This is the code for CVPR 2021 oral paper: Jigsaw Clustering for Unsupervised Visual Representation Learning

Last update: Sep 18, 2022

Related tags

Deep Learning JigsawClustering

Overview

JigsawClustering

Jigsaw Clustering for Unsupervised Visual Representation Learning

Pengguang Chen, Shu Liu, Jiaya Jia

Introduction

This project provides an implementation for the CVPR 2021 paper "Jigsaw Clustering for Unsupervised Visual Representation Learning"

Installation

Environment

We verify our code on

4x2080Ti GPUs
CUDA 10.1
python 3.7
torch 1.6.0
torchvision 0.7.0

Other similar envirouments should also work properly.

Install

We use the SyncBN from apex, please install apex refer to https://github.com/NVIDIA/apex (SyncBN from pytorch should also work properly, we will verify it later.)

We use detectron2 for the training of detection tasks. If you are willing to finetune our pretrained model on the detection task, please install detectron2 refer to https://github.com/facebookresearch/detectron2

git clone https://github.com/Jia-Research-Lab/JigsawClustering.git
cd JigsawClustering/
pip install diffdist

Dataset

Please put the data under ./datasets. The directory looks like:

datasets
│
│───ImageNet/
│   │───class1/
│   │───class2/
│   │   ...
│   └───class1000/
│   
│───coco/
│   │───annotations/
│   │───train2017/
│   └───val2017/
│
│───VOC2012/
│   
└───VOC2007/

Results and pretrained model

The pretrained model is available at here.

Task	Dataset	Results
Linear Evaluation	ImageNet	66.4
Semi-Supervised 1%	ImageNet	40.7
Semi-Supervised 10%	ImageNet	63.0
Detection	COCO	39.3

Training

Pre-training on ImageNet

python main.py --dist-url 'tcp://localhost:10107' --multiprocessing-distributed --world-size 1 --rank 0 \
    -a resnet50 \
    --lr 0.03 --batch-size 256 --epoch 200 \
    --save-dir outputs/jigclu_pretrain/ \
    --resume outputs/jigclu_pretrain/model_best.pth.tar \
    --loss-t 0.3 \
    --cross-ratio 0.3 \
    datasets/ImageNet/

Linear evaluation on ImageNet

python main_lincls.py --dist-url 'tcp://localhost:10007' --multiprocessing-distributed --world-size 1 --rank 0 \
    -a resnet50 \
    --lr 10.0 --batch-size 256 \
    --prefix module.encoder. \
    --pretrained outputs/jigclu_pretrain/model_best.pth.tar \
    --save-dir outputs/jigclu_linear/ \
    datasets/ImageNet/

Semi-Supervised finetune on ImageNet

10% label

python main_semi.py --dist-url 'tcp://localhost:10102' --multiprocessing-distributed --world-size 1 --rank 0 \
    -a resnet50 \
    --batch-size 256 \
    --wd 0.0 --lr 0.01 --lr-last-layer 0.2 \
    --syncbn \
    --prefix module.encoder. \
    --labels-perc 10 \
    --pretrained outputs/jigclu_pretrain/model_best.pth.tar \
    --save-dir outputs/jigclu_semi_10p/ \
    datasets/ImageNet/

1% label

python main_semi.py --dist-url 'tcp://localhost:10101' --multiprocessing-distributed --world-size 1 --rank 0 \
    -a resnet50 \
    --batch-size 256 \
    --wd 0.0 --lr 0.02 --lr-last-layer 5.0 \
    --syncbn \
    --prefix module.encoder. \
    --labels-perc 1 \
    --pretrained outputs/jigclu_pretrain/model_best.pth.tar \
    --save-dir outputs/jigclu_semi_1p/ \
    datasets/ImageNet/

Transfer to COCO detection

Please convert the pretrained weight first

python detection/convert.py

Then start training using

python detection/train_net.py --config-file detection/configs/R50-JigClu.yaml --num-gpus 4

VOC detection

python detection/train_net.py --config-file detection/configs/voc-R50-JigClu.yaml --num-gpus 4

Citation

Please consider citing JigsawClustering in your publications if it helps your research.

@inproceedings{chen2021jigclu,
    title={Jigsaw Clustering for Unsupervised Visual Representation Learning},
    author={Pengguang Chen, Shu Liu, and Jiaya Jia},
    booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2021},
}

Comments

Some question about trainning

Hi~Thanks for your excellent work! I have a machine with 2 1080Ti,and I want to train your model on CIFAR10 with resnet18.

I use the parmeters like this ,but it seems don't work.

The program is stuck in this situation.

opened by zbw0329 10
Some details about the training

Hi, I have recently read your paper and find it very interesting. There are still some confusions about the experiments.

The experiments require 4 2080ti for training. Does it mean we must have 4 2080ti on one single machine? What if I have 4 2080ti on different machines? Is there any suggestion for this situation? BTW, how long does it take when you train on ImageNet1k?

Much appreciation for your reply.

