector_config in config["detectors"].values():
        detector_config["model"]["labelmap"] = (
            request.app.frigate_config.model.merged_labelmap
@@ -147,13 +166,7 @@ def config(request: Request):

@router.get("/config/raw")
 def config_raw():
-    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
+    config_file = find_config_file()

    if not os.path.isfile(config_file):
        return JSONResponse(
@@ -198,13 +211,7 @@ def config_save(save_option: str, body: Any = Body(media_type="text/plain")):

    # Save the config to file
    try:
-        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
+        config_file = find_config_file()

        with open(config_file, "w") as f:
            f.write(new_config)
@@ -253,13 +260,7 @@ def config_save(save_option: str, body: Any = Body(media_type="text/plain")):

@router.put("/config/set")
 def config_set(request: Request, body: AppConfigSetBody):
-    config_file = os.environ.get("CONFIG_FILE", f"{CONFIG_DIR}/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
+    config_file = find_config_file()

    with open(config_file, "r") as f:
        old_raw_config = f.read()
@@ -18,7 +18,7 @@ from joserfc import jwt
 from peewee import DoesNotExist
 from slowapi import Limiter

-from frigate.api.defs.app_body import (
+from frigate.api.defs.request.app_body import (
    AppPostLoginBody,
    AppPostUsersBody,
    AppPutPasswordBody,
@@ -85,7 +85,12 @@ def get_remote_addr(request: Request):
            return str(ip)

    # if there wasn't anything in the route, just return the default
-    return request.remote_addr or "127.0.0.1"
+    remote_addr = None
+
+    if hasattr(request, "remote_addr"):
+        remote_addr = request.remote_addr
+
+    return remote_addr or "127.0.0.1"


 def get_jwt_secret() -> str:
@@ -324,7 +329,7 @@ def login(request: Request, body: AppPostLoginBody):
    try:
        db_user: User = User.get_by_id(user)
    except DoesNotExist:
-        return JSONResponse(content={"message": "Login failed"}, status_code=400)
+        return JSONResponse(content={"message": "Login failed"}, status_code=401)

    password_hash = db_user.password_hash
    if verify_password(password, password_hash):
@@ -335,7 +340,7 @@ def login(request: Request, body: AppPostLoginBody):
            response, JWT_COOKIE_NAME, encoded_jwt, expiration, JWT_COOKIE_SECURE
        )
        return response
-    return JSONResponse(content={"message": "Login failed"}, status_code=400)
+    return JSONResponse(content={"message": "Login failed"}, status_code=401)


@router.get("/users")
@@ -28,6 +28,7 @@ class EventsQueryParams(BaseModel):
    is_submitted: Optional[int] = None
    min_length: Optional[float] = None
    max_length: Optional[float] = None
+    event_id: Optional[str] = None
    sort: Optional[str] = None
    timezone: Optional[str] = "utc"

@@ -46,6 +47,7 @@ class EventsSearchQueryParams(BaseModel):
    time_range: Optional[str] = DEFAULT_TIME_RANGE
    has_clip: Optional[bool] = None
    has_snapshot: Optional[bool] = None
+    is_submitted: Optional[bool] = None
    timezone: Optional[str] = "utc"
    min_score: Optional[float] = None
    max_score: Optional[float] = None
@@ -3,7 +3,7 @@ from typing import Union
 from pydantic import BaseModel
 from pydantic.json_schema import SkipJsonSchema

-from frigate.review.maintainer import SeverityEnum
+from frigate.review.types import SeverityEnum


 class ReviewQueryParams(BaseModel):
@@ -1,4 +1,4 @@
-from typing import Optional, Union
+from typing import List, Optional, Union

 from pydantic import BaseModel, Field

@@ -17,14 +17,18 @@ class EventsDescriptionBody(BaseModel):
 class EventsCreateBody(BaseModel):
    source_type: Optional[str] = "api"
    sub_label: Optional[str] = None
-    score: Optional[int] = 0
+    score: Optional[float] = 0
    duration: Optional[int] = 30
    include_recording: Optional[bool] = True
    draw: Optional[dict] = {}


 class EventsEndBody(BaseModel):
-    end_time: Optional[int] = None
+    end_time: Optional[float] = None
+
+
+class EventsDeleteBody(BaseModel):
+    event_ids: List[str] = Field(title="The event IDs to delete")


 class SubmitPlusBody(BaseModel):
@@ -0,0 +1,20 @@
+from typing import Union
+
+from pydantic import BaseModel, Field
+from pydantic.json_schema import SkipJsonSchema
+
+from frigate.record.export import (
+    PlaybackFactorEnum,
+    PlaybackSourceEnum,
+)
+
+
+class ExportRecordingsBody(BaseModel):
+    playback: PlaybackFactorEnum = Field(
+        default=PlaybackFactorEnum.realtime, title="Playback factor"
+    )
+    source: PlaybackSourceEnum = Field(
+        default=PlaybackSourceEnum.recordings, title="Playback source"
+    )
+    name: str = Field(title="Friendly name", default=None, max_length=256)
+    image_path: Union[str, SkipJsonSchema[None]] = None
@@ -0,0 +1,42 @@
+from typing import Any, Optional
+
+from pydantic import BaseModel, ConfigDict
+
+
+class EventResponse(BaseModel):
+    id: str
+    label: str
+    sub_label: Optional[str]
+    camera: str
+    start_time: float
+    end_time: Optional[float]
+    false_positive: Optional[bool]
+    zones: list[str]
+    thumbnail: str
+    has_clip: bool
+    has_snapshot: bool
+    retain_indefinitely: bool
+    plus_id: Optional[str]
+    model_hash: Optional[str]
+    detector_type: Optional[str]
+    model_type: Optional[str]
+    data: dict[str, Any]
+
+    model_config = ConfigDict(protected_namespaces=())
+
+
+class EventCreateResponse(BaseModel):
+    success: bool
+    message: str
+    event_id: str
+
+
+class EventMultiDeleteResponse(BaseModel):
+    success: bool
+    deleted_events: list[str]
+    not_found_events: list[str]
+
+
+class EventUploadPlusResponse(BaseModel):
+    success: bool
+    plus_id: str
@@ -3,7 +3,7 @@ from typing import Dict

