/RegionCL

This is the official code repo for "RegionCL: Can Simple Region Swapping Contribute to Contrastive Learning?"

Primary LanguagePythonApache License 2.0Apache-2.0

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