SCALoss

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

Introduction

• 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.

Prerequisites

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

Install

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

Getting started

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

Results and Checkpoints

YOLOv3-tiny

Model	mAP 0.5:0.95	AP 0.5	AP 0.65	AP 0.75	AP 0.8	AP 0.9
IoU	18.8	36.2	27.2	17.3	11.6	1.9
GIoU relative improv.(%)	18.8 0%	36.2 0%	27.1 -0.37%	17.6 1.73%	11.8 1.72%	2.1 10.53%
DIoU relative improv.(%)	18.8 0%	36.4 0.55%	26.9 -1.1%	17.2 -0.58%	11.8 1.72%	1.9 0%
CIoU relative improv.(%)	18.9 0.53%	36.6 1.1%	27.3 0.37%	17.2 -0.58%	11.6 0%	2.1 10.53%
SCA relative improv.(%)	19.9 5.85%	36.6 1.1%	28.3 4.04%	19.1 10.4%	13.3 14.66%	2.7 42.11%

The convergence curves of different losses on YOLOV3-tiny:

YOLOv3

Model	mAP 0.5:0.95	AP 0.5	AP 0.65	AP 0.75	AP 0.8	AP 0.9
IoU	44.8	64.2	57.5	48.8	41.8	20.7
GIoU relative improv.(%)	44.7 -0.22%	64.4 0.31%	57.5 0%	48.5 -0.61%	42 0.48%	20.4 -1.45%
DIoU relative improv.(%)	44.7 -0.22%	64.3 0.16%	57.5 0%	48.9 0.2%	42.1 0.72%	19.8 -4.35%
CIoU relative improv.(%)	44.7 -0.22%	64.3 0.16%	57.5 0%	48.9 0.2%	41.7 -0.24%	19.8 -4.35%
SCA relative improv.(%)	45.3 1.12%	64.1 -0.16%	57.9 0.7%	49.9 2.25%	43.3 3.59%	21.4 3.38%

YOLOV5s

comming soon

Citation

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}
}

Acknowledgement

The code is modified from ultralytics/yolov3.