 from pydantic import BaseModel, Json

-from frigate.review.maintainer import SeverityEnum
+from frigate.review.types import SeverityEnum


 class ReviewSegmentResponse(BaseModel):
@@ -14,29 +14,36 @@ from fastapi.responses import JSONResponse
 from peewee import JOIN, DoesNotExist, fn, operator
 from playhouse.shortcuts import model_to_dict

-from frigate.api.defs.events_body import (
-    EventsCreateBody,
-    EventsDescriptionBody,
-    EventsEndBody,
-    EventsSubLabelBody,
-    SubmitPlusBody,
-)
-from frigate.api.defs.events_query_parameters import (
+from frigate.api.defs.query.events_query_parameters import (
    DEFAULT_TIME_RANGE,
    EventsQueryParams,
    EventsSearchQueryParams,
    EventsSummaryQueryParams,
 )
-from frigate.api.defs.regenerate_query_parameters import (
+from frigate.api.defs.query.regenerate_query_parameters import (
    RegenerateQueryParameters,
 )
-from frigate.api.defs.tags import Tags
-from frigate.const import (
-    CLIPS_DIR,
+from frigate.api.defs.request.events_body import (
+    EventsCreateBody,
+    EventsDeleteBody,
+    EventsDescriptionBody,
+    EventsEndBody,
+    EventsSubLabelBody,
+    SubmitPlusBody,
 )
+from frigate.api.defs.response.event_response import (
+    EventCreateResponse,
+    EventMultiDeleteResponse,
+    EventResponse,
+    EventUploadPlusResponse,
+)
+from frigate.api.defs.response.generic_response import GenericResponse
+from frigate.api.defs.tags import Tags
+from frigate.const import CLIPS_DIR
 from frigate.embeddings import EmbeddingsContext
+from frigate.events.external import ExternalEventProcessor
 from frigate.models import Event, ReviewSegment, Timeline
-from frigate.object_processing import TrackedObject
+from frigate.object_processing import TrackedObject, TrackedObjectProcessor
 from frigate.util.builtin import get_tz_modifiers

 logger = logging.getLogger(__name__)
@@ -44,7 +51,7 @@ logger = logging.getLogger(__name__)
 router = APIRouter(tags=[Tags.events])


-@router.get("/events")
+@router.get("/events", response_model=list[EventResponse])
 def events(params: EventsQueryParams = Depends()):
    camera = params.camera
    cameras = params.cameras
@@ -88,6 +95,7 @@ def events(params: EventsQueryParams = Depends()):
    is_submitted = params.is_submitted
    min_length = params.min_length
    max_length = params.max_length
+    event_id = params.event_id

    sort = params.sort

@@ -230,6 +238,9 @@ def events(params: EventsQueryParams = Depends()):
        elif is_submitted > 0:
            clauses.append((Event.plus_id != ""))

+    if event_id is not None:
+        clauses.append((Event.id == event_id))
+
    if len(clauses) == 0:
        clauses.append((True))

@@ -242,6 +253,8 @@ def events(params: EventsQueryParams = Depends()):
            order_by = Event.start_time.asc()
        elif sort == "date_desc":
            order_by = Event.start_time.desc()
+        else:
+            order_by = Event.start_time.desc()
    else:
        order_by = Event.start_time.desc()

@@ -257,7 +270,7 @@ def events(params: EventsQueryParams = Depends()):
    return JSONResponse(content=list(events))


-@router.get("/events/explore")
+@router.get("/events/explore", response_model=list[EventResponse])
 def events_explore(limit: int = 10):
    # get distinct labels for all events
    distinct_labels = Event.select(Event.label).distinct().order_by(Event.label)
@@ -302,7 +315,8 @@ def events_explore(limit: int = 10):
                "data": {
                    k: v
                    for k, v in event.data.items()
-                    if k in ["type", "score", "top_score", "description"]
+                    if k
+                    in ["type", "score", "top_score", "description", "sub_label_score"]
                },
                "event_count": label_counts[event.label],
            }
@@ -318,7 +332,7 @@ def events_explore(limit: int = 10):
    return JSONResponse(content=processed_events)


-@router.get("/event_ids")
+@router.get("/event_ids", response_model=list[EventResponse])
 def event_ids(ids: str):
    ids = ids.split(",")

@@ -356,6 +370,7 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
    time_range = params.time_range
    has_clip = params.has_clip
    has_snapshot = params.has_snapshot
+    is_submitted = params.is_submitted

    # for similarity search
    event_id = params.event_id
@@ -437,6 +452,12 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
    if has_snapshot is not None:
        event_filters.append((Event.has_snapshot == has_snapshot))

