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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

ArchitectureSelf-supervised MethodsModel Checkpoints
ResNet152MoCo-V2ResNet152-checkpoint_0799.pth.tar
ResNet101MoCo-V2ResNet101-checkpoint_0199.pth.tar
ResNet50MoCo-V2ResNet50-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

Teacher/StudentsEfficient-B0Efficient-B1ResNet-18ResNet-34MobileNet-v3Vit-TinyXCiT-Tiny
ResNet-50ResNet50-EfficientB0-checkpoint_0199.pth.tarDisCo-R50-Effb1.pth.tarResNet50-ResNet18-checkpoint_0199.pth.tarDisCo-R50-R34.pth.tarDisCo-R50-Mob.pth.tar--
ResNet-101ResNet101-EfficientB0-checkpoint_0199.pth.tarDisCo-R101-Effb1.pth.tarResNet101-ResNet18-checkpoint_0199.pth.tarDisCo-R101-R34.pth.tarDisCo-R101-Mob.pth.tar--
ResNet-152ResNet152-EfficientB0-checkpoint_0199.pth.tarDisCo-R152-Effb1.pth.tarResNet152-ResNet18-checkpoint_0199.pth.tarDisCo-R152-R34.pth.tarDisCo-R152-Mob.pth.tar--
ResNet-50*2ResNet50w2-EfficientB0-checkpoint_0199.pth.tarDisCo-RN50x2-Effb1.pth.tarResNet50w2-ResNet18-checkpoint_0199.pth.tarDisCo-RN50x2-R34.pth.tarDisCo-RN50x2-Mob.pth.tar--
ViT-Small-----DisCo-ViT-small-ViT-tiny.pth-
XCiT-Small------XCiT-Small-XCiT-Tiny-checkpoint.pth

Requirements

Reproduction

Commands can be found on Reproduction.

Thanks

Code heavily depends on MoCo-V2, Detectron2.