Merge acb17a7b50 into 28b0ad782a

* remove extra gap controller overrides

* new vod endpoint for clips to set discontinuity

ensure tracking-detail playlists emit #EXT-X-DISCONTINUITY (avoids fMP4 timestamp rewrites and playback stalls) while leaving standard recordings behavior unchanged

* use new endpoint

.github/workflows/ci.yml

@@ -224,3 +224,29 @@ jobs:
           sources: |
             ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}:${{ env.SHORT_SHA }}-amd64
             ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}:${{ env.SHORT_SHA }}-rpi
+  axera_build:
+    runs-on: ubuntu-22.04
+    name: AXERA Build
+    needs:
+      - amd64_build
+      - arm64_build
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v5
+        with:
+          persist-credentials: false
+      - name: Set up QEMU and Buildx
+        id: setup
+        uses: ./.github/actions/setup
+        with:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Build and push Axera build
+        uses: docker/bake-action@v6
+        with:
+          source: .
+          push: true
+          targets: axcl
+          files: docker/axcl/axcl.hcl
+          set: |
+            axcl.tags=${{ steps.setup.outputs.image-name }}-axcl
+            *.cache-from=type=gha

docker/axcl/Dockerfile

@@ -0,0 +1,55 @@
+# syntax=docker/dockerfile:1.6
+
+# https://askubuntu.com/questions/972516/debian-frontend-environment-variable
+ARG DEBIAN_FRONTEND=noninteractive
+
+# Globally set pip break-system-packages option to avoid having to specify it every time
+ARG PIP_BREAK_SYSTEM_PACKAGES=1
+
+
+FROM frigate AS frigate-axcl
+ARG TARGETARCH
+ARG PIP_BREAK_SYSTEM_PACKAGES
+
+# Install axpyengine
+RUN wget https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3.rc1/axengine-0.1.3-py3-none-any.whl -O /axengine-0.1.3-py3-none-any.whl
+RUN pip3 install -i https://mirrors.aliyun.com/pypi/simple/ /axengine-0.1.3-py3-none-any.whl \
+    && rm /axengine-0.1.3-py3-none-any.whl
+
+# Install axcl
+RUN if [ "$TARGETARCH" = "amd64" ]; then \
+        echo "Installing x86_64 version of axcl"; \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/axcl_host_x86_64_V3.6.5_20250908154509_NO4973.deb -O /axcl.deb; \
+    else \
+        echo "Installing aarch64 version of axcl"; \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/axcl_host_aarch64_V3.6.5_20250908154509_NO4973.deb -O /axcl.deb; \
+    fi
+
+RUN mkdir /unpack_axcl && \
+    dpkg-deb -x /axcl.deb /unpack_axcl && \
+    cp -R /unpack_axcl/usr/bin/axcl /usr/bin/ && \
+    cp -R /unpack_axcl/usr/lib/axcl /usr/lib/ && \
+    rm -rf /unpack_axcl /axcl.deb
+
+
+# Install axcl ffmpeg
+RUN mkdir -p /usr/lib/ffmpeg/axcl
+
+RUN if [ "$TARGETARCH" = "amd64" ]; then \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/ffmpeg-x64 -O /usr/lib/ffmpeg/axcl/ffmpeg && \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/ffprobe-x64 -O /usr/lib/ffmpeg/axcl/ffprobe; \
+    else \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/ffmpeg-aarch64 -O /usr/lib/ffmpeg/axcl/ffmpeg && \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/ffprobe-aarch64 -O /usr/lib/ffmpeg/axcl/ffprobe; \
+    fi
+
+RUN chmod +x /usr/lib/ffmpeg/axcl/ffmpeg /usr/lib/ffmpeg/axcl/ffprobe
+
+# Set ldconfig path
+RUN echo "/usr/lib/axcl" > /etc/ld.so.conf.d/ax.conf
+
+# Set env
+ENV PATH="$PATH:/usr/bin/axcl"
+ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/lib/axcl"
+
+ENTRYPOINT ["sh", "-c", "ldconfig && exec /init"]