+    if is_submitted is not None:
+        if is_submitted == 0:
+            event_filters.append((Event.plus_id.is_null()))
+        elif is_submitted > 0:
+            event_filters.append((Event.plus_id != ""))
+
    if min_score is not None and max_score is not None:
        event_filters.append((Event.data["score"].between(min_score, max_score)))
    else:
@@ -569,19 +590,17 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())

        processed_events.append(processed_event)

-    # Sort by search distance if search_results are available, otherwise by start_time as default
-    if search_results:
+    if (sort is None or sort == "relevance") and search_results:
        processed_events.sort(key=lambda x: x.get("search_distance", float("inf")))
+    elif min_score is not None and max_score is not None and sort == "score_asc":
+        processed_events.sort(key=lambda x: x["score"])
+    elif min_score is not None and max_score is not None and sort == "score_desc":
+        processed_events.sort(key=lambda x: x["score"], reverse=True)
+    elif sort == "date_asc":
+        processed_events.sort(key=lambda x: x["start_time"])
    else:
-        if sort == "score_asc":
-            processed_events.sort(key=lambda x: x["score"])
-        elif sort == "score_desc":
-            processed_events.sort(key=lambda x: x["score"], reverse=True)
-        elif sort == "date_asc":
-            processed_events.sort(key=lambda x: x["start_time"])
-        else:
-            # "date_desc" default
-            processed_events.sort(key=lambda x: x["start_time"], reverse=True)
+        # "date_desc" default
+        processed_events.sort(key=lambda x: x["start_time"], reverse=True)

    # Limit the number of events returned
    processed_events = processed_events[:limit]
@@ -634,7 +653,7 @@ def events_summary(params: EventsSummaryQueryParams = Depends()):
    return JSONResponse(content=[e for e in groups.dicts()])


-@router.get("/events/{event_id}")
+@router.get("/events/{event_id}", response_model=EventResponse)
 def event(event_id: str):
    try:
        return model_to_dict(Event.get(Event.id == event_id))
@@ -642,7 +661,7 @@ def event(event_id: str):
        return JSONResponse(content="Event not found", status_code=404)


-@router.post("/events/{event_id}/retain")
+@router.post("/events/{event_id}/retain", response_model=GenericResponse)
 def set_retain(event_id: str):
    try:
        event = Event.get(Event.id == event_id)
@@ -661,7 +680,7 @@ def set_retain(event_id: str):
    )


-@router.post("/events/{event_id}/plus")
+@router.post("/events/{event_id}/plus", response_model=EventUploadPlusResponse)
 def send_to_plus(request: Request, event_id: str, body: SubmitPlusBody = None):
    if not request.app.frigate_config.plus_api.is_active():
        message = "PLUS_API_KEY environment variable is not set"
@@ -773,7 +792,7 @@ def send_to_plus(request: Request, event_id: str, body: SubmitPlusBody = None):
    )


-@router.put("/events/{event_id}/false_positive")
+@router.put("/events/{event_id}/false_positive", response_model=EventUploadPlusResponse)
 def false_positive(request: Request, event_id: str):
    if not request.app.frigate_config.plus_api.is_active():
        message = "PLUS_API_KEY environment variable is not set"
@@ -862,7 +881,7 @@ def false_positive(request: Request, event_id: str):
    )


-@router.delete("/events/{event_id}/retain")
+@router.delete("/events/{event_id}/retain", response_model=GenericResponse)
 def delete_retain(event_id: str):
    try:
        event = Event.get(Event.id == event_id)
@@ -881,7 +900,7 @@ def delete_retain(event_id: str):
    )


-@router.post("/events/{event_id}/sub_label")
+@router.post("/events/{event_id}/sub_label", response_model=GenericResponse)
 def set_sub_label(
    request: Request,
    event_id: str,
@@ -933,7 +952,7 @@ def set_sub_label(
    )


-@router.post("/events/{event_id}/description")
+@router.post("/events/{event_id}/description", response_model=GenericResponse)
 def set_description(
    request: Request,
    event_id: str,
@@ -980,7 +999,7 @@ def set_description(
    )


-@router.put("/events/{event_id}/description/regenerate")
+@router.put("/events/{event_id}/description/regenerate", response_model=GenericResponse)
 def regenerate_description(
    request: Request, event_id: str, params: RegenerateQueryParameters = Depends()
 ):
@@ -992,9 +1011,11 @@ def regenerate_description(
            status_code=404,
        )

+    camera_config = request.app.frigate_config.cameras[event.camera]
+
    if (
        request.app.frigate_config.semantic_search.enabled
-        and request.app.frigate_config.genai.enabled
+        and camera_config.genai.enabled
    ):
        request.app.event_metadata_updater.publish((event.id, params.source))

@@ -1022,40 +1043,67 @@ def regenerate_description(
    )


