YOLOv5 in DOTA_OBB dataset with CSL_label.(Oriented Object Detection)
Datasets: DOTA
train.py. Train.detect.py. Detect and visualize the detection result. Get the detection result txt.evaluation.py. Merge the detection result and visualize it. Finally evaluate the detector
1. Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:
$ pip install -r requirements.txt 2. Install swig
$ cd \.....\yolov5_DOTA_OBB\utils $ sudo apt-get install swig 3. Create the c++ extension for python
$ swig -c++ -python polyiou.i $ python setup.py build_ext --inplace 1.'Get Dataset'
Split the DOTA_OBB image and labels. Trans DOTA format to YOLO longside format.
You can refer to hukaixuan19970627/DOTA_devkit_YOLO.
The Oriented YOLO Longside Format is:
$ classid x_c y_c longside shortside Θ Θ∈[0, 180) * longside: The longest side of the oriented rectangle. * shortside: The other side of the oriented rectangle. * Θ: The angle between the longside and the x-axis(The x-axis rotates clockwise).x轴顺时针旋转遇到最长边所经过的角度 WARNING: IMAGE SIZE MUST MEETS 'HEIGHT = WIDTH'
2.'train.py'
- All same as ultralytics/yolov5. You better train demo files first before train your custom dataset.
- Single GPU training:
$ python train.py --batch-size 4 --device 0 - Multi GPU training: DistributedDataParallel Mode
python -m torch.distributed.launch --nproc_per_node 4 train.py --sync-bn --device 0,1,2,3 
3.'detect.py'
- Download the demo files.
- Then run the demo. Visualize the detection result and get the result txt files.
$ python detect.py 
4.'evaluation.py'
- Run the detect.py demo first. Then change the path with yours:
evaluation ( detoutput=r'/....../DOTA_demo_view/detection', imageset=r'/....../DOTA_demo_view/row_images', annopath=r'/....../DOTA_demo_view/row_DOTA_labels/{:s}.txt' ) draw_DOTA_image ( imgsrcpath=r'/...../DOTA_demo_view/row_images', imglabelspath=r'/....../DOTA_demo_view/detection/result_txt/result_merged', dstpath=r'/....../DOTA_demo_view/detection/merged_drawed' ) - Run the evaluation.py demo. Get the evaluation result and visualize the detection result which after merged.
$ python evaluation.py 
在使用中有任何问题，欢迎反馈给我，可以用以下联系方式跟我交流
- 知乎（@略略略）
- 代码问题提issues,其他问题请知乎上联系
感谢以下的项目,排名不分先后
借用moblieNetv2思想，改变backbone部分，实现轻量化网络，减少计算量和参数量 待续..