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YOLOv5 ascend

This repo is YOLOv5 om model inference program on the Huawei Ascend platform.

All programs passed the test on Huawei Atlas 300I inference card (Ascend 310 AI CPU, CANN 5.0.2, npu-smi 21.0.2).

You can run demo by python detect_yolov5_ascend.py.

Environments

In addition to the Ascend environments with ATC tools, CANN(pyACL), and Python, you will need the following python packages.

opencv_python
Pillow
torch
torchvision

Export om model

(1) Training your YOLOv5 model by ultralytics/yolov5. Then export the pytorch model to onnx format.

# in yolov5 root path, exporting pth model to onnx model.
python export.py --weights yolov5s.pt --opset 12 --simplify --include onnx

(2) On the Huawei Ascend platform, using the atc tool convert the onnx model to om model.

# on Ascend 310 AI CPU, exporting onnx model to om model.
atc --input_shape="images:1,3,640,640" --input_format=NCHW --output="yolov5s" --soc_version=Ascend310 --framework=5 --model="yolov5s.onnx" --output_type=FP32

Inference by Ascend NPU

(1) Clone repo and move *.om model to yolov5-ascend/ascend/*.om.

git clone git@github.com:jackhanyuan/yolov5-ascend.git
mv yolov5s.om yolov5-ascend/ascend/

(2) Edit label file in yolov5-ascend/ascend/yolov5.label.

(3) Run inference program.

python detect_yolov5_ascend.py

The result will save to img_out folder.