-@router.delete("/events/{event_id}")
-def delete_event(request: Request, event_id: str):
+def delete_single_event(event_id: str, request: Request) -> dict:
    try:
        event = Event.get(Event.id == event_id)
    except DoesNotExist:
-        return JSONResponse(
-            content=({"success": False, "message": "Event " + event_id + " not found"}),
-            status_code=404,
-        )
+        return {"success": False, "message": f"Event {event_id} not found"}

    media_name = f"{event.camera}-{event.id}"
    if event.has_snapshot:
-        media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.jpg")
-        media.unlink(missing_ok=True)
-        media = Path(f"{os.path.join(CLIPS_DIR, media_name)}-clean.png")
-        media.unlink(missing_ok=True)
-    if event.has_clip:
-        media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.mp4")
-        media.unlink(missing_ok=True)
+        snapshot_paths = [
+            Path(f"{os.path.join(CLIPS_DIR, media_name)}.jpg"),
+            Path(f"{os.path.join(CLIPS_DIR, media_name)}-clean.png"),
+        ]
+        for media in snapshot_paths:
+            media.unlink(missing_ok=True)

    event.delete_instance()
    Timeline.delete().where(Timeline.source_id == event_id).execute()
+
    # If semantic search is enabled, update the index
    if request.app.frigate_config.semantic_search.enabled:
        context: EmbeddingsContext = request.app.embeddings
        context.db.delete_embeddings_thumbnail(event_ids=[event_id])
        context.db.delete_embeddings_description(event_ids=[event_id])
-    return JSONResponse(
-        content=({"success": True, "message": "Event " + event_id + " deleted"}),
-        status_code=200,
-    )
+
+    return {"success": True, "message": f"Event {event_id} deleted"}


-@router.post("/events/{camera_name}/{label}/create")
+@router.delete("/events/{event_id}", response_model=GenericResponse)
+def delete_event(request: Request, event_id: str):
+    result = delete_single_event(event_id, request)
+    status_code = 200 if result["success"] else 404
+    return JSONResponse(content=result, status_code=status_code)
+
+
+@router.delete("/events/", response_model=EventMultiDeleteResponse)
+def delete_events(request: Request, body: EventsDeleteBody):
+    if not body.event_ids:
+        return JSONResponse(
+            content=({"success": False, "message": "No event IDs provided."}),
+            status_code=404,
+        )
+
+    deleted_events = []
+    not_found_events = []
+
+    for event_id in body.event_ids:
+        result = delete_single_event(event_id, request)
+        if result["success"]:
+            deleted_events.append(event_id)
+        else:
+            not_found_events.append(event_id)
+
+    response = {
+        "success": True,
+        "deleted_events": deleted_events,
+        "not_found_events": not_found_events,
+    }
+    return JSONResponse(content=response, status_code=200)
+
+
+@router.post("/events/{camera_name}/{label}/create", response_model=EventCreateResponse)
 def create_event(
    request: Request,
    camera_name: str,
@@ -1077,9 +1125,11 @@ def create_event(
        )

    try:
-        frame = request.app.detected_frames_processor.get_current_frame(camera_name)
+        frame_processor: TrackedObjectProcessor = request.app.detected_frames_processor
+        external_processor: ExternalEventProcessor = request.app.external_processor

-        event_id = request.app.external_processor.create_manual_event(
+        frame = frame_processor.get_current_frame(camera_name)
+        event_id = external_processor.create_manual_event(
            camera_name,
            label,
            body.source_type,
@@ -1109,7 +1159,7 @@ def create_event(
    )


-@router.put("/events/{event_id}/end")
+@router.put("/events/{event_id}/end", response_model=GenericResponse)
 def end_event(request: Request, event_id: str, body: EventsEndBody):
    try:
        end_time = body.end_time or datetime.datetime.now().timestamp()
@@ -4,13 +4,14 @@ import logging
 import random
 import string
 from pathlib import Path
-from typing import Optional

 import psutil
 from fastapi import APIRouter, Request
 from fastapi.responses import JSONResponse
 from peewee import DoesNotExist
+from playhouse.shortcuts import model_to_dict

+from frigate.api.defs.request.export_recordings_body import ExportRecordingsBody
 from frigate.api.defs.tags import Tags
 from frigate.const import EXPORT_DIR
 from frigate.models import Export, Previews, Recordings
@@ -19,6 +20,7 @@ from frigate.record.export import (
    PlaybackSourceEnum,
    RecordingExporter,
 )
+from frigate.util.builtin import is_current_hour

 logger = logging.getLogger(__name__)

@@ -37,7 +39,7 @@ def export_recording(
    camera_name: str,
    start_time: float,
    end_time: float,
-    body: dict = None,
+    body: ExportRecordingsBody,
 ):
    if not camera_name or not request.app.frigate_config.cameras.get(camera_name):
        return JSONResponse(
@@ -47,18 +49,10 @@ def export_recording(
            status_code=404,
        )

-    json: dict[str, any] = body or {}
-    playback_factor = json.get("playback", "realtime")
-    playback_source = json.get("source", "recordings")
-    friendly_name: Optional[str] = json.get("name")
-
-    if len(friendly_name or "") > 256:
-        return JSONResponse(
-            content=({"success": False, "message": "File name is too long."}),
-            status_code=401,
-        )
-
-    existing_image = json.get("image_path")
+    playback_factor = body.playback
+    playback_source = body.source
+    friendly_name = body.name
+    existing_image = body.image_path

    if playback_source == "recordings":
        recordings_count = (
@@ -94,7 +88,7 @@ def export_recording(
            .count()
        )

