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Initial commit for AXERA AI accelerators (#22206)

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* feat: Initial AXERA detector

* chore: update pip install URL for axengine package

* Update docker/main/Dockerfile

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

* Update docs/docs/configuration/object_detectors.md

Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>

* Update AXERA section in installation.md

Removed details section for AXERA accelerators in installation guide.

* Update axmodel download URL to Hugging Face

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
Co-authored-by: shizhicheng <shizhicheng@axera-tech.com>
This commit is contained in:
GuoQing Liu
2026-03-11 06:49:28 -06:00
committed by GitHub
co-authored by Nicolas Mowen Josh Hawkins shizhicheng
parent a0b8271532
commit 9eb037c369
5 changed files with 181 additions and 4 deletions
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docs/docs/configuration/object_detectors.md
+43 -3
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@@ -49,6 +49,11 @@ Frigate supports multiple different detectors that work on different types of ha
- [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs.
**AXERA** <CommunityBadge />
- [AXEngine](#axera): axmodels can run on AXERA AI acceleration.
**For Testing**
- [CPU Detector (not recommended for actual use](#cpu-detector-not-recommended): Use a CPU to run tflite model, this is not recommended and in most cases OpenVINO can be used in CPU mode with better results.
@@ -1478,6 +1483,41 @@ model:
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
```
## AXERA
Hardware accelerated object detection is supported on the following SoCs:
- AX650N
- AX8850N
This implementation uses the [AXera Pulsar2 Toolchain](https://huggingface.co/AXERA-TECH/Pulsar2).
See the [installation docs](../frigate/installation.md#axera) for information on configuring the AXEngine hardware.
### Configuration
When configuring the AXEngine detector, you have to specify the model name.
#### yolov9
A yolov9 model is provided in the container at `/axmodels` and is used by this detector type by default.
Use the model configuration shown below when using the axengine detector with the default axmodel:
```yaml
detectors:
axengine:
type: axengine
model:
path: frigate-yolov9-tiny
model_type: yolo-generic
width: 320
height: 320
tensor_format: bgr
labelmap_path: /labelmap/coco-80.txt
```
# Models
Some model types are not included in Frigate by default.
@@ -1571,12 +1611,12 @@ YOLOv9 model can be exported as ONNX using the command below. You can copy and p
```sh
docker build . --build-arg MODEL_SIZE=t --build-arg IMG_SIZE=320 --output . -f- <<'EOF'
FROM python:3.11 AS build
RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
RUN apt-get update && apt-get install --no-install-recommends -y cmake libgl1 && rm -rf /var/lib/apt/lists/*
COPY --from=ghcr.io/astral-sh/uv:0.10.4 /uv /bin/
WORKDIR /yolov9
ADD https://github.com/WongKinYiu/yolov9.git .
RUN uv pip install --system -r requirements.txt
RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier>=0.4.1 onnxscript
RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier==0.4.* onnxscript
ARG MODEL_SIZE
ARG IMG_SIZE
ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt