旋转目标检测模型部署
参考资料：
YOLOv8-OBB 是 YOLOv8 系列中专为“旋转目标检测”设计的模型。它的全称为 Oriented Bounding Box（定向边界框），与普通 YOLO 检测模型的区别是：YOLOv8-OBB 输出的不是水平矩形，而是带旋转角的矩形框（x, y, w, h, θ），因此能够更紧密地包围倾斜或旋转的物体。
1.获取原始模型
1.进入目标检测模型仓库：
git clone https://github.com/airockchip/ultralytics_yolov8
cd ultralytics_yolov8
2.使用conda创建环境
conda create -name yolov8 python=3.9
conda activate yolov8
3.安装yolov8相关依赖
pip3 install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 ultralytics==8.3.31 onnx==1.17.0 onnxruntime==1.8.0 onnxsim==0.4.36
如已安装过可忽略！
4.修改./ultralytics/cfg/default.yaml配置文件
vi ./ultralytics/cfg/default.yaml
将原始的model: yolov8n.pt为：
model: yolov8n-obb.pt
5.导出ONNX模型
python ultralytics/engine/exporter.py
如果无法下载模型可直接访问yolov8n-obb.pt，下载后放在ultralytics_yolov8目录下，再次执行。
运行效果如下：
(yolov8) baiwen@dshanpi-a1:~/ultralytics_yolov8$ python ultralytics/engine/exporter.py
Ultralytics YOLOv8.2.82 🚀 Python-3.9.23 torch-2.4.1 CPU (Cortex-A53)
YOLOv8n-obb summary (fused): 187 layers, 3,080,144 parameters, 0 gradients, 8.3 GFLOPs
PyTorch: starting from 'yolov8n-obb.pt' with input shape (16, 3, 640, 640) BCHW and output shape(s) ((), (16, 1, 8400)) (6.3 MB)
RKNN: starting export with torch 2.4.1...
RKNN: feed yolov8n-obb.onnx to RKNN-Toolkit or RKNN-Toolkit2 to generate RKNN model.
Refer https://github.com/airockchip/rknn_model_zoo/tree/main/models/CV/object_detection/yolo
RKNN: export success ✅ 3.1s, saved as 'yolov8n-obb.onnx' (11.8 MB)
Export complete (23.8s)
Results saved to /home/baiwen/ultralytics_yolov8
Predict: yolo predict task=obb model=yolov8n-obb.onnx imgsz=640
Validate: yolo val task=obb model=yolov8n-obb.onnx imgsz=640 data=runs/DOTAv1.0-ms.yaml
Visualize: https://netron.app
执行完成后可以在当前目录下看到ONNX模型文件yolov8n-obb.onnx。
(yolov8) baiwen@dshanpi-a1:~/ultralytics_yolov8$ ls
CITATION.cff docs mkdocs.yml README.zh-CN.md tests yolov8n-obb.onnx yolov8n.pt
CONTRIBUTING.md examples pyproject.toml RKOPT_README.md ultralytics yolov8n-obb.pt yolov8n-seg.onnx
docker LICENSE README.md RKOPT_README.zh-CN.md ultralytics.egg-info yolov8n.onnx yolov8n-seg.pt
将导出的ONNX模型拷贝至yolov8模型目录。
cp yolov8n-obb.onnx ~/Projects/rknn_model_zoo/examples/yolov8_obb/model
2.模型转换
1.使用Conda激活rknn-toolkit2环境
conda activate rknn-toolkit2
2.进入yolov8模型转换目录
cd ~/Projects/rknn_model_zoo/examples/yolov8_obb/python
3.执行模型转换
python3 convert.py ../model/yolov8n-obb.onnx rk3576
运行效果如下：
(rknn-toolkit2) baiwen@dshanpi-a1:~/Projects/rknn_model_zoo/examples/yolov8_obb/python$ python3 convert.py ../model/yolov8n-obb.onnx rk3576
I rknn-toolkit2 version: 2.3.2
--> Config model
done
--> Loading model
I Loading : 100%|███████████████████████████████████████████████| 144/144 [00:00<00:00, 7599.52it/s]
done
--> Building model
I OpFusing 0: 100%|███████████████████████████████████████████████| 100/100 [00:01<00:00, 94.25it/s]
I OpFusing 1 : 100%|██████████████████████████████████████████████| 100/100 [00:01<00:00, 81.58it/s]
I OpFusing 2 : 100%|██████████████████████████████████████████████| 100/100 [00:03<00:00, 30.86it/s]
I GraphPreparing : 100%|█████████████████████████████████████████| 175/175 [00:00<00:00, 873.51it/s]
I Quantizating : 100%|████████████████████████████████████████████| 175/175 [00:31<00:00, 5.57it/s]
W build: The default input dtype of 'images' is changed from 'float32' to 'int8' in rknn model for performance!
Please take care of this change when deploy rknn model with Runtime API!
W build: The default output dtype of '383' is changed from 'float32' to 'int8' in rknn model for performance!
Please take care of this change when deploy rknn model with Runtime API!
W build: The default output dtype of '398' is changed from 'float32' to 'int8' in rknn model for performance!
Please take care of this change when deploy rknn model with Runtime API!
W build: The default output dtype of '413' is changed from 'float32' to 'int8' in rknn model for performance!
Please take care of this change when deploy rknn model with Runtime API!
W build: The default output dtype of '414' is changed from 'float32' to 'int8' in rknn model for performance!
Please take care of this change when deploy rknn model with Runtime API!
I rknn building ...
I rknn building done.
done
--> Export rknn model
done
可以看到转换完成后在model目录下看到端侧的RKNN模型。
(rknn-toolkit2) baiwen@dshanpi-a1:~/Projects/rknn_model_zoo/examples/yolov8_obb/python$ ls ../model
download_model.sh test.jpg yolov8n-obb.onnx yolov8n_obb.rknn yolov8_obb_labels_list.txt
3.模型推理
由于代码需要安装shapely，执行如下命令：
pip install Shapely
执行推理测试代码：
python3 yolov8_obb.py --model_path ../model/yolov8n_obb.rknn --target rk3576
运行效果如下：
(rknn-toolkit2) baiwen@dshanpi-a1:~/Projects/rknn_model_zoo/examples/yolov8_obb/python$ python3 yolov8_obb.py --model_path ../model/yolov8n_obb.rknn --target rk3576
I rknn-toolkit2 version: 2.3.2
done
--> Init runtime environment
I target set by user is: rk3576
done
--> Running model
W inference: The 'data_format' is not set, and its default value is 'nhwc'!
save image in ./result.jpg
运行完成后可以在当前目录下看到生成的结果图像。