-        if previews_count <= 0:
+        if not is_current_hour(start_time) and previews_count <= 0:
            return JSONResponse(
                content=(
                    {"success": False, "message": "No previews found for time range"}
@@ -214,3 +208,14 @@ def export_delete(event_id: str):
        ),
        status_code=200,
    )
+
+
+@router.get("/exports/{export_id}")
+def get_export(export_id: str):
+    try:
+        return JSONResponse(content=model_to_dict(Export.get(Export.id == export_id)))
+    except DoesNotExist:
+        return JSONResponse(
+            content={"success": False, "message": "Export not found"},
+            status_code=404,
+        )
@@ -26,14 +26,13 @@ from frigate.storage import StorageMaintainer
 logger = logging.getLogger(__name__)


-def check_csrf(request: Request):
+def check_csrf(request: Request) -> bool:
    if request.method in ["GET", "HEAD", "OPTIONS", "TRACE"]:
-        pass
+        return True
    if "origin" in request.headers and "x-csrf-token" not in request.headers:
-        return JSONResponse(
-            content={"success": False, "message": "Missing CSRF header"},
-            status_code=401,
-        )
+        return False
+
+    return True


 # Used to retrieve the remote-user header: https://starlette-context.readthedocs.io/en/latest/plugins.html#easy-mode
@@ -71,7 +70,12 @@ def create_fastapi_app(
    @app.middleware("http")
    async def frigate_middleware(request: Request, call_next):
        # Before request
-        check_csrf(request)
+        if not check_csrf(request):
+            return JSONResponse(
+                content={"success": False, "message": "Missing CSRF header"},
+                status_code=401,
+            )
+
        if database.is_closed():
            database.connect()

@@ -87,7 +91,11 @@ def create_fastapi_app(
        logger.info("FastAPI started")

    # Rate limiter (used for login endpoint)
-    auth.rateLimiter.set_limit(frigate_config.auth.failed_login_rate_limit or "")
+    if frigate_config.auth.failed_login_rate_limit is None:
+        limiter.enabled = False
+    else:
+        auth.rateLimiter.set_limit(frigate_config.auth.failed_login_rate_limit)
+
    app.state.limiter = limiter
    app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)
    app.add_middleware(SlowAPIMiddleware)
@@ -20,7 +20,7 @@ from pathvalidate import sanitize_filename
 from peewee import DoesNotExist, fn
 from tzlocal import get_localzone_name

-from frigate.api.defs.media_query_parameters import (
+from frigate.api.defs.query.media_query_parameters import (
    Extension,
    MediaEventsSnapshotQueryParams,
    MediaLatestFrameQueryParams,
@@ -36,6 +36,7 @@ from frigate.const import (
    RECORD_DIR,
 )
 from frigate.models import Event, Previews, Recordings, Regions, ReviewSegment
+from frigate.object_processing import TrackedObjectProcessor
 from frigate.util.builtin import get_tz_modifiers
 from frigate.util.image import get_image_from_recording

@@ -79,7 +80,11 @@ def mjpeg_feed(


 def imagestream(
-    detected_frames_processor, camera_name: str, fps: int, height: int, draw_options
+    detected_frames_processor: TrackedObjectProcessor,
+    camera_name: str,
+    fps: int,
+    height: int,
+    draw_options: dict[str, any],
 ):
    while True:
        # max out at specified FPS
@@ -118,6 +123,7 @@ def latest_frame(
    extension: Extension,
    params: MediaLatestFrameQueryParams = Depends(),
 ):
+    frame_processor: TrackedObjectProcessor = request.app.detected_frames_processor
    draw_options = {
        "bounding_boxes": params.bbox,
        "timestamp": params.timestamp,
@@ -127,19 +133,25 @@ def latest_frame(
        "regions": params.regions,
    }
    quality = params.quality
+    mime_type = extension
+
+    if extension == "png":
+        quality_params = None
+    elif extension == "webp":
+        quality_params = [int(cv2.IMWRITE_WEBP_QUALITY), quality]
+    else:
+        quality_params = [int(cv2.IMWRITE_JPEG_QUALITY), quality]
+        mime_type = "jpeg"

    if camera_name in request.app.frigate_config.cameras:
-        frame = request.app.detected_frames_processor.get_current_frame(
-            camera_name, draw_options
-        )
+        frame = frame_processor.get_current_frame(camera_name, draw_options)
        retry_interval = float(
            request.app.frigate_config.cameras.get(camera_name).ffmpeg.retry_interval
            or 10
        )

        if frame is None or datetime.now().timestamp() > (
-            request.app.detected_frames_processor.get_current_frame_time(camera_name)
-            + retry_interval
+            frame_processor.get_current_frame_time(camera_name) + retry_interval
        ):
            if request.app.camera_error_image is None:
                error_image = glob.glob("/opt/frigate/frigate/images/camera-error.jpg")
@@ -170,17 +182,15 @@ def latest_frame(

        frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)

-        ret, img = cv2.imencode(
-            f".{extension}", frame, [int(cv2.IMWRITE_WEBP_QUALITY), quality]
-        )
+        ret, img = cv2.imencode(f".{extension}", frame, quality_params)
        return Response(
            content=img.tobytes(),
-            media_type=f"image/{extension}",
-            headers={"Content-Type": f"image/{extension}", "Cache-Control": "no-store"},
+            media_type=f"image/{mime_type}",
+            headers={"Content-Type": f"image/{mime_type}", "Cache-Control": "no-store"},
        )
    elif camera_name == "birdseye" and request.app.frigate_config.birdseye.restream:
        frame = cv2.cvtColor(
-            request.app.detected_frames_processor.get_current_frame(camera_name),
+            frame_processor.get_current_frame(camera_name),
            cv2.COLOR_YUV2BGR_I420,
        )

