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More inference speed updates (#19947)
* More inference speed updates * Update hardware.md * Update hardware.md * Update index.md * More inference speeds * Update home-assistant.md * Update object_detectors.md * Update first_model.md
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Nicolas Mowen
GitHub
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@ -1040,7 +1040,7 @@ COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
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WORKDIR /yolov9
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ADD https://github.com/WongKinYiu/yolov9.git .
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RUN uv pip install --system -r requirements.txt
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RUN uv pip install --system onnx onnxruntime onnx-simplifier>=0.4.1
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RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier>=0.4.1
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ARG MODEL_SIZE
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ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt
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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
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@ -132,17 +132,19 @@ More information is available [in the detector docs](/configuration/object_detec
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Inference speeds vary greatly depending on the CPU or GPU used, some known examples of GPU inference times are below:
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| Name | MobileNetV2 Inference Time | YOLOv9 | YOLO-NAS Inference Time | RF-DETR Inference Time | Notes |
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| -------------- | -------------------------- | -------------------------- | ------------------------- | ---------------------- | ---------------------------------- |
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| Intel HD 530 | 15 - 35 ms | | | | Can only run one detector instance |
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| Intel HD 620 | 15 - 25 ms | | 320: ~ 35 ms | | |
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| Intel HD 630 | ~ 15 ms | | 320: ~ 30 ms | | |
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| Intel UHD 730 | ~ 10 ms | | 320: ~ 19 ms 640: ~ 54 ms | | |
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| Intel UHD 770 | ~ 15 ms | t-320: 24: ms s-320: 30 ms | 320: ~ 20 ms 640: ~ 46 ms | | |
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| Intel N100 | ~ 15 ms | s-320: 30ms | 320: ~ 25 ms | | Can only run one detector instance |
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| Intel Iris XE | ~ 10 ms | | 320: ~ 18 ms 640: ~ 50 ms | | |
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| Intel Arc A380 | ~ 6 ms | | 320: ~ 10 ms 640: ~ 22 ms | 336: 20 ms 448: 27 ms | |
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| Intel Arc A750 | ~ 4 ms | | 320: ~ 8 ms | | |
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| Name | MobileNetV2 Inference Time | YOLOv9 | YOLO-NAS Inference Time | RF-DETR Inference Time | Notes |
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| -------------- | -------------------------- | --------------------------------------- | ------------------------- | ---------------------- | ---------------------------------- |
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| Intel HD 530 | 15 - 35 ms | | | | Can only run one detector instance |
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| Intel HD 620 | 15 - 25 ms | | 320: ~ 35 ms | | |
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| Intel HD 630 | ~ 15 ms | | 320: ~ 30 ms | | |
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| Intel UHD 730 | ~ 10 ms | | 320: ~ 19 ms 640: ~ 54 ms | | |
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| Intel UHD 770 | ~ 15 ms | t-320: 24 ms s-320: 30 ms s-640: 45 ms | 320: ~ 20 ms 640: ~ 46 ms | | |
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| Intel N100 | ~ 15 ms | s-320: 30 ms | 320: ~ 25 ms | | Can only run one detector instance |
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| Intel N150 | ~ 15 ms | t-320: 16ms s-320: 24 ms | | | |
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| Intel Iris XE | ~ 10 ms | s-320: 12 ms s-640: 30 ms | 320: ~ 18 ms 640: ~ 50 ms | | |
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| Intel Arc A310 | | s-320: 9 ms | | | |
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| Intel Arc A380 | ~ 6 ms | | 320: ~ 10 ms 640: ~ 22 ms | 336: 20 ms 448: 27 ms | |
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| Intel Arc A750 | ~ 4 ms | | 320: ~ 8 ms | | |
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### TensorRT - Nvidia GPU
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@ -169,6 +171,7 @@ Inference speeds will vary greatly depending on the GPU and the model used.
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| Name | YOLOv9 Inference Time | YOLO-NAS Inference Time | RF-DETR Inference Time |
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| --------------- | --------------------- | ------------------------- | ---------------------- |
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| GTX 1070 | s-320: 16 ms | 320: 14 ms | |
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| RTX 3050 | t-320: 15 ms | 320: ~ 10 ms 640: ~ 16 ms | Nano-320: ~ 12 ms |
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| RTX 3070 | t-320: 11 ms | 320: ~ 8 ms 640: ~ 14 ms | Nano-320: ~ 9 ms |
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| RTX A4000 | | 320: ~ 15 ms | |
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@ -185,6 +185,26 @@ For clips to be castable to media devices, audio is required and may need to be
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<a name="api"></a>
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## Camera API
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To disable a camera dynamically
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```
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action: camera.turn_off
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data: {}
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target:
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entity_id: camera.back_deck_cam # your Frigate camera entity ID
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```
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To enable a camera that has been disabled dynamically
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```
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action: camera.turn_on
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data: {}
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target:
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entity_id: camera.back_deck_cam # your Frigate camera entity ID
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```
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## Notification API
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Many people do not want to expose Frigate to the web, so the integration creates some public API endpoints that can be used for notifications.
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@ -34,6 +34,12 @@ Model IDs are not secret values and can be shared freely. Access to your model i
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:::
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:::tip
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When setting the plus model id, all other fields should be removed as these are configured automatically with the Frigate+ model config
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:::
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## Step 4: Adjust your object filters for higher scores
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Frigate+ models generally have much higher scores than the default model provided in Frigate. You will likely need to increase your `threshold` and `min_score` values. Here is an example of how these values can be refined, but you should expect these to evolve as your model improves. For more information about how `threshold` and `min_score` are related, see the docs on [object filters](../configuration/object_filters.md#object-scores).
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@ -51,7 +51,7 @@ Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVi
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| [Coral (all form factors)](/configuration/object_detectors.md#edge-tpu-detector) | `edgetpu` | `mobiledet` |
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| [Intel](/configuration/object_detectors.md#openvino-detector) | `openvino` | `yolov9` |
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| [NVidia GPU](/configuration/object_detectors#onnx) | `onnx` | `yolov9` |
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| [AMD ROCm GPU](/configuration/object_detectors#amdrocm-gpu-detector) | `rocm` | `yolov9` |
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| [AMD ROCm GPU](/configuration/object_detectors#amdrocm-gpu-detector) | `onnx` | `yolov9` |
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| [Hailo8/Hailo8L/Hailo8R](/configuration/object_detectors#hailo-8) | `hailo8l` | `yolov9` |
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| [Rockchip NPU](/configuration/object_detectors#rockchip-platform)\* | `rknn` | `yolov9` |
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