docker/axcl/axcl.hcl

@@ -0,0 +1,13 @@
+target frigate {
+  dockerfile = "docker/main/Dockerfile"
+  platforms = ["linux/amd64", "linux/arm64"]
+  target = "frigate"
+}
+
+target axcl {
+  dockerfile = "docker/axcl/Dockerfile"
+  contexts = {
+    frigate = "target:frigate",
+  }
+  platforms = ["linux/amd64", "linux/arm64"]
+}

docker/axcl/axcl.mk

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

docker/axcl/user_installation.sh

@@ -0,0 +1,83 @@
+#!/bin/bash
+
+# Update package list and install dependencies
+sudo apt-get update
+sudo apt-get install -y build-essential cmake git wget pciutils kmod udev
+
+# Check if gcc-12 is needed
+current_gcc_version=$(gcc --version | head -n1 | awk '{print $NF}')
+gcc_major_version=$(echo $current_gcc_version | cut -d'.' -f1)
+
+if [[ $gcc_major_version -lt 12 ]]; then
+    echo "Current GCC version ($current_gcc_version) is lower than 12, installing gcc-12..."
+    sudo apt-get install -y gcc-12
+    sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 12
+    echo "GCC-12 installed and set as default"
+else
+    echo "Current GCC version ($current_gcc_version) is sufficient, skipping GCC installation"
+fi
+
+# Determine architecture
+arch=$(uname -m)
+download_url=""
+
+if [[ $arch == "x86_64" ]]; then
+    download_url="https://github.com/ivanshi1108/assets/releases/download/v0.16.2/axcl_host_x86_64_V3.6.5_20250908154509_NO4973.deb"
+    deb_file="axcl_host_x86_64_V3.6.5_20250908154509_NO4973.deb"
+elif [[ $arch == "aarch64" ]]; then
+    download_url="https://github.com/ivanshi1108/assets/releases/download/v0.16.2/axcl_host_aarch64_V3.6.5_20250908154509_NO4973.deb"
+    deb_file="axcl_host_aarch64_V3.6.5_20250908154509_NO4973.deb"
+else
+    echo "Unsupported architecture: $arch"
+    exit 1
+fi
+
+# Download AXCL driver
+echo "Downloading AXCL driver for $arch..."
+wget "$download_url" -O "$deb_file"
+
+if [ $? -ne 0 ]; then
+    echo "Failed to download AXCL driver"
+    exit 1
+fi
+
+# Install AXCL driver
+echo "Installing AXCL driver..."
+sudo dpkg -i "$deb_file"
+
+if [ $? -ne 0 ]; then
+    echo "Failed to install AXCL driver, attempting to fix dependencies..."
+    sudo apt-get install -f -y
+    sudo dpkg -i "$deb_file"
+
+    if [ $? -ne 0 ]; then
+        echo "AXCL driver installation failed"
+        exit 1
+    fi
+fi
+
+# Update environment
+echo "Updating environment..."
+source /etc/profile
+
+# Verify installation
+echo "Verifying AXCL installation..."
+if command -v axcl-smi &> /dev/null; then
+    echo "AXCL driver detected, checking AI accelerator status..."
+
+    axcl_output=$(axcl-smi 2>&1)
+    axcl_exit_code=$?
+
+    echo "$axcl_output"
+
+    if [ $axcl_exit_code -eq 0 ]; then
+        echo "AXCL driver installation completed successfully!"
+    else
+        echo "AXCL driver installed but no AI accelerator detected or communication failed."
+        echo "Please check if the AI accelerator is properly connected and powered on."
+        exit 1
+    fi
+else
+    echo "axcl-smi command not found. AXCL driver installation may have failed."
+    exit 1
+fi

docs/docs/configuration/object_detectors.md

@@ -49,6 +49,11 @@ Frigate supports multiple different detectors that work on different types of ha
 
 - [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs.
 