@@ -189,13 +199,11 @@ def latest_frame(

        frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)

-        ret, img = cv2.imencode(
-            f".{extension}", frame, [int(cv2.IMWRITE_WEBP_QUALITY), quality]
-        )
+        ret, img = cv2.imencode(f".{extension}", frame, quality_params)
        return Response(
            content=img.tobytes(),
-            media_type=f"image/{extension}",
-            headers={"Content-Type": f"image/{extension}", "Cache-Control": "no-store"},
+            media_type=f"image/{mime_type}",
+            headers={"Content-Type": f"image/{mime_type}", "Cache-Control": "no-store"},
        )
    else:
        return JSONResponse(
@@ -238,6 +246,7 @@ def get_snapshot_from_recording(
        recording: Recordings = recording_query.get()
        time_in_segment = frame_time - recording.start_time
        codec = "png" if format == "png" else "mjpeg"
+        mime_type = "png" if format == "png" else "jpeg"
        config: FrigateConfig = request.app.frigate_config

        image_data = get_image_from_recording(
@@ -254,7 +263,7 @@ def get_snapshot_from_recording(
                ),
                status_code=404,
            )
-        return Response(image_data, headers={"Content-Type": f"image/{format}"})
+        return Response(image_data, headers={"Content-Type": f"image/{mime_type}"})
    except DoesNotExist:
        return JSONResponse(
            content={
@@ -813,15 +822,15 @@ def grid_snapshot(
 ):
    if camera_name in request.app.frigate_config.cameras:
        detect = request.app.frigate_config.cameras[camera_name].detect
-        frame = request.app.detected_frames_processor.get_current_frame(camera_name, {})
+        frame_processor: TrackedObjectProcessor = request.app.detected_frames_processor
+        frame = frame_processor.get_current_frame(camera_name, {})
        retry_interval = float(
            request.app.frigate_config.cameras.get(camera_name).ffmpeg.retry_interval
            or 10
        )

        if frame is None or datetime.now().timestamp() > (
-            request.app.detected_frames_processor.get_current_frame_time(camera_name)
-            + retry_interval
+            frame_processor.get_current_frame_time(camera_name) + retry_interval
        ):
            return JSONResponse(
                content={"success": False, "message": "Unable to get valid frame"},
@@ -917,7 +926,7 @@ def grid_snapshot(
        ret, jpg = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])

        return Response(
-            jpg.tobytes,
+            jpg.tobytes(),
            media_type="image/jpeg",
            headers={"Cache-Control": "no-store"},
        )
@@ -1453,7 +1462,6 @@ def preview_thumbnail(file_name: str):

    return Response(
        jpg_bytes,
-        # FIXME: Shouldn't it be either jpg or webp depending on the endpoint?
        media_type="image/webp",
        headers={
            "Content-Type": "image/webp",
@@ -1482,7 +1490,7 @@ def label_thumbnail(request: Request, camera_name: str, label: str):
        ret, jpg = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])

        return Response(
-            jpg.tobytes,
+            jpg.tobytes(),
            media_type="image/jpeg",
            headers={"Cache-Control": "no-store"},
        )
@@ -1535,6 +1543,6 @@ def label_snapshot(request: Request, camera_name: str, label: str):
        _, jpg = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])

        return Response(
-            jpg.tobytes,
+            jpg.tobytes(),
            media_type="image/jpeg",
        )
@@ -12,20 +12,21 @@ from fastapi.responses import JSONResponse
 from peewee import Case, DoesNotExist, fn, operator
 from playhouse.shortcuts import model_to_dict

-from frigate.api.defs.generic_response import GenericResponse
-from frigate.api.defs.review_body import ReviewModifyMultipleBody
-from frigate.api.defs.review_query_parameters import (
+from frigate.api.defs.query.review_query_parameters import (
    ReviewActivityMotionQueryParams,
    ReviewQueryParams,
    ReviewSummaryQueryParams,
 )
-from frigate.api.defs.review_responses import (
+from frigate.api.defs.request.review_body import ReviewModifyMultipleBody
+from frigate.api.defs.response.generic_response import GenericResponse
+from frigate.api.defs.response.review_response import (
    ReviewActivityMotionResponse,
    ReviewSegmentResponse,
    ReviewSummaryResponse,
 )
 from frigate.api.defs.tags import Tags
 from frigate.models import Recordings, ReviewSegment
+from frigate.review.types import SeverityEnum
 from frigate.util.builtin import get_tz_modifiers

 logger = logging.getLogger(__name__)
@@ -161,7 +162,7 @@ def review_summary(params: ReviewSummaryQueryParams = Depends()):
                    None,
                    [
                        (
-                            (ReviewSegment.severity == "alert"),
+                            (ReviewSegment.severity == SeverityEnum.alert),
                            ReviewSegment.has_been_reviewed,
                        )
                    ],
@@ -173,7 +174,7 @@ def review_summary(params: ReviewSummaryQueryParams = Depends()):
                    None,
                    [
                        (
-                            (ReviewSegment.severity == "detection"),
+                            (ReviewSegment.severity == SeverityEnum.detection),
                            ReviewSegment.has_been_reviewed,
                        )
                    ],
@@ -185,7 +186,7 @@ def review_summary(params: ReviewSummaryQueryParams = Depends()):
                    None,
                    [
                        (
-                            (ReviewSegment.severity == "alert"),
+                            (ReviewSegment.severity == SeverityEnum.alert),
                            1,
                        )
                    ],
@@ -197,7 +198,7 @@ def review_summary(params: ReviewSummaryQueryParams = Depends()):
                    None,
                    [
                        (
-                            (ReviewSegment.severity == "detection"),
+                            (ReviewSegment.severity == SeverityEnum.detection),
                            1,
                        )
                    ],
@@ -230,6 +231,7 @@ def review_summary(params: ReviewSummaryQueryParams = Depends()):
        label_clause = reduce(operator.or_, label_clauses)
        clauses.append((label_clause))

