YOLOv5_DOTA_OBB

YOLOv5 in DOTA_OBB dataset with CSL_label.(Oriented Object Detection)

Datasets and pretrained checkpoint

• Datasets : DOTA
• Pretrained Checkpoint or Demo Files :
  • train,detect_and_evaluate_demo_files: | Baidu Drive(6666). | Google Drive |
  • yolov5x.pt: | Baidu Drive(6666). | Google Drive |
  • yolov5l.pt: | Baidu Drive(6666). | Google Drive |
  • yolov5m.pt: | Baidu Drive(6666). | Google Drive |
  • yolov5s.pt: | Baidu Drive(6666). | Google Drive |
  • YOLOv5_DOTAv1.5_OBB.pt: | Baidu Drive(6666). | Google Drive |

Fuction

• 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

Installation (Linux Recommend, Windows not Recommend)

1. Python 3.8 with all requirements.txt dependencies installed, including torch==1.6, opencv-python==4.1.2.30, 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

More detailed explanation

想要了解相关实现的细节和原理可以看我的知乎文章:

• YOLOv5_DOTAv1.5(遥感旋转目标检测，全踩坑记录).
• YOLOv5_DOTA无人机/遥感旋转目标检测项目代码（从数据集制作、模型训练、性能评估全套流程）.
• YOLOv5在无人机/遥感场景下做旋转目标检测时进行的适应性改建详解.
• 【旋转目标检测】YOLOv5应对无人机/遥感场景相关难点的解决方案.

Usage Example

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

有问题反馈

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

感激

感谢以下的项目,排名不分先后

• ultralytics/yolov5.
• Thinklab-SJTU/CSL_RetinaNet_Tensorflow.

关于作者

  Name  : "胡凯旋"
  describe myself："咸鱼一枚"