+**AXERA** <CommunityBadge />
+
+- [AXEngine](#axera): axmodels can run on AXERA AI acceleration.
+
+
 **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.
@@ -1476,6 +1481,41 @@ model:
   input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
 ```
 
+## AXERA
+
+Hardware accelerated object detection is supported on the following SoCs:
+
+- AX650N
+- AX8850N
+
+This implementation uses the [AXera Pulsar2 Toolchain](https://huggingface.co/AXERA-TECH/Pulsar2).
+
+See the [installation docs](../frigate/installation.md#axera) for information on configuring the AXEngine hardware.
+
+### Configuration
+
+When configuring the AXEngine detector, you have to specify the model name.
+
+#### yolov9
+
+A yolov9 model is provided in the container at /axmodels and is used by this detector type by default.
+
+Use the model configuration shown below when using the axengine detector with the default axmodel:
+
+```yaml
+detectors:
+  axengine:
+    type: axengine
+
+model:
+  path: frigate-yolov9-tiny
+  model_type: yolo-generic
+  width: 320
+  height: 320
+  tensor_format: bgr
+  labelmap_path: /labelmap/coco-80.txt
+```
+
 # Models
 
 Some model types are not included in Frigate by default.

docs/docs/frigate/hardware.md

@@ -104,6 +104,10 @@ Frigate supports multiple different detectors that work on different types of ha
 
 - [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs to provide efficient object detection.
 
+**AXERA** <CommunityBadge />
+
+- [AXEngine](#axera): axera models can run on AXERA NPUs via AXEngine, delivering highly efficient object detection.
+
 :::
 
 ### Hailo-8
@@ -287,6 +291,14 @@ The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms fo
 | ssd mobilenet | ~ 25 ms                         |
 | yolov5m       | ~ 118 ms                        |
 
+### AXERA
+
+- **AXEngine** Default model is **yolov9**
+
+| Name             | AXERA AX650N/AX8850N Inference Time |
+| ---------------- | ----------------------------------- |
+| yolov9-tiny      | ~ 4 ms                              |
+
 ## 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.

docs/docs/frigate/installation.md

@@ -287,6 +287,42 @@ or add these options to your `docker run` command:
 
 Next, you should configure [hardware object detection](/configuration/object_detectors#synaptics) and [hardware video processing](/configuration/hardware_acceleration_video#synaptics).
 
+### AXERA
+
+<details>
+<summary>AXERA accelerators</summary>
+AXERA accelerators are available in an M.2 form factor, compatible with both Raspberry Pi and Orange Pi. This form factor has also been successfully tested on x86 platforms, making it a versatile choice for various computing environments.
+
+#### Installation
+
+Using AXERA accelerators requires the installation of the AXCL driver. We provide a convenient Linux script to complete this installation.
+
+Follow these steps for installation:
+
+1. Copy or download [this script](https://github.com/ivanshi1108/assets/releases/download/v0.16.2/user_installation.sh).
+2. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
+3. Run the script with `./user_installation.sh`
+
+#### Setup
+
+To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
+
+Next, grant Docker permissions to access your hardware by adding the following lines to your `docker-compose.yml` file:
+
+```yaml
+devices:
+  - /dev/axcl_host
+  - /dev/ax_mmb_dev
+  - /dev/msg_userdev
+```
+
+If you are using `docker run`, add this option to your command `--device /dev/axcl_host --device /dev/ax_mmb_dev --device /dev/msg_userdev`
+
+#### Configuration
+
+Finally, configure [hardware object detection](/configuration/object_detectors#axera) to complete the setup.
+</details>
+
 ## Docker
 
 Running through Docker with Docker Compose is the recommended install method.

frigate/api/media.py

@@ -837,7 +837,19 @@ async def recording_clip(
     dependencies=[Depends(require_camera_access)],
     description="Returns an HLS playlist for the specified timestamp-range on the specified camera. Append /master.m3u8 or /index.m3u8 for HLS playback.",
 )
-async def vod_ts(camera_name: str, start_ts: float, end_ts: float):
