RegionCL: Can Simple Region Swapping Contribute to Contrastive Learning?

Introduction | Results | Installation | News

Introduction

This repository contains the code, models, test results for the paper RegionCL: Can Simple Region Swapping Contribute to Contrastive Learning?. It contains a simple region swapping module to construct both global- and region-level contrastive pairs with minor modifications to current self-supervised methods, e.g., MoCov2, DenseCL, SimSiam, and so on.

Results

We test RegionCL-M/D/S on the ImageNet dataset for classification, MS COCO dataset for detection, instance segmentation and human pose estimation, Cityscapes dataset for instance and semantic segmentation, and AP-10K dataset for animal pose estimation. The results and training logs are available below.

Pretrain & Classification on ImageNet-1K

Model	Pretrain	IN1K Linear
RegionCL-M	log | config	69.4 | log
RegionCL-D	N/A	68.5 | N/A
RegionCL-S	N/A	71.3 | N/A

MaskRCNN detection and segmentation on MS COCO

Model	MS COCO Det&Seg C4 1x	MS COCO Det&Seg FPN 1x
RegionCL-M	39.8&34.8 | log	40.1&36.3 | log
RegionCL-D	40.3&35.2 | N/A	40.4&36.7 | N/A
RegionCL-S	38.7&33.7 | N/A	38.8&35.2 | N/A

Model	MS COCO Det&Seg C4 2x	MS COCO Det&Seg FPN 2x
RegionCL-M	41.5&35.9 | log	41.6&37.7 | log
RegionCL-D	41.8&36.4 | N/A	42.1&38.0 | N/A
RegionCL-S	40.7&35.4 | N/A	41.0&37.1 | N/A

RetinaNet detection on MS COCO

Model	MS COCO Det 1x	MS COCO Det 2x
RegionCL-M	38.4 | log	40.1 | log
RegionCL-D	38.8 | N/A	40.6 | N/A
RegionCL-S	36.8 | N/A	39.1 | N/A

Instance and semantic segmentation on Cityscapes

Model	MaskRCNN Inst-Seg	UperNet Sem-Seg 40K	UperNet Sem-Seg 80K
RegionCL-M	34.9 | log	78.1 | log	79.0 | log
RegionCL-D	34.8 | N/A	78.7 | N/A	79.5 | N/A
RegionCL-S	34.9 | N/A	77.8 | N/A	78.7 | N/A

SimpleBaseline pose estimation on MS COCO and AP-10K

Model	Human Pose	Animal Pose
RegionCL-M	72.3	70.6
RegionCL-D	73.6	72.1
RegionCL-S	72.2	71.6

Installation

The code is based on Openselfsup, thanks for their wonderful work!

Requirements:

• Python 3.6.5+
• Pytorch (version 1.7.0)
• mmcv (version 1.0.3)

1. Install mmcv following the requirements as in link

2. Clone this repository

   git clone https://github.com/Annbless/RegionCL.git

3. Go into the repository

   cd RegionCL

4. Install this repository

   pip install -v -e .

News

2021/12/30 Release the code and results for RegionCL-M

To do

• Release the code and logs for RegionCL-D and RegionCL-S
• Release the pretrained models
• Release the RegionCL with more self-supervised methods