PyTorch implementation of the paper "SCALoss: Side and Corner Aligned Loss for Bounding Box Regression" (AAAI 2022).

• IoU-based loss has the gradient vanish problem in the case of low overlapping bounding boxes with slow convergence speed.
• Side Overlap can put more penalty for low overlapping bounding box cases and Corner Distance can speed up the convergence.
• SCALoss, which combines Side Overlap and Corner Distance, can serve as a comprehensive similarity measure, leading to better localization performance and faster convergence speed.

• Python>=3.6.0
• PyTorch>=1.7
• Other dependencies described in requirements.txt

Conda is not necessary for the installation. Nevertheless, the installation process here is described using it.

$ conda create -n sca-yolo python=3.8 -y
$ conda activate sca-yolo
$ git clone https://github.com/Turoad/SCALoss
$ cd SCALoss
$ pip install -r requirements.txt

Train a model:

python train.py --data [dataset config] --cfg [model config] --weights [path of pretrain weights] --batch-size [batch size num]

For example, to train yolov3-tiny on COCO dataset from scratch with batch size=128.

python train.py --data coco.yaml --cfg yolov3-tiny.yaml --weights '' --batch-size 128

For multi-gpu training, it is recommended to use:

python -m torch.distributed.launch --nproc_per_node 4 train.py --img 640 --batch 32 --epochs 300 --data coco.yaml --weights '' --cfg yolov3.yaml --device 0,1,2,3

Test a model:

python val.py --data coco.yaml --weights runs/train/exp15/weights/last.pt --img 640 --iou-thres=0.65

The convergence curves of different losses on YOLOV3-tiny:

comming soon

If our paper and code are beneficial to your work, please consider citing:

@inproceedings{zheng2022scaloss,
  title={SCALoss: Side and Corner Aligned Loss for Bounding Box Regression},
  author={Zheng, Tu and Zhao, Shuai and Liu, Yang and Liu, Zili and Cai, Deng},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2022}
}

The code is modified from ultralytics/yolov3.