A Broad Study on the Transferability of Visual Representations with Contrastive Learning

Related tags

Deep Learningtransfer-learningself-supervised-learningcross-domain-learningcontrastive-learningiccv2021
Overview

A Broad Study on the Transferability of Visual Representations with Contrastive Learning

Paper

This repository contains code for the paper: A Broad Study on the Transferability of Visual Representations with Contrastive Learning

Prerequisites

  • PyTorch 1.7
  • pytorch-lightning 1.1.5

Install the required dependencies by:

pip install -r environments/requirements.txt

How to Run

Download Datasets

The data should be located in ~/datasets/cdfsl folder. To download all the datasets:

bash data_loader/download.sh

Training

python main.py --system ${system}  --dataset ${train_dataset} --gpus -1 --model resnet50

where system is one of base_finetune(ce), moco (SelfSupCon), moco_mit (SupCon), base_plus_moco (CE+SelfSupCon), or supervised_mean2 (SupCon+SelfSupCon).

To know more about the cli arguments, see configs.py.

You can also run the training script by bash scripts/run_linear_bn.sh -m train.

Evaluation

Linear evaluation

python main.py --system linear_eval \
  --train_aug true --val_aug false \
  --dataset ${val_data}_train --val_dataset ${val_data}_test \
  --ckpt ${ckpt} --load_base --batch_size ${bs} \
  --lr ${lr} --optim_wd ${wd}  --linear_bn --linear_bn_affine false \
  --scheduler step  --step_lr_milestones ${_milestones}

You can also run the evaluation script by bash scripts/run_linear_bn.sh -m tune to hyper-parameter tune, and then bash scripts/run_linear_bn.sh -m test to do linear-evaluation on the optimal hyper-parameter.

Few-shot

python main.py --system few_shot \
    --val_dataset ${val_data} \
    --load_base --test --model ${model} \
    --ckpt ${ckpt} --num_workers 4

You can also run the evaluation script by bash scripts/run_fewshot.sh.

Full-network finetuning

python main.py --system linear_transfer \
    --dataset ${val_data}_train --val_dataset ${val_data}_test \
    --ckpt ${ckpt} --load_base \
    --batch_size ${bs} --lr ${lr} --optim_wd ${wd} \
    --scheduler step  --step_lr_milestones ${_milestones} \
    --linear_bn --linear_bn_affine false \
    --max_epochs ${max_epochs}

You can also run the evaluation script by bash scripts/run_transfer_bn.sh -m tune to hyper-parameter tune, and then bash scripts/run_transfer_bn.sh -m test to do linear-evaluation on the optimal hyper-parameter.

Pretrained models

  • ImageNet pretrained models can be found here

  • mini-ImageNet pretrained models can be found here

You can also convert our pretrained checkpoint into torchvision resnet style checkpoint by python utils/convert_to_torchvision_resnet.py -i [input ckpt] -o [output path]

Citation

If you find this repo useful for your research, please consider citing the paper:

@misc{islam2021broad,
      title={A Broad Study on the Transferability of Visual Representations with Contrastive Learning}, 
      author={Ashraful Islam and Chun-Fu Chen and Rameswar Panda and Leonid Karlinsky and Richard Radke and Rogerio Feris},
      year={2021},
      eprint={2103.13517},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledgement

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Comments
  • eurosat.zip cannot be found on google drive

    eurosat.zip cannot be found on google drive

    eurosat.zip cannot be found on google drive with the url: https://drive.google.com/uc?id=1FYZvuBePf2tuEsEaBCsACtIHi6eFpSwe

    Can you please check the url? Thank you.

    opened by Cohesion97 2
  • How to get CKA scores between different stages in Figure 4?

    How to get CKA scores between different stages in Figure 4?

    Thanks for your amazing study! I have some questions about the CKA scores shown in Figure 4. Take ResNet-50 as an example, which has five stages.

    1. Does stage 5 include the average pooling layer to output the feature of size 1x2048?
    2. Given an input sample, for the feature after each in-between stage (1-4), do you flatten the original feature map (1 x c x h x w) to a vector (1 x chw) OR do you adopt an extra average pooling process to obtain a vector (1 x c)? I've tried to flatten the feature map after each stage but obtained a very high-dimension vector (about 1M).

    (c: feature dimension; h: height; w: width) Looking forward to your reply, thanks.

    opened by klfsalfjl 0
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Ashraful Islam
Ashraful Islam
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