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

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

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

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

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

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

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

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,

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:

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

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

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
Releases(v0.1.0)
Owner
Ashraful Islam
Ashraful Islam
An implementation of the [Hierarchical (Sig-Wasserstein) GAN] algorithm for large dimensional Time Series Generation

Hierarchical GAN for large dimensional financial market data Implementation This repository is an implementation of the [Hierarchical (Sig-Wasserstein

11 Nov 29, 2022
To Design and Implement Logistic Regression to Classify Between Benign and Malignant Cancer Types

To Design and Implement Logistic Regression to Classify Between Benign and Malignant Cancer Types, from a Database Taken From Dr. Wolberg reports his Clinic Cases.

Astitva Veer Garg 1 Jul 31, 2022
Cl datasets - PyTorch image dataloaders and utility functions to load datasets for supervised continual learning

Continual learning datasets Introduction This repository contains PyTorch image

berjaoui 5 Aug 28, 2022
Code of the paper "Shaping Visual Representations with Attributes for Few-Shot Learning (ASL)".

Shaping Visual Representations with Attributes for Few-Shot Learning This code implements the Shaping Visual Representations with Attributes for Few-S

chx_nju 9 Sep 01, 2022
Learning where to learn - Gradient sparsity in meta and continual learning

Learning where to learn - Gradient sparsity in meta and continual learning In this paper, we investigate gradient sparsity found by MAML in various co

Johannes Oswald 28 Dec 09, 2022
The BCNet related data and inference model.

BCNet This repository includes the some source code and related dataset of paper BCNet: Learning Body and Cloth Shape from A Single Image, ECCV 2020,

81 Dec 12, 2022
Code repository for "Free View Synthesis", ECCV 2020.

Free View Synthesis Code repository for "Free View Synthesis", ECCV 2020. Setup Install the following Python packages in your Python environment - num

Intelligent Systems Lab Org 253 Dec 07, 2022
[ECCV'20] Convolutional Occupancy Networks

Convolutional Occupancy Networks Paper | Supplementary | Video | Teaser Video | Project Page | Blog Post This repository contains the implementation o

622 Dec 30, 2022
Gradient Step Denoiser for convergent Plug-and-Play

Source code for the paper "Gradient Step Denoiser for convergent Plug-and-Play"

Samuel Hurault 11 Sep 17, 2022
Code for the SIGGRAPH 2022 paper "DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds."

DeltaConv [Paper] [Project page] Code for the SIGGRAPH 2022 paper "DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds" by Ru

98 Nov 26, 2022
Apply Graph Self-Supervised Learning methods to graph-level task(TUDataset, MolculeNet Datset)

Graphlevel-SSL Overview Apply Graph Self-Supervised Learning methods to graph-level task(TUDataset, MolculeNet Dataset). It is unified framework to co

JunSeok 8 Oct 15, 2021
Kaggle competition: Springleaf Marketing Response

PruebaEnel Prueba Kaggle-Springleaf-master Prueba Kaggle-Springleaf Kaggle competition: Springleaf Marketing Response Competencia de Kaggle: Marketing

1 Feb 09, 2022
PyTorch implemention of ICCV'21 paper SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation

SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation This is the PyTorch implemention of ICCV'21 paper SGPA: Structure

Chen Kai 24 Dec 05, 2022
Personal project about genus-0 meshes, spherical harmonics and a cow

How to transform a cow into spherical harmonics ? Spot the cow, from Keenan Crane's blog Context In the field of Deep Learning, training on images or

3 Aug 22, 2022
The repository for the paper "When Do You Need Billions of Words of Pretraining Data?"

pretraining-learning-curves This is the repository for the paper When Do You Need Billions of Words of Pretraining Data? Edge Probing We use jiant1 fo

ML² AT CILVR 19 Nov 25, 2022
Reading Group @mila-iqia on Computational Optimal Transport for Machine Learning Applications

Computational Optimal Transport for Machine Learning Reading Group Over the last few years, optimal transport (OT) has quickly become a central topic

Ali Harakeh 11 Aug 26, 2022
Face-Recognition-Attendence-System - This face recognition Attendence system using Python

Face-Recognition-Attendence-System I have developed this face recognition Attend

Riya Gupta 4 May 10, 2022
A public available dataset for road boundary detection in aerial images

Topo-boundary This is the official github repo of paper Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images

Zhenhua Xu 79 Jan 04, 2023
Tensors and Dynamic neural networks in Python with strong GPU acceleration

PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration Deep neural networks b

61.4k Jan 04, 2023
李云龙二次元风格化!打滚卖萌,使用了animeGANv2进行了视频的风格迁移

李云龙二次元风格化!一键star、fork,你也可以生成这样的团长! 打滚卖萌求star求fork! 0.效果展示 视频效果前往B站观看效果最佳:李云龙二次元风格化: github开源repo:李云龙二次元风格化 百度AIstudio开源地址,一键fork即可运行: 李云龙二次元风格化!一键fork

oukohou 44 Dec 04, 2022