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.

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