+    day_in_seconds = 60 * 60 * 24
    last_month = (
        ReviewSegment.select(
            fn.strftime(
@@ -246,7 +248,7 @@ def review_summary(params: ReviewSummaryQueryParams = Depends()):
                    None,
                    [
                        (
-                            (ReviewSegment.severity == "alert"),
+                            (ReviewSegment.severity == SeverityEnum.alert),
                            ReviewSegment.has_been_reviewed,
                        )
                    ],
@@ -258,7 +260,7 @@ def review_summary(params: ReviewSummaryQueryParams = Depends()):
                    None,
                    [
                        (
-                            (ReviewSegment.severity == "detection"),
+                            (ReviewSegment.severity == SeverityEnum.detection),
                            ReviewSegment.has_been_reviewed,
                        )
                    ],
@@ -270,7 +272,7 @@ def review_summary(params: ReviewSummaryQueryParams = Depends()):
                    None,
                    [
                        (
-                            (ReviewSegment.severity == "alert"),
+                            (ReviewSegment.severity == SeverityEnum.alert),
                            1,
                        )
                    ],
@@ -282,7 +284,7 @@ def review_summary(params: ReviewSummaryQueryParams = Depends()):
                    None,
                    [
                        (
-                            (ReviewSegment.severity == "detection"),
+                            (ReviewSegment.severity == SeverityEnum.detection),
                            1,
                        )
                    ],
@@ -292,7 +294,7 @@ def review_summary(params: ReviewSummaryQueryParams = Depends()):
        )
        .where(reduce(operator.and_, clauses))
        .group_by(
-            (ReviewSegment.start_time + seconds_offset).cast("int") / (3600 * 24),
+            (ReviewSegment.start_time + seconds_offset).cast("int") / day_in_seconds,
        )
        .order_by(ReviewSegment.start_time.desc())
    )
@@ -362,7 +364,7 @@ def delete_reviews(body: ReviewModifyMultipleBody):
    ReviewSegment.delete().where(ReviewSegment.id << list_of_ids).execute()

    return JSONResponse(
-        content=({"success": True, "message": "Delete reviews"}), status_code=200
+        content=({"success": True, "message": "Deleted review items."}), status_code=200
    )


@@ -488,8 +490,6 @@ def set_not_reviewed(review_id: str):
    review.save()

    return JSONResponse(
-        content=(
-            {"success": True, "message": "Set Review " + review_id + " as not viewed"}
-        ),
+        content=({"success": True, "message": f"Set Review {review_id} as not viewed"}),
        status_code=200,
    )
@@ -36,6 +36,7 @@ from frigate.const import (
    EXPORT_DIR,
    MODEL_CACHE_DIR,
    RECORD_DIR,
+    SHM_FRAMES_VAR,
 )
 from frigate.db.sqlitevecq import SqliteVecQueueDatabase
 from frigate.embeddings import EmbeddingsContext, manage_embeddings
@@ -63,13 +64,14 @@ from frigate.record.cleanup import RecordingCleanup
 from frigate.record.export import migrate_exports
 from frigate.record.record import manage_recordings
 from frigate.review.review import manage_review_segments
-from frigate.service_manager import ServiceManager
 from frigate.stats.emitter import StatsEmitter
 from frigate.stats.util import stats_init
 from frigate.storage import StorageMaintainer
 from frigate.timeline import TimelineProcessor
 from frigate.util.builtin import empty_and_close_queue
+from frigate.util.image import SharedMemoryFrameManager, UntrackedSharedMemory
 from frigate.util.object import get_camera_regions_grid
+from frigate.util.services import set_file_limit
 from frigate.version import VERSION
 from frigate.video import capture_camera, track_camera
 from frigate.watchdog import FrigateWatchdog
@@ -79,6 +81,7 @@ logger = logging.getLogger(__name__)

 class FrigateApp:
    def __init__(self, config: FrigateConfig) -> None:
+        self.audio_process: Optional[mp.Process] = None
        self.stop_event: MpEvent = mp.Event()
        self.detection_queue: Queue = mp.Queue()
        self.detectors: dict[str, ObjectDetectProcess] = {}
@@ -90,6 +93,7 @@ class FrigateApp:
        self.processes: dict[str, int] = {}
        self.embeddings: Optional[EmbeddingsContext] = None
        self.region_grids: dict[str, list[list[dict[str, int]]]] = {}
+        self.frame_manager = SharedMemoryFrameManager()
        self.config = config

    def ensure_dirs(self) -> None:
@@ -325,20 +329,20 @@ class FrigateApp:
                        for det in self.config.detectors.values()
                    ]
                )
-                shm_in = mp.shared_memory.SharedMemory(
+                shm_in = UntrackedSharedMemory(
                    name=name,
                    create=True,
                    size=largest_frame,
                )
            except FileExistsError:
-                shm_in = mp.shared_memory.SharedMemory(name=name)
+                shm_in = UntrackedSharedMemory(name=name)

            try:
-                shm_out = mp.shared_memory.SharedMemory(
+                shm_out = UntrackedSharedMemory(
                    name=f"out-{name}", create=True, size=20 * 6 * 4
                )
            except FileExistsError:
-                shm_out = mp.shared_memory.SharedMemory(name=f"out-{name}")
+                shm_out = UntrackedSharedMemory(name=f"out-{name}")

            self.detection_shms.append(shm_in)
            self.detection_shms.append(shm_out)
@@ -431,6 +435,11 @@ class FrigateApp:
                logger.info(f"Capture process not started for disabled camera {name}")
                continue

