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

A 2D Visual Localization Framework based on Essential Matrices [ICRA2020]

A 2D Visual Localization Framework based on Essential Matrices This repository provides implementation of our paper accepted at ICRA: To Learn or Not

27 Nov 07, 2022

CCCL: Contrastive Cascade Graph Learning.

CCGL: Contrastive Cascade Graph Learning This repo provides a reference implementation of Contrastive Cascade Graph Learning (CCGL) framework as descr

19 Dec 05, 2022

A pytorch implementation of Detectron. Both training from scratch and inferring directly from pretrained Detectron weights are available.

Use this instead: https://github.com/facebookresearch/maskrcnn-benchmark A Pytorch Implementation of Detectron Example output of e2e_mask_rcnn-R-101-F

2.8k Dec 29, 2022

Segmentation and Identification of Vertebrae in CT Scans using CNN, k-means Clustering and k-NN

Segmentation and Identification of Vertebrae in CT Scans using CNN, k-means Clustering and k-NN If you use this code for your research, please cite ou

41 Dec 08, 2022

Simple-Neural-Network From Scratch in Python

Simple-Neural-Network From Scratch in Python This is a simple Neural Network created without any Machine Learning Libraries. The only dependencies are

1 Dec 28, 2021

Godot RL Agents is a fully Open Source packages that allows video game creators

Godot RL Agents The Godot RL Agents is a fully Open Source packages that allows video game creators, AI researchers and hobbiest the opportunity to le

326 Dec 30, 2022

SGoLAM - Simultaneous Goal Localization and Mapping

SGoLAM - Simultaneous Goal Localization and Mapping PyTorch implementation of the MultiON runner-up entry, SGoLAM: Simultaneous Goal Localization and

10 Jan 05, 2023

[ICCV 2021 Oral] Deep Evidential Action Recognition

DEAR (Deep Evidential Action Recognition) Project | Paper & Supp Wentao Bao, Qi Yu, Yu Kong International Conference on Computer Vision (ICCV Oral), 2

80 Jan 03, 2023

Some toy examples of score matching algorithms written in PyTorch

toy_gradlogp This repo implements some toy examples of the following score matching algorithms in PyTorch: ssm-vr: sliced score matching with variance

21 Dec 26, 2022

Bringing sanity to world of messed-up data

Sanitize sanitize is a Python module for making sure various things (e.g. HTML) are safe to use. It was originally written by Mark Pilgrim and is dist

63 Oct 26, 2021

Moon-patrol - A faithful recreation of the 1983 hit classic Moon Patrol for the Atari 2600 created using the Pygame library for Python

Moon Patrol A recreation of the hit Atari 2600 game, Moon Patrol Moon Patrol is

3 Apr 20, 2022

Official PyTorch implementation of "The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation" (ICCV 21).

CenterGroup This the official implementation of our ICCV 2021 paper The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person P

43 Dec 25, 2022

Official Implementation of SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations

Official Implementation of SimIPU SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations Since

37 Dec 01, 2022