# yolo-seg-ort
<!-- PROJECT SHIELDS -->
<!-- PROJECT LOGO -->
<br />
<p align="center"> <a href="https://github.com/7emotions/yolo-seg-ort/">
<img src="images/logo.png" alt="Logo">
</a>
<h3 align="center">yolo-seg-ort</h3>
<p align="center">
采用纯ONNX Runtime实现YOLOv11-seg的onnx模型。<br />
<a href="https://github.com/7emotions/yolo-seg-ort"><strong>探索本项目的文档 »</strong></a><br />
<br />
<a href="https://github.com/7emotions/yolo-seg-ort/releases">查看发布</a>
·
<a href="https://github.com/7emotions/yolo-seg-ort/issues">报告Bug</a>
·
<a href="https://github.com/7emotions/yolo-seg-ort/issues">提出新特性</a>
</p>
</p>
## 1. 模型转换
```python
from ultralytics import YOLO
# Load the YOLO11 model
model = YOLO("best.pt")
# Export the model to ONNX format
model.export(format="onnx") # creates 'yolo11n.onnx'
```
## 2. 安装依赖
```bash
pip install -r requirements.txt
```
## 3. 用法
```python
from yolo_seg_ort import YOLOSeg
import cv2
onnx_path = "best.onnx"
image_path = "test.jpg"
image = cv2.imread(image_path)
model = YOLOSeg(
onnx_model=onnx_path,
classes=["Grass", "Ground", "Ramp", "Road", "Stairs"],
conf=0.25,
iou=0.7,
imgsz=640,
)
result = model(image)
if result:
result[0].save("./results.jpg")
# result[0].show()
else:
print("未检测到任何对象或结果为空。")
```
## 4. 结果
<img src="test/test.jpg"/><img src="test/results.jpg" />
## 5. 贡献者
[7emotions](https://github.com/7emotions)
## 6. 许可证
本项目采用 MIT 许可证。有关详细信息，请查看 [LICENSE](LICENSE) 文件。
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