+            # pre-create shms
+            for i in range(shm_frame_count):
+                frame_size = config.frame_shape_yuv[0] * config.frame_shape_yuv[1]
+                self.frame_manager.create(f"{config.name}_frame{i}", frame_size)
+
            capture_process = util.Process(
                target=capture_camera,
                name=f"camera_capture:{name}",
@@ -449,8 +458,9 @@ class FrigateApp:
        ]

        if audio_cameras:
-            proc = AudioProcessor(audio_cameras, self.camera_metrics).start(wait=True)
-            self.processes["audio_detector"] = proc.pid or 0
+            self.audio_process = AudioProcessor(audio_cameras, self.camera_metrics)
+            self.audio_process.start()
+            self.processes["audio_detector"] = self.audio_process.pid or 0

    def start_timeline_processor(self) -> None:
        self.timeline_processor = TimelineProcessor(
@@ -512,15 +522,21 @@ class FrigateApp:
                    1,
                )

-        shm_frame_count = min(50, int(available_shm / (cam_total_frame_size)))
+        if cam_total_frame_size == 0.0:
+            return 0
+
+        shm_frame_count = min(
+            int(os.environ.get(SHM_FRAMES_VAR, "50")),
+            int(available_shm / (cam_total_frame_size)),
+        )

        logger.debug(
            f"Calculated total camera size {available_shm} / {cam_total_frame_size} :: {shm_frame_count} frames for each camera in SHM"
        )

-        if shm_frame_count < 10:
+        if shm_frame_count < 20:
            logger.warning(
-                f"The current SHM size of {total_shm}MB is too small, recommend increasing it to at least {round(min_req_shm + cam_total_frame_size * 10)}MB."
+                f"The current SHM size of {total_shm}MB is too small, recommend increasing it to at least {round(min_req_shm + cam_total_frame_size * 20)}MB."
            )

        return shm_frame_count
@@ -572,6 +588,9 @@ class FrigateApp:
        # Ensure global state.
        self.ensure_dirs()

+        # Set soft file limits.
+        set_file_limit()
+
        # Start frigate services.
        self.init_camera_metrics()
        self.init_queues()
@@ -638,6 +657,11 @@ class FrigateApp:
            ReviewSegment.end_time == None
        ).execute()

+        # stop the audio process
+        if self.audio_process:
+            self.audio_process.terminate()
+            self.audio_process.join()
+
        # ensure the capture processes are done
        for camera, metrics in self.camera_metrics.items():
            capture_process = metrics.capture_process
@@ -701,11 +725,10 @@ class FrigateApp:
        self.event_metadata_updater.stop()
        self.inter_zmq_proxy.stop()

+        self.frame_manager.cleanup()
        while len(self.detection_shms) > 0:
            shm = self.detection_shms.pop()
            shm.close()
            shm.unlink()

-        ServiceManager.current().shutdown(wait=True)
-
        os._exit(os.EX_OK)
@@ -22,7 +22,7 @@ from frigate.const import (
 )
 from frigate.models import Event, Previews, Recordings, ReviewSegment
 from frigate.ptz.onvif import OnvifCommandEnum, OnvifController
-from frigate.types import ModelStatusTypesEnum
+from frigate.types import ModelStatusTypesEnum, TrackedObjectUpdateTypesEnum
 from frigate.util.object import get_camera_regions_grid
 from frigate.util.services import restart_frigate

@@ -137,8 +137,14 @@ class Dispatcher:
            event.data["description"] = payload["description"]
            event.save()
            self.publish(
-                "event_update",
-                json.dumps({"id": event.id, "description": event.data["description"]}),
+                "tracked_object_update",
+                json.dumps(
+                    {
+                        "type": TrackedObjectUpdateTypesEnum.description,
+                        "id": event.id,
+                        "description": event.data["description"],
+                    }
+                ),
            )

        def handle_update_model_state():
@@ -14,7 +14,7 @@ class EventUpdatePublisher(Publisher):
        super().__init__("update")

    def publish(
-        self, payload: tuple[EventTypeEnum, EventStateEnum, str, dict[str, any]]
+        self, payload: tuple[EventTypeEnum, EventStateEnum, str, str, dict[str, any]]
    ) -> None:
        super().publish(payload)

@@ -133,7 +133,7 @@ class MqttClient(Communicator):  # type: ignore[misc]
        """Mqtt connection callback."""
        threading.current_thread().name = "mqtt"
        if reason_code != 0:
-            if reason_code == "Server Unavailable":
+            if reason_code == "Server unavailable":
                logger.error(
                    "Unable to connect to MQTT server: MQTT Server unavailable"
                )
--- a/Show More
+++ b/Show More