YOLOv5_Rotate

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

Datasets and pretrained checkpoint

• Datasets : DOTA

• Datasets : [Electrical-safety]-Not public yet

• 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 |

• The latest YOLOV5 model weights : (https://github.com/ultralytics/yolov5).

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

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'

$  python detect.py