+async def vod_ts(
+    camera_name: str,
+    start_ts: float,
+    end_ts: float,
+    force_discontinuity: bool = False,
+):
+    logger.debug(
+        "VOD: Generating VOD for %s from %s to %s with force_discontinuity=%s",
+        camera_name,
+        start_ts,
+        end_ts,
+        force_discontinuity,
+    )
     recordings = (
         Recordings.select(
             Recordings.path,
@@ -862,6 +874,14 @@ async def vod_ts(camera_name: str, start_ts: float, end_ts: float):
 
     recording: Recordings
     for recording in recordings:
+        logger.debug(
+            "VOD: processing recording: %s start=%s end=%s duration=%s",
+            recording.path,
+            recording.start_time,
+            recording.end_time,
+            recording.duration,
+        )
+
         clip = {"type": "source", "path": recording.path}
         duration = int(recording.duration * 1000)
 
@@ -870,6 +890,11 @@ async def vod_ts(camera_name: str, start_ts: float, end_ts: float):
             inpoint = int((start_ts - recording.start_time) * 1000)
             clip["clipFrom"] = inpoint
             duration -= inpoint
+            logger.debug(
+                "VOD: applied clipFrom %sms to %s",
+                inpoint,
+                recording.path,
+            )
 
         # adjust end if recording.end_time is after end_ts
         if recording.end_time > end_ts:
@@ -877,12 +902,23 @@ async def vod_ts(camera_name: str, start_ts: float, end_ts: float):
 
         if duration < min_duration_ms:
             # skip if the clip has no valid duration (too short to contain frames)
+            logger.debug(
+                "VOD: skipping recording %s - resulting duration %sms too short",
+                recording.path,
+                duration,
+            )
             continue
 
         if min_duration_ms <= duration < max_duration_ms:
             clip["keyFrameDurations"] = [duration]
             clips.append(clip)
             durations.append(duration)
+            logger.debug(
+                "VOD: added clip %s duration_ms=%s clipFrom=%s",
+                recording.path,
+                duration,
+                clip.get("clipFrom"),
+            )
         else:
             logger.warning(f"Recording clip is missing or empty: {recording.path}")
 
@@ -902,7 +938,7 @@ async def vod_ts(camera_name: str, start_ts: float, end_ts: float):
     return JSONResponse(
         content={
             "cache": hour_ago.timestamp() > start_ts,
-            "discontinuity": False,
+            "discontinuity": force_discontinuity,
             "consistentSequenceMediaInfo": True,
             "durations": durations,
             "segment_duration": max(durations),
@@ -986,6 +1022,19 @@ async def vod_event(
     return vod_response
 
 
+@router.get(
+    "/vod/clip/{camera_name}/start/{start_ts}/end/{end_ts}",
+    dependencies=[Depends(require_camera_access)],
+    description="Returns an HLS playlist for a timestamp range with HLS discontinuity enabled. Append /master.m3u8 or /index.m3u8 for HLS playback.",
+)
+async def vod_clip(
+    camera_name: str,
+    start_ts: float,
+    end_ts: float,
+):
+    return await vod_ts(camera_name, start_ts, end_ts, force_discontinuity=True)
+
+
 @router.get(
     "/events/{event_id}/snapshot.jpg",
     description="Returns a snapshot image for the specified object id. NOTE: The query params only take affect while the event is in-progress. Once the event has ended the snapshot configuration is used.",

frigate/detectors/plugins/axengine.py

@@ -0,0 +1,86 @@
+import logging
+import os.path
+import re
+import urllib.request
+from typing import Literal
+
+import axengine as axe
+
+from frigate.const import MODEL_CACHE_DIR
+from frigate.detectors.detection_api import DetectionApi
+from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
+from frigate.util.model import post_process_yolo
+
+logger = logging.getLogger(__name__)
+
+DETECTOR_KEY = "axengine"
+
+supported_models = {
+    ModelTypeEnum.yologeneric: "frigate-yolov9-.*$",
+}
+
+model_cache_dir = os.path.join(MODEL_CACHE_DIR, "axengine_cache/")
+
+
+class AxengineDetectorConfig(BaseDetectorConfig):
