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

K-Nearest Neighbor in Pytorch

Pytorch KNN CUDA 2019/11/02 This repository will no longer be maintained as pytorch supports sort() and kthvalue on tensors. git clone https://github.

65 Dec 01, 2022

Just Randoms Cats with python

Random-Cat Just Randoms Cats with python.

2 Dec 21, 2021

Open-Ended Commonsense Reasoning (NAACL 2021)

Open-Ended Commonsense Reasoning Quick links: [Paper] | [Video] | [Slides] | [Documentation] This is the repository of the paper, Differentiable Open-

31 Oct 19, 2022

Posterior predictive distributions quantify uncertainties ignored by point estimates.

177 Dec 06, 2022

Unofficial PyTorch Implementation of "DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features"

Pytorch Implementation of Deep Orthogonal Fusion of Local and Global Features (DOLG) This is the unofficial PyTorch Implementation of "DOLG: Single-St

96 Jan 06, 2023

Yolov5+SlowFast: Realtime Action Detection Based on PytorchVideo

Yolov5+SlowFast: Realtime Action Detection A realtime action detection frame work based on PytorchVideo. Here are some details about our modification:

181 Dec 30, 2022

Official code for "Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes", CVPR2022

[CVPR 2022] Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes Dongkwon Jin, Wonhui Park, Seong-Gyun Jeong, Heeyeon Kwon, and Cha

106 Dec 29, 2022

基于AlphaPose的TensorRT加速

1. Requirements CUDA 11.1 TensorRT 7.2.2 Python 3.8.5 Cython PyTorch 1.8.1 torchvision 0.9.1 numpy 1.17.4 (numpy版本过高会出报错 this issue ) python-package s

52 Dec 06, 2022

NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-based Simulation (ACL-IJCNLP 2021)

NeuralWOZ This code is official implementation of "NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-based Simulation". Sungdong Kim, Mi

31 Oct 25, 2022

Multi-Joint dynamics with Contact. A general purpose physics simulator.

MuJoCo Physics MuJoCo stands for Multi-Joint dynamics with Contact. It is a general purpose physics engine that aims to facilitate research and develo

5.2k Jan 02, 2023

Datasets, tools, and benchmarks for representation learning of code.

The CodeSearchNet challenge has been concluded We would like to thank all participants for their submissions and we hope that this challenge provided

1.8k Dec 25, 2022

MakeItTalk: Speaker-Aware Talking-Head Animation

MakeItTalk: Speaker-Aware Talking-Head Animation This is the code repository implementing the paper: MakeItTalk: Speaker-Aware Talking-Head Animation

285 Jan 08, 2023

Implementation of the master's thesis "Temporal copying and local hallucination for video inpainting".

Temporal copying and local hallucination for video inpainting This repository contains the implementation of my master's thesis "Temporal copying and

1 Dec 02, 2022

Multi-Anchor Active Domain Adaptation for Semantic Segmentation (ICCV 2021 Oral)

Multi-Anchor Active Domain Adaptation for Semantic Segmentation Munan Ning*, Donghuan Lu*, Dong Wei†, Cheng Bian, Chenglang Yuan, Shuang Yu, Kai Ma, Y

36 Dec 07, 2022

A simple, unofficial implementation of MAE using pytorch-lightning

Masked Autoencoders in PyTorch A simple, unofficial implementation of MAE (Masked Autoencoders are Scalable Vision Learners) using pytorch-lightning.

20 Dec 03, 2022

Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach

Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach This is the implementation of traffic prediction code in DTMP based on PyTo

1 Dec 19, 2021

Model Agnostic Interpretability for Multiple Instance Learning

MIL Model Agnostic Interpretability This repo contains the code for "Model Agnostic Interpretability for Multiple Instance Learning". Overview Executa

10 Dec 17, 2022

Open standard for machine learning interoperability

Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides

13.9k Dec 30, 2022

Analysis of Antarctica sequencing samples contaminated with SARS-CoV-2

Analysis of SARS-CoV-2 reads in sequencing of 2018-2019 Antarctica samples in PRJNA692319 The samples analyzed here are described in this preprint, wh

4 Feb 09, 2022

[ACM MM 2021] Yes, "Attention is All You Need", for Exemplar based Colorization

Transformer for Image Colorization This is an implemention for Yes, "Attention Is All You Need", for Exemplar based Colorization, and the current soft

30 Dec 07, 2022