Best wishes!

opened by Hanzy1996 3
Some questions about the results of ImageNet100

Thank you for your wonderful work, I want to do some more works based on your code. But I meet some questions about the results. I use the JigsawClustering and the dataset ImageNet100 to train the model. I only changed one line in the model to fit this dataset(I added model.fc = nn.Linear(2048, 100) in line 162 of main_lincls.py). However, despite using 4 GPUs, and did not change the configuration file. I only got an accuracy of 79.24. There is still a certain gap between this and the 80.9 reported in the paper. How can I achieve the accuracy reported in the paper now? Once again, thank you for your excellent work and code. I am looking forward to your reply.

opened by WilyZhao8 1
Results of Faster-RCNN R50-FPN with model pretrained on ImageNet with standard cross-entropy loss

Hi, thanks for your work! In Objection Detection, do you apply ResNet-50 model pretrained on ImageNet with standard cross-entropy loss to Faster-RCNN R50-FPN?

opened by fzfs 1
Training the model on a single GPU

Hi! I'm aware that the question has been asked previously, but could you guide how to modify jigclu to remove the distributeddataparallel depedency?

Thanks!

opened by shuvam-creditmate 2
It seems that the model has not learned anything，What should I do？

Thanks for your excellent work! I change the dataloader to use JigClu in CIFAR-10,and train the model on it by 1000epoch. But the prediction of my model is all the same. It seem that model always cluster into the same cluster

opened by zbw0329 10

Releases(1.0)

1.0(May 11, 2021)

Release pretrained models.
Source code(tar.gz)
Source code(zip)
JigClu_200e.pth(213.72 MB)

Owner

DV Lab

Deep Vision Lab

GitHub Repository

Scales, Chords, and Cadences: Practical Music Theory for MIR Researchers

ISMIR-musicTheoryTutorial This repository has slides and Jupyter notebooks for the ISMIR 2021 tutorial Scales, Chords, and Cadences: Practical Music T

58 Oct 11, 2022

Driller: augmenting AFL with symbolic execution!

Driller Driller is an implementation of the driller paper. This implementation was built on top of AFL with angr being used as a symbolic tracer. Dril

791 Jan 06, 2023

The author's officially unofficial PyTorch BigGAN implementation.

BigGAN-PyTorch The author's officially unofficial PyTorch BigGAN implementation. This repo contains code for 4-8 GPU training of BigGANs from Large Sc

2.6k Jan 02, 2023

Pytorch implementation of "ARM: Any-Time Super-Resolution Method"

ARM-Net Dependencies Python 3.6 Pytorch 1.7 Results Train Data preprocessing cd data_scripts python extract_subimages_test.py python data_augmentation

55 Nov 24, 2022

Data manipulation and transformation for audio signal processing, powered by PyTorch

torchaudio: an audio library for PyTorch The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the

1.9k Dec 28, 2022

Pytorch implementation of set transformer

set_transformer Official PyTorch implementation of the paper Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks .

410 Jan 06, 2023

LightLog is an open source deep learning based lightweight log analysis tool for log anomaly detection.

LightLog Introduction LightLog is an open source deep learning based lightweight log analysis tool for log anomaly detection. Function description [BG

25 Dec 17, 2022

Code for our work "Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection".

A2S-USOD Code for our work "Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection". Code will be released upon

15 Dec 16, 2022

Code for the ICML 2021 paper: "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision"

ViLT Code for the paper: "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision" Install pip install -r requirements.txt pip

922 Jan 01, 2023

PyTorch implementation for paper StARformer: Transformer with State-Action-Reward Representations.

StARformer This repository contains the PyTorch implementation for our paper titled StARformer: Transformer with State-Action-Reward Representations.

14 Dec 09, 2022

《Fst Lerning of Temporl Action Proposl vi Dense Boundry Genertor》(AAAI 2020)

Update 2020.03.13: Release tensorflow-version and pytorch-version DBG complete code. 2019.11.12: Release tensorflow-version DBG inference code. 2019.1

338 Dec 16, 2022

MAUS: A Dataset for Mental Workload Assessment Using Wearable Sensor - Baseline system

MAUS: A Dataset for Mental Workload Assessment Using Wearable Sensor - Baseline system Getting started To start working on this assignment, you should

2 Aug 06, 2022

A solution to the 2D Ising model of ferromagnetism, implemented using the Metropolis algorithm

Solving the Ising model on a 2D lattice using the Metropolis Algorithm Introduction The Ising model is a simplified model of ferromagnetism, the pheno

5 Nov 13, 2022

Baseline powergrid model for NY

Baseline-powergrid-model-for-NY Table of Contents About The Project Built With Usage License Contact Acknowledgements About The Project As the urgency

6 Nov 24, 2022

StarGAN v2-Tensorflow - Simple Tensorflow implementation of StarGAN v2

Official Tensorflow implementation Open ! - Clova AI StarGAN v2 — Un-official TensorFlow Implementation [Paper] [Pytorch] : Diverse Image Synthesis f

110 Jul 02, 2022

A PyTorch Lightning solution to training OpenAI's CLIP from scratch.

train-CLIP 📎 A PyTorch Lightning solution to training CLIP from scratch. Goal ⚽ Our aim is to create an easy to use Lightning implementation of OpenA

396 Dec 30, 2022

Deeprl - Standard DQN and dueling network for simple games

DeepRL This code implements the standard deep Q-learning and dueling network with experience replay (memory buffer) for playing simple games. DQN algo

6 Apr 12, 2020

RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems

RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems This is our implementation for the paper: Weibo Gao, Qi Liu*, Zhenya Hu

10 Oct 16, 2022

pytorch implementation of GPV-Pose

GPV-Pose Pytorch implementation of GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting. (link) UPDATE A new version

40 Dec 01, 2022

Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included.

pixel_character_generator Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included. Dataset TinyHero D

88 Nov 17, 2022