+    type: Literal[DETECTOR_KEY]
+
+
+class Axengine(DetectionApi):
+    type_key = DETECTOR_KEY
+
+    def __init__(self, config: AxengineDetectorConfig):
+        logger.info("__init__ axengine")
+        super().__init__(config)
+        self.height = config.model.height
+        self.width = config.model.width
+        model_path = config.model.path or "frigate-yolov9-tiny"
+        model_props = self.parse_model_input(model_path)
+        self.session = axe.InferenceSession(model_props["path"])
+
+    def __del__(self):
+        pass
+
+    def parse_model_input(self, model_path):
+        model_props = {}
+        model_props["preset"] = True
+
+        model_matched = False
+
+        for model_type, pattern in supported_models.items():
+            if re.match(pattern, model_path):
+                model_matched = True
+                model_props["model_type"] = model_type
+
+        if model_matched:
+            model_props["filename"] = model_path + ".axmodel"
+            model_props["path"] = model_cache_dir + model_props["filename"]
+
+            if not os.path.isfile(model_props["path"]):
+                self.download_model(model_props["filename"])
+        else:
+            supported_models_str = ", ".join(model[1:-1] for model in supported_models)
+            raise Exception(
+                f"Model {model_path} is unsupported. Provide your own model or choose one of the following: {supported_models_str}"
+            )
+        return model_props
+
+    def download_model(self, filename):
+        if not os.path.isdir(model_cache_dir):
+            os.mkdir(model_cache_dir)
+
+        GITHUB_ENDPOINT = os.environ.get("GITHUB_ENDPOINT", "https://github.com")
+        urllib.request.urlretrieve(
+            f"{GITHUB_ENDPOINT}/ivanshi1108/assets/releases/download/v0.16.2/{filename}",
+            model_cache_dir + filename,
+        )
+
+    def detect_raw(self, tensor_input):
+        results = None
+        results = self.session.run(None, {"images": tensor_input})
+        if self.detector_config.model.model_type == ModelTypeEnum.yologeneric:
+            return post_process_yolo(results, self.width, self.height)
+        else:
+            raise ValueError(
+                f'Model type "{self.detector_config.model.model_type}" is currently not supported.'
+            )

web/public/locales/en/views/classificationModel.json

@@ -83,6 +83,7 @@
     "aria": "Select Recent Classifications"
   },
   "categories": "Classes",
+  "none": "None",
   "createCategory": {
     "new": "Create New Class"
   },

web/src/components/overlay/ClassificationSelectionDialog.tsx

@@ -131,7 +131,9 @@ export default function ClassificationSelectionDialog({
                   className="flex cursor-pointer gap-2 smart-capitalize"
                   onClick={() => onCategorizeImage(category)}
                 >
-                  {category.replaceAll("_", " ")}
+                  {category === "none"
+                    ? t("none")
+                    : category.replaceAll("_", " ")}
                 </SelectorItem>
               ))}
               <Separator />

web/src/components/overlay/detail/TrackingDetails.tsx

@@ -446,7 +446,7 @@ export function TrackingDetails({
       (event.end_time ?? Date.now() / 1000) + annotationOffset / 1000;
     const startTime = eventStartRecord - REVIEW_PADDING;
     const endTime = eventEndRecord + REVIEW_PADDING;
-    const playlist = `${baseUrl}vod/${event.camera}/start/${startTime}/end/${endTime}/index.m3u8`;
+    const playlist = `${baseUrl}vod/clip/${event.camera}/start/${startTime}/end/${endTime}/index.m3u8`;
 
     return {
       playlist,
@@ -559,7 +559,6 @@ export function TrackingDetails({
                 isDetailMode={true}
                 camera={event.camera}
                 currentTimeOverride={currentTime}
-                enableGapControllerRecovery={true}
               />
               {isVideoLoading && (
                 <ActivityIndicator className="absolute left-1/2 top-1/2 -translate-x-1/2 -translate-y-1/2" />

web/src/components/overlay/dialog/TrainFilterDialog.tsx

@@ -180,7 +180,9 @@ export function ClassFilterContent({
