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Detail XDNA2 community detector (#24480)

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* Update hardware.md

Fix stray qualification

* docs: add XDNA2 detector configuration

* docs: add XDNA2 ZMQ model config
This commit is contained in:
Mitchell Currie
2026-09-28 22:49:37 -06:00
committed by GitHub
parent 4e196516dd
commit 61e50a366f
3 changed files with 87 additions and 0 deletions
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docs/data/object_detectors_models.yaml
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@@ -894,6 +894,41 @@ deepstack:
api_url: http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection
type: deepstack
api_timeout: 0.1 # seconds
xdna2:
title: AMD XDNA2
models:
- key: yolov9
label: YOLOv9
recommended: true
download: |-
Prepare the model using the frigate-xdna setup instructions linked above. For local YOLO models, Frigate must have access to the same ONNX file bytes as the sidecar. The example below uses YOLOv9-C at 320x320. Frigate+ models may instead use the same `plus://MODEL_ID` in Frigate and the sidecar.
ui: |-
Navigate to **Settings > System > Detectors and model** and select **ZMQ IPC** from the detector type dropdown and click **Add**, then set the endpoint to `tcp://xdna:5555`. Then on the same page, in the **Custom Model** tab, configure:
| Field | Value |
| ---------------------------------------- | ------------------------------------------ |
| **Custom object detector model path** | `/config/models/yolov9-c-320.onnx` |
| **Label map for custom object detector** | `/labelmap/coco-80.txt` |
| **Object detection model input width** | `320` |
| **Object detection model input height** | `320` |
| **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
| **Model Input Tensor Shape** | `nchw` |
| **Model Input D Type** | `float` |
| **Object Detection Model Type** | `yolo-generic` |
yaml: |-
detectors:
xdna:
type: zmq
endpoint: tcp://xdna:5555
model:
model_type: yolo-generic
width: 320
height: 320
input_tensor: nchw
input_dtype: float
path: /config/models/yolov9-c-320.onnx
labelmap_path: /labelmap/coco-80.txt
memryx:
title: MemryX
models: