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Remove rocm detector (#16913)

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* Remove rocm detector plugin

* Update docs to recommend using onnx for rocm

* Formatting
This commit is contained in:
Nicolas Mowen
2025-03-03 08:16:14 -06:00
committed by GitHub
parent 0128ec2ba6
commit 71e6e04d77
4 changed files with 9 additions and 206 deletions
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docs/docs/configuration/object_detectors.md
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@@ -49,7 +49,7 @@ This does not affect using hardware for accelerating other tasks such as [semant
# Officially Supported Detectors
Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `onnx`, `openvino`, `rknn`, `rocm`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `onnx`, `openvino`, `rknn`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
## Edge TPU Detector
@@ -367,7 +367,7 @@ model:
### Setup
The `rocm` detector supports running YOLO-NAS models on AMD GPUs. Use a frigate docker image with `-rocm` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-rocm`.
Support for AMD GPUs is provided using the [ONNX detector](#ONNX). In order to utilize the AMD GPU for object detection use a frigate docker image with `-rocm` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-rocm`.
### Docker settings for GPU access
@@ -446,29 +446,9 @@ $ docker exec -it frigate /bin/bash -c '(unset HSA_OVERRIDE_GFX_VERSION && /opt/
### Supported Models
There is no default model provided, the following formats are supported:
#### YOLO-NAS
[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
After placing the downloaded onnx model in your config folder, you can use the following configuration:
```yaml
detectors:
rocm:
type: rocm
model:
model_type: yolonas
width: 320 # <--- should match whatever was set in notebook
height: 320 # <--- should match whatever was set in notebook
input_pixel_format: bgr
path: /config/yolo_nas_s.onnx
labelmap_path: /labelmap/coco-80.txt
```
Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
See [ONNX supported models](#supported-models) for supported models, there are some caveats:
- D-FINE models are not supported
- YOLO-NAS models are known to not run well on integrated GPUs
## ONNX