This project aims to adapt the YOLOv7 model to detect arbitrarily oriented objects in remote-sensing images. To achieve this, modifying the original loss function of the model is required. We obtained a successful result by increasing the number of anchor boxes with different rotating angles and combining the smooth-L1-IoU loss function proposed by R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object into the original loss for generating bounding boxes.

1. Clone repository

   $ git clone https://github.com/catchashu10/RotatedObjectDetection.git
   $ cd R-YOLOv7/
   

2. Create Environment

• Conda

  1. Create virual environment

     $ conda create -n ryolo python=3.8
     $ conda activate ryolo
     

  2. Install PyTorch and torchvision following the official instructions, e.g.,

     If you are using CUDA 11.8 version
     $ conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
     

  3. Install required libraries

     $ pip install -r requirements.txt
     

  4. Install detectron2 for calculating SkewIoU on GPU following the official instructions, e.g.,

     python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
     

• Docker

  $ docker build -t ryolo docker/
  $ sudo docker run -it --rm --gpus all --ipc=host --ulimit memlock=-1 -v ${your_path}:/workspace ryolo
  

6. Download pretrained weights
   weights

7. Make sure your files arrangment looks like the following
   Note that each of your dataset folder in data should split into three files, namely train, test, and detect.

   R-YOLOv7/
   ├── train.py
   ├── test.py
   ├── detect.py
   ├── xml2txt.py
   ├── environment.xml
   ├── requirements.txt
   ├── model/
   ├── datasets/
   ├── lib/
   ├── outputs/
   ├── weights/
       ├── pretrained/ (for training)
       └── UCAS-AOD/ (for testing and detection)
   └── data/
       └── UCAS-AOD/
           ├── class.names
           ├── train/
               ├── ...png
               └── ...txt
           ├── test/
               ├── ...png
               └── ...txt
           └── detect/
               └── ...png
   

The methods to load and train different datasets, such as UCAS-AOD and DOTA can be found at /datasets. The angle of each bounding box is limited in (- pi/2, pi/2], and the height of each bounding box is always longer than its width.

Check all the settings in the .yaml files that you will use in the /data folder.

$ python train.py --model_name DOTA_yolov7_csl_800 --config data/hyp.yaml --img_size 800 --data data/DOTA.yaml --epochs 100 --mode csl --ver yolov7

You can run display_inputs.py to visualize whether your data is loaded successfully.

Please refer to this repository to rearrange files so that the dataset can be loaded and trained by the model.

Download the official dataset from here. The original files should be able to be loaded and trained by the model.

$ python test.py --data data/DOTA.yaml --hyp data/hyp.yaml --weight_path weights/DOTA_yolov7_csl_800/best.pth --batch_size 8 --img_size 800 --mode csl --ver yolov7

$ python detect.py --data data/UCAS_AOD.yaml --hyp data/hyp.yaml --weight_path weights/DOTA_yolov7_csl_800/best.pth --batch_size 8 --img_size 800 --conf_thres 0.3 --mode csl --ver yolov7

If you would like to use tensorboard for tracking training process.

• Open an additional terminal in the same folder where you are running the program.
• Run command $ tensorboard --logdir=weights --port=6006
• Go to http://localhost:6006/

yingkunwu/R-YOLOv4

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

@inproceedings{wang2023yolov7,
  title={YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
  author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={7464--7475},
  year={2023}
}

R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

@article{r3det,
  title={R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object},
  author={Xue Yang, Junchi Yan, Ziming Feng, Tao He},
  journal = {arXiv},
  year={2019}
}