Detail XDNA2 community detector (#24480)

* Update hardware.md

Fix stray qualification

* docs: add XDNA2 detector configuration

* docs: add XDNA2 ZMQ model config

docs/data/object_detectors_models.yaml

@@ -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:

docs/docs/configuration/object_detectors.md

@@ -29,6 +29,7 @@ Frigate supports multiple different detectors that work on different types of ha
 
 - [ROCm](#amdrocm-gpu-detector): ROCm can run on AMD Discrete GPUs to provide efficient object detection.
 - [ONNX](#onnx): ROCm will automatically be detected and used as a detector in the `-rocm` Frigate image when a supported ONNX model is configured.
+- <CommunityBadge /> [XDNA2](#amd-xdna2): AMD Ryzen AI / XDNA2 NPUs can run object detection through the community-maintained `frigate-xdna` ZMQ sidecar.
 
 **Apple Silicon**
 
@@ -508,6 +509,28 @@ To verify that the integration is working correctly, start Frigate and observe t
 
 # Community Supported Detectors
 
+## AMD XDNA2
+
+AMD Ryzen AI / XDNA2 NPUs can be used through the community-maintained
+[frigate-xdna](https://github.com/mitchins/frigate-xdna) detector sidecar.
+The sidecar runs separately from Frigate and connects using Frigate's ZMQ
+detector interface.
+
+Currently qualified on **Ryzen AI Max 300 / Strix Halo**. Other XDNA2 devices
+are not yet qualified; XDNA1 is unsupported.
+
+Follow the frigate-xdna setup instructions to prepare and start the sidecar
+before starting Frigate.
+
+### Configuration {#configuration-xdna2}
+
+Using the detector config below will connect Frigate to the sidecar:
+
+<ModelConfigDropdown detectorTitle="AMD XDNA2" models={objectDetectorsModels.xdna2.models} />
+
+The example assumes Frigate and the sidecar share a Docker network where the
+sidecar is named `xdna`.
+
 ## MemryX MX3
 
 This detector is available for use with the MemryX MX3 accelerator M.2 module. Frigate supports the MX3 on compatible hardware platforms, providing efficient and high-performance object detection.

docs/docs/frigate/hardware.md

@@ -70,6 +70,9 @@ Frigate supports multiple different detectors that work on different types of ha
 - [ROCm](#rocm---amd-gpu): ROCm can run on AMD Discrete GPUs to provide efficient object detection
   - [Supports limited model architectures](../../configuration/object_detectors#amdrocm-gpu-detector)
   - Runs best on discrete AMD GPUs
+- <CommunityBadge /> [XDNA2 (Ryzen AI)](#amd-xdna2): AMD XDNA2 NPU (sub-watt power AI/ML processor separate to the GPU) inside Strix and other "AI" branded AMD platforms
+  - Has only been tested with YOLOv9, in theory other graphs may be compiled too.
+  - Runs via ZMQ proxy which adds some latency, only recommended for local connection
 
 **Apple Silicon**
 
@@ -296,6 +299,32 @@ The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms fo
 | ---------------- | ----------------------------------- |
 | yolov9-tiny      | ~ 4 ms                              |
 
+### AMD Ryzen AI / XDNA2
+
+Frigate supports AMD XDNA2 NPUs through the community-maintained
+frigate-xdna ZMQ sidecar. It works with stock Frigate and supports
+Frigate+ models or compatible local YOLO ONNX models. Models are compiled
+once on the target system and cached for subsequent use.
+
+Currently qualified on **Ryzen AI Max 300 / Strix Halo**. Other XDNA2
+devices are not yet qualified; XDNA1 is unsupported.
+
+Measured YOLOv9 detector latency on Strix Halo:
+
+| Model | 320 | 640 |
+| ----- | ---: | ---: |
+| YOLOv9-T | ~7.4 ms | unsupported |
+| YOLOv9-S | ~9.0 ms | ~20.0 ms |
+| YOLOv9-M | ~13.1 ms | ~34.4 ms |
+| YOLOv9-C | ~14.1 ms | ~35.2 ms |
+| YOLOv9-E | ~69.4 ms | ~224.8 ms |
+
+**YOLOv9-C at 320 is the recommended quality/performance balance.**
+C at 640 is also usable where the lower throughput is acceptable.
+
+Setup, model preparation, and compatibility details are available
+[in the frigate-xdna documentation](https://github.com/mitchins/frigate-xdna).
+
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
 
 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.