Makefile

@@ -1,7 +1,7 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.16.2
+VERSION = 0.16.1
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty

docs/docs/configuration/object_detectors.md

@@ -1049,10 +1049,10 @@ python3 yolo_to_onnx.py -m yolov7-320
 
 #### YOLOv9
 
-YOLOv9 model can be exported as ONNX using the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=t` and `IMG_SIZE=320` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available model sizes are `t`, `s`, `m`, `c`, and `e`, common image sizes are `320` and `640`).
+YOLOv9 model can be exported as ONNX using the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=t` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available sizes are `t`, `s`, `m`, `c`, and `e`).
 
 ```sh
-docker build . --build-arg MODEL_SIZE=t --build-arg IMG_SIZE=320 --output . -f- <<'EOF'
+docker build . --build-arg MODEL_SIZE=t --output . -f- <<'EOF'
 FROM python:3.11 AS build
 RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
 COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
@@ -1061,13 +1061,11 @@ ADD https://github.com/WongKinYiu/yolov9.git .
 RUN uv pip install --system -r requirements.txt
 RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier>=0.4.1
 ARG MODEL_SIZE
-ARG IMG_SIZE
 ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt
 RUN sed -i "s/ckpt = torch.load(attempt_download(w), map_location='cpu')/ckpt = torch.load(attempt_download(w), map_location='cpu', weights_only=False)/g" models/experimental.py
-RUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz ${IMG_SIZE} --simplify --include onnx
+RUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz 320 --simplify --include onnx
 FROM scratch
 ARG MODEL_SIZE
-ARG IMG_SIZE
+COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /
-COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /yolov9-${MODEL_SIZE}-${IMG_SIZE}.onnx
 EOF
 ```

docs/docs/frigate/hardware.md

@@ -138,11 +138,11 @@ Inference speeds vary greatly depending on the CPU or GPU used, some known examp
 | Intel HD 620   | 15 - 25 ms                 |                                                   | 320: ~ 35 ms              |                        |                                    |
 | Intel HD 630   | ~ 15 ms                    |                                                   | 320: ~ 30 ms              |                        |                                    |
 | Intel UHD 730  | ~ 10 ms                    |                                                   | 320: ~ 19 ms 640: ~ 54 ms |                        |                                    |
-| Intel UHD 770  | ~ 15 ms                    | t-320: ~ 16 ms s-320: ~ 20 ms s-640: ~ 40 ms      | 320: ~ 20 ms 640: ~ 46 ms |                        |                                    |
+| Intel UHD 770  | ~ 15 ms                    | t-320: 24 ms s-320: 30 ms s-640: 45 ms            | 320: ~ 20 ms 640: ~ 46 ms |                        |                                    |
 | Intel N100     | ~ 15 ms                    | s-320: 30 ms                                      | 320: ~ 25 ms              |                        | Can only run one detector instance |
-| Intel N150     | ~ 15 ms                    | t-320: 16 ms s-320: 24 ms                         |                           |                        |                                    |
+| Intel N150     | ~ 15 ms                    | t-320: 16ms s-320: 24 ms                          |                           |                        |                                    |
 | Intel Iris XE  | ~ 10 ms                    | s-320: 12 ms s-640: 30 ms                         | 320: ~ 18 ms 640: ~ 50 ms |                        |                                    |
-| Intel Arc A310 | ~ 5  ms                    | t-320: 7 ms t-640: 11 ms s-320: 8 ms s-640: 15 ms | 320: ~ 8 ms 640: ~ 14 ms  |                        |                                    |
+| Intel Arc A310 |                            | t-320: 7 ms t-640: 11 ms s-320: 8 ms s-640: 15 ms |                           |                        |                                    |
 | Intel Arc A380 | ~ 6 ms                     |                                                   | 320: ~ 10 ms 640: ~ 22 ms | 336: 20 ms 448: 27 ms  |                                    |
 | Intel Arc A750 | ~ 4 ms                     |                                                   | 320: ~ 8 ms               |                        |                                    |
 

frigate/detectors/detector_config.py

@@ -161,10 +161,6 @@ class ModelConfig(BaseModel):
         if model_info.get("inputDataType"):
             self.input_dtype = model_info["inputDataType"]
 
-        # RKNN always uses NHWC
-        if detector == "rknn":
-            self.input_tensor = InputTensorEnum.nhwc
-
         # generate list of attribute labels
         self.attributes_map = {
             **model_info.get("attributes", DEFAULT_ATTRIBUTE_LABEL_MAP),

frigate/util/services.py

@@ -301,7 +301,7 @@ def get_intel_gpu_stats(intel_gpu_device: Optional[str]) -> Optional[dict[str, s
         "-o",
         "-",
         "-s",
-        "1000",  # Intel changed this from seconds to milliseconds in 2024+ versions
+        "1",
     ]
 
     if intel_gpu_device:

web/src/hooks/use-camera-live-mode.ts

@@ -33,43 +33,29 @@ export default function useCameraLiveMode(
 
   const streamsFetcher = useCallback(async (key: string) => {
     const streamNames = key.split(",");
-
-    const metadataPromises = streamNames.map(async (streamName) => {
-      try {
-        const response = await fetch(`/api/go2rtc/streams/${streamName}`, {
-          priority: "low",
-        });
-
-        if (response.ok) {
-          const data = await response.json();
-          return { streamName, data };
-        }
-        return { streamName, data: null };
-      } catch (error) {
-        // eslint-disable-next-line no-console
-        console.error(`Failed to fetch metadata for ${streamName}:`, error);
-        return { streamName, data: null };
-      }
-    });
-
-    const results = await Promise.allSettled(metadataPromises);
-
     const metadata: { [key: string]: LiveStreamMetadata } = {};
-    results.forEach((result) => {
+
-      if (result.status === "fulfilled" && result.value.data) {
+    await Promise.all(
-        metadata[result.value.streamName] = result.value.data;
+      streamNames.map(async (streamName) => {
-      }
+        try {
-    });
+          const response = await fetch(`/api/go2rtc/streams/${streamName}`);
+          if (response.ok) {
+            const data = await response.json();
+            metadata[streamName] = data;
+          }
+        } catch (error) {
+          // eslint-disable-next-line no-console
+          console.error(`Failed to fetch metadata for ${streamName}:`, error);
+        }
+      }),
+    );
 
     return metadata;
   }, []);
 
   const { data: allStreamMetadata = {} } = useSWR<{
     [key: string]: LiveStreamMetadata;
-  }>(restreamedStreamsKey, streamsFetcher, {
+  }>(restreamedStreamsKey, streamsFetcher, { revalidateOnFocus: false });
-    revalidateOnFocus: false,
-    dedupingInterval: 10000,
-  });
 
   const [preferredLiveModes, setPreferredLiveModes] = useState<{
     [key: string]: LivePlayerMode;