Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2025-03-04 00:17:22 +01:00
Code Issues Projects Releases Wiki Activity

Update d-fine documentation (#16881)

Browse Source
This commit is contained in:
Nicolas Mowen 2025-03-01 16:09:41 -07:00 committed by GitHub
parent 458134de5d
commit f56668e467
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 27 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

46
docs/docs/configuration/object_detectors.md
View File

@ -562,30 +562,15 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl
#### D-FINE
[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.
[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 YOLO-NAS model for use in Frigate.
To export as ONNX:
:::warning
1. Clone: https://github.com/Peterande/D-FINE and install all dependencies.
2. Select and download a checkpoint from the [readme](https://github.com/Peterande/D-FINE).
3. Modify line 58 of `tools/deployment/export_onnx.py` and change batch size to 1: `data = torch.rand(1, 3, 640, 640)`
4. Run the export, making sure you select the right config, for your checkpoint.
Example:
```
python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_m_obj2coco.yml -r output/dfine_m_obj2coco.pth
```
:::tip
Model export has only been tested on Linux (or WSL2). Not all dependencies are in `requirements.txt`. Some live in the deployment folder, and some are still missing entirely and must be installed manually.
Make sure you change the batch size to 1 before exporting.
D-FINE is currently not supported on OpenVINO
:::
After placing the downloaded onnx model in your config 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:
@ -784,6 +769,29 @@ Some model types are not included in Frigate by default.
Here are some tips for getting different model types
### Downloading D-FINE Model
To export as ONNX:
1. Clone: https://github.com/Peterande/D-FINE and install all dependencies.
2. Select and download a checkpoint from the [readme](https://github.com/Peterande/D-FINE).
3. Modify line 58 of `tools/deployment/export_onnx.py` and change batch size to 1: `data = torch.rand(1, 3, 640, 640)`
4. Run the export, making sure you select the right config, for your checkpoint.
Example:
```
python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_m_obj2coco.yml -r output/dfine_m_obj2coco.pth
```
:::tip
Model export has only been tested on Linux (or WSL2). Not all dependencies are in `requirements.txt`. Some live in the deployment folder, and some are still missing entirely and must be installed manually.
Make sure you change the batch size to 1 before exporting.
:::
### Downloading YOLO-NAS Model
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) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).