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

Paper

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

Prerequisites

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

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:

@inproceedings{islam2021broad,
  title={A broad study on the transferability of visual representations with contrastive learning},
  author={Islam, Ashraful and Chen, Chun-Fu Richard and Panda, Rameswar and Karlinsky, Leonid and Radke, Richard and Feris, Rogerio},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={8845--8855},
  year={2021}
}

Acknowledgement