From a6de759b01b0c067df12645bcd2c9888f311c3ef Mon Sep 17 00:00:00 2001 From: Nicolas Mowen Date: Sat, 22 Mar 2025 07:37:12 -0600 Subject: [PATCH] Update detector docs --- docs/docs/configuration/object_detectors.md | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/docs/docs/configuration/object_detectors.md b/docs/docs/configuration/object_detectors.md index 71716de6a..6083d0e95 100644 --- a/docs/docs/configuration/object_detectors.md +++ b/docs/docs/configuration/object_detectors.md @@ -344,6 +344,12 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl [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. +:::warning + +Currently D-FINE models only run on OpenVINO in CPU mode, GPUs currently fail to compile the model + +::: + After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration: ```yaml @@ -653,7 +659,7 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration: -``` +```yaml detectors: onnx: type: onnx @@ -671,7 +677,7 @@ model: [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. -After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration: +After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration: ```yaml detectors: @@ -898,7 +904,7 @@ Make sure you change the batch size to 1 before exporting. To export as ONNX: 1. `pip3 install rfdetr` -2. `python` +2. `python3` 3. `from rfdetr import RFDETRBase` 4. `x = RFDETRBase()` 5. `x.export()`