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DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning(ECCV-2022 Oral)
This repository contains the Official Pytorch Implementation for DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning
@article{gao2021disco,
title={DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning},
author={Yuting Gao, Jia-Xin Zhuang, Shaohui Lin, Hao Cheng, Xing Sun, Ke Li, Chunhua Shen},
journal={European Conference on Computer Vision(ECCV)},
year={2022}
}
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Framework
<img width="580" alt="image" src="https://user-images.githubusercontent.com/22510464/124569124-3f0a1800-de78-11eb-8734-dfe86d87197d.png">Checkpoints
Teacher Models
Architecture | Self-supervised Methods | Model Checkpoints |
---|---|---|
ResNet152 | MoCo-V2 | ResNet152-checkpoint_0799.pth.tar |
ResNet101 | MoCo-V2 | ResNet101-checkpoint_0199.pth.tar |
ResNet50 | MoCo-V2 | ResNet50-checkpoint_0199.pth.tar |
For teacher models such as ResNet-50*2 etc, we use their official implementation, which can be downloaded from their github pages.
Student Models by DisCo
Requirements
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Python3
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Pytorch 1.6+
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Detectron2
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8 GPUs are preferred
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ImageNet, Cifar10/100, VOC, COCO
Reproduction
Commands can be found on Reproduction.
Thanks
Code heavily depends on MoCo-V2, Detectron2.