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

Last update: Nov 09, 2022

Overview

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

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

cdfsl-benchmark

Comments

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?
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.

Does stage 5 include the average pooling layer to output the feature of size 1x2048?

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

SUPERVISED-CONTRASTIVE-LEARNING-FOR-PRE-TRAINED-LANGUAGE-MODEL-FINE-TUNING - The Facebook paper about fine tuning RoBERTa with contrastive loss

"# SUPERVISED-CONTRASTIVE-LEARNING-FOR-PRE-TRAINED-LANGUAGE-MODEL-FINE-TUNING" i

28 Dec 12, 2022

PyTorch implementation code for the paper MixCo: Mix-up Contrastive Learning for Visual Representation

How to Reproduce our Results This repository contains PyTorch implementation code for the paper MixCo: Mix-up Contrastive Learning for Visual Represen

46 Dec 15, 2022

Conformer: Local Features Coupling Global Representations for Visual Recognition

Conformer: Local Features Coupling Global Representations for Visual Recognition (arxiv) This repository is built upon DeiT and timm Usage First, inst

378 Jan 8, 2023

ImageNet-CoG is a benchmark for concept generalization. It provides a full evaluation framework for pre-trained visual representations which measure how well they generalize to unseen concepts.

The ImageNet-CoG Benchmark Project Website Paper (arXiv) Code repository for the ImageNet-CoG Benchmark introduced in the paper "Concept Generalizatio

23 Oct 9, 2022

Re-implementation of the Noise Contrastive Estimation algorithm for pyTorch, following "Noise-contrastive estimation: A new estimation principle for unnormalized statistical models." (Gutmann and Hyvarinen, AISTATS 2010)

Noise Contrastive Estimation for pyTorch Overview This repository contains a re-implementation of the Noise Contrastive Estimation algorithm, implemen

42 Nov 24, 2022

Code for Efficient Visual Pretraining with Contrastive Detection

Code for DetCon This repository contains code for the ICCV 2021 paper "Efficient Visual Pretraining with Contrastive Detection" by Olivier J. Hénaff,

56 Nov 13, 2022

The official implementation of CVPR 2021 Paper: Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation.

Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation This repository is the official implementation of CVPR 2021 paper:

9 Nov 14, 2022

This repository is the official implementation of Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning (NeurIPS21).

Core-tuning This repository is the official implementation of ``Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regular

18 Dec 17, 2022

improvement of CLIP features over the traditional resnet features on the visual question answering, image captioning, navigation and visual entailment tasks.

CLIP-ViL In our paper "How Much Can CLIP Benefit Vision-and-Language Tasks?", we show the improvement of CLIP features over the traditional resnet fea

310 Dec 28, 2022

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

Related tags

Overview

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

Prerequisites

How to Run

Download Datasets

Training

Evaluation

Linear evaluation

Few-shot

Full-network finetuning

Pretrained models

Citation

Acknowledgement

You might also like...

SUPERVISED-CONTRASTIVE-LEARNING-FOR-PRE-TRAINED-LANGUAGE-MODEL-FINE-TUNING - The Facebook paper about fine tuning RoBERTa with contrastive loss

PyTorch implementation code for the paper MixCo: Mix-up Contrastive Learning for Visual Representation

Conformer: Local Features Coupling Global Representations for Visual Recognition

ImageNet-CoG is a benchmark for concept generalization. It provides a full evaluation framework for pre-trained visual representations which measure how well they generalize to unseen concepts.

Re-implementation of the Noise Contrastive Estimation algorithm for pyTorch, following "Noise-contrastive estimation: A new estimation principle for unnormalized statistical models." (Gutmann and Hyvarinen, AISTATS 2010)

Code for Efficient Visual Pretraining with Contrastive Detection

The official implementation of CVPR 2021 Paper: Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation.

This repository is the official implementation of Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning (NeurIPS21).

improvement of CLIP features over the traditional resnet features on the visual question answering, image captioning, navigation and visual entailment tasks.

Comments

eurosat.zip cannot be found on google drive

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

Releases(v0.1.0)

v0.1.0(Apr 20, 2021)

Owner

Ashraful Islam

nanodet_plus,yolov5_v6.0

Weight estimation in CT by multi atlas techniques

Differentiable molecular simulation of proteins with a coarse-grained potential

You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors

A general framework for inferring CNNs efficiently. Reduce the inference latency of MobileNet-V3 by 1.3x on an iPhone XS Max without sacrificing accuracy.

DeepFaceEditing: Deep Face Generation and Editing with Disentangled Geometry and Appearance Control

TensorRT examples (Jetson, Python/C++)(object detection)

Public scripts, services, and configuration for running a smart home K3S network cluster

Official implementation of the NeurIPS'21 paper 'Conditional Generation Using Polynomial Expansions'.

The official implementation of CircleNet: Anchor-free Detection with Circle Representation, MICCAI 2030

MLSpace: Hassle-free machine learning & deep learning development

Benchmarks for Object Detection in Aerial Images

A basic reminder tool written in Python.

Python implementation of Bayesian optimization over permutation spaces.

A higher performance pytorch implementation of DeepLab V3 Plus(DeepLab v3+)

Official PyTorch implementation for FastDPM, a fast sampling algorithm for diffusion probabilistic models

Code for the paper "Generative design of breakwaters usign deep convolutional neural network as a surrogate model"

The 2nd place solution of 2021 google landmark retrieval on kaggle.

An implementation for the loss function proposed in Decoupled Contrastive Loss paper.

Spam your friends and famly and when you do your famly will disown you and you will have no friends.