# yolov5-ascend

**Repository Path**: snowingbear/yolov5-ascend

## Basic Information

- **Project Name**: yolov5-ascend
- **Description**: yolov5-ascend
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 6
- **Forks**: 1
- **Created**: 2023-01-06
- **Last Updated**: 2025-12-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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.

```txt
opencv_python
Pillow
torch
torchvision
```

## Export om model 
(1) Training your YOLOv5 model by [ultralytics/yolov5](https://github.com/ultralytics/yolov5). Then export the pytorch model to onnx format.
```bash
# 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.
```bash
# 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`.
```bash
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.
```bash
python detect_yolov5_ascend.py
```
The result will save to `img_out` folder.