               {allClasses.map((item) => (
                 <FilterSwitch
                   key={item}
-                  label={item.replaceAll("_", " ")}
+                  label={
+                    item === "none" ? t("none") : item.replaceAll("_", " ")
+                  }
                   isChecked={classes?.includes(item) ?? false}
                   onCheckedChange={(isChecked) => {
                     if (isChecked) {

web/src/components/player/HlsVideoPlayer.tsx

@@ -57,7 +57,6 @@ type HlsVideoPlayerProps = {
   isDetailMode?: boolean;
   camera?: string;
   currentTimeOverride?: number;
-  enableGapControllerRecovery?: boolean;
 };
 
 export default function HlsVideoPlayer({
@@ -82,7 +81,6 @@ export default function HlsVideoPlayer({
   isDetailMode = false,
   camera,
   currentTimeOverride,
-  enableGapControllerRecovery = false,
 }: HlsVideoPlayerProps) {
   const { t } = useTranslation("components/player");
   const { data: config } = useSWR<FrigateConfig>("config");
@@ -173,21 +171,12 @@ export default function HlsVideoPlayer({
     }
 
     // Base HLS configuration
-    const baseConfig: Partial<HlsConfig> = {
+    const hlsConfig: Partial<HlsConfig> = {
       maxBufferLength: 10,
       maxBufferSize: 20 * 1000 * 1000,
       startPosition: currentSource.startPosition,
     };
 
-    const hlsConfig = { ...baseConfig };
-
-    if (enableGapControllerRecovery) {
-      hlsConfig.highBufferWatchdogPeriod = 1; // Check for stalls every 1 second (default: 3)
-      hlsConfig.nudgeOffset = 0.2; // Nudge playhead forward 0.2s when stalled (default: 0.1)
-      hlsConfig.nudgeMaxRetry = 5; // Try up to 5 nudges before giving up (default: 3)
-      hlsConfig.maxBufferHole = 0.5; // Tolerate up to 0.5s gaps between fragments (default: 0.1)
-    }
-
     hlsRef.current = new Hls(hlsConfig);
     hlsRef.current.attachMedia(videoRef.current);
     hlsRef.current.loadSource(currentSource.playlist);
@@ -201,13 +190,7 @@ export default function HlsVideoPlayer({
         hlsRef.current.destroy();
       }
     };
-  }, [
-    videoRef,
-    hlsRef,
-    useHlsCompat,
-    currentSource,
-    enableGapControllerRecovery,
-  ]);
+  }, [videoRef, hlsRef, useHlsCompat, currentSource]);
 
   // state handling
 

web/src/pages/FaceLibrary.tsx

@@ -371,7 +371,12 @@ export default function FaceLibrary() {
         {selectedFaces?.length > 0 ? (
           <div className="flex items-center justify-center gap-2">
             <div className="mx-1 flex w-48 items-center justify-center text-sm text-muted-foreground">
-              <div className="p-1">{`${selectedFaces.length} selected`}</div>
+              <div className="p-1">
+                {t("selected", {
+                  ns: "views/event",
+                  count: selectedFaces.length,
+                })}
+              </div>
               <div className="p-1">{"|"}</div>
               <div
                 className="cursor-pointer p-2 text-primary hover:rounded-lg hover:bg-secondary"

web/src/views/classification/ModelTrainingView.tsx

@@ -422,7 +422,12 @@ export default function ModelTrainingView({ model }: ModelTrainingViewProps) {
             )}
           >
             <div className="flex w-48 items-center justify-center text-sm text-muted-foreground">
-              <div className="p-1">{`${selectedImages.length} selected`}</div>
+              <div className="p-1">
+                {t("selected", {
+                  ns: "views/event",
+                  count: selectedImages.length,
+                })}
+              </div>
               <div className="p-1">{"|"}</div>
               <div
                 className="cursor-pointer p-2 text-primary hover:rounded-lg hover:bg-secondary"
@@ -682,7 +687,7 @@ function LibrarySelector({
                 className="flex-grow cursor-pointer capitalize"
                 onClick={() => setPageToggle(id)}
               >
-                {id.replaceAll("_", " ")}
+                {id === "none" ? t("none") : id.replaceAll("_", " ")}
                 <span className="ml-2 text-muted-foreground">
                   ({dataset?.[id].length})
                 </span>