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Add docs for rf-detr model
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@ -342,7 +342,7 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl
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#### D-FINE
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[D-FINE](https://github.com/Peterande/D-FINE) is the [current state of the art](https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=d-fine-redefine-regression-task-in-detrs-as) at the time of writing. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
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[D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
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After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration:
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@ -647,9 +647,29 @@ model:
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Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
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#### RF-DETR
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[RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more informatoin on downloading the RF-DETR model for use in Frigate.
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After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
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```
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detectors:
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onnx:
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type: onnx
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model:
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model_type: rfdetr
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width: 560
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height: 560
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input_tensor: nchw
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input_dtype: float
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path: /config/model_cache/rfdetr.onnx
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```
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#### D-FINE
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[D-FINE](https://github.com/Peterande/D-FINE) is the [current state of the art](https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=d-fine-redefine-regression-task-in-detrs-as) at the time of writing. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
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[D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
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After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration:
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@ -873,6 +893,16 @@ Make sure you change the batch size to 1 before exporting.
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:::
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### Download RF-DETR Model
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To export as ONNX:
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1. `pip3 install rfdetr`
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2. `python`
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3. `from rfdetr import RFDETRBase`
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4. `x = RFDETRBase()`
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5. `x.export()`
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### Downloading YOLO-NAS Model
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You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
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