Skip to content

Latest commit

 

History

History
102 lines (84 loc) · 4.45 KB

README.md

File metadata and controls

102 lines (84 loc) · 4.45 KB

SCALoss

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

Introduction

corner_center_comp

  • 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

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: converge curve

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.