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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:
docs/docs/configuration/object_detectors.md
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@@ -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
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@@ -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.