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 simple Tensorflow based library for deep and/or denoising AutoEncoder.

libsdae - deep-Autoencoder & denoising autoencoder A simple Tensorflow based library for Deep autoencoder and denoising AE. Library follows sklearn st

147 Nov 18, 2022

Generative Exploration and Exploitation - This is an improved version of GENE.

GENE This is an improved version of GENE. In the original version, the states are generated from the decoder of VAE. We have to check whether the gere

33 Mar 23, 2022

PSTR: End-to-End One-Step Person Search With Transformers (CVPR2022)

PSTR (CVPR2022) This code is an official implementation of "PSTR: End-to-End One-Step Person Search With Transformers (CVPR2022)". End-to-end one-step

28 Dec 13, 2022

A curated list of references for MLOps

9.3k Jan 07, 2023

Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.

Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. Not an official Google product. Me

27 Dec 12, 2022

Code of the paper "Deep Human Dynamics Prior" in ACM MM 2021.

Code of the paper "Deep Human Dynamics Prior" in ACM MM 2021. Figure 1: In the process of motion capture (mocap), some joints or even the whole human

3 Oct 31, 2022

Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative Adversarial Neural Networks

ForecastingNonverbalSignals This is the implementation for the paper Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative A

1 Feb 10, 2022

An automated facial recognition based attendance system (desktop application)

Facial_Recognition_based_Attendance_System An automated facial recognition based attendance system (desktop application) Made using Python, Tkinter an

1 Jun 21, 2022

Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.

Pyserini Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations. Retrieval using sparse re

706 Dec 29, 2022

Implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021).

[PDF] | [Slides] The official implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021 Long talk) Installation Inst

117 Dec 09, 2022

Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning

SkFlow has been moved to Tensorflow. SkFlow has been moved to http://github.com/tensorflow/tensorflow into contrib folder specifically located here. T

3.2k Dec 29, 2022

Implementation of the paper "Language-agnostic representation learning of source code from structure and context".

Code Transformer This is an official PyTorch implementation of the CodeTransformer model proposed in: D. Zügner, T. Kirschstein, M. Catasta, J. Leskov

131 Dec 13, 2022

Neighborhood Reconstructing Autoencoders

Neighborhood Reconstructing Autoencoders The official repository for Neighborhood Reconstructing Autoencoders (Lee, Kwon, and Park, NeurIPS 2021). T

24 Dec 14, 2022

This repository contains a CBIR system that uses swin transformer to extract image's feature.

Swin-transformer based CBIR This repository contains a CBIR(content-based image retrieval) system. Here we use Swin-transformer to extract query image

12 Nov 17, 2022

Latex code for making neural networks diagrams

PlotNeuralNet Latex code for drawing neural networks for reports and presentation. Have a look into examples to see how they are made. Additionally, l

18.6k Jan 01, 2023

Here is the diagnostic tool for BMVC 2021 paper Diagnosing Errors in Video Relation Detectors.

Here is the diagnostic tool for BMVC 2021 paper Diagnosing Errors in Video Relation Detectors. We provide a tiny ground truth file demo_gt.json, and t

3 Dec 26, 2022

The InterScript dataset contains interactive user feedback on scripts generated by a T5-XXL model.

Interscript The Interscript dataset contains interactive user feedback on a T5-11B model generated scripts. Dataset data.json contains the data in an

8 Dec 01, 2022

PyTorch implementation of PSPNet

PSPNet with PyTorch Unofficial implementation of "Pyramid Scene Parsing Network" (https://arxiv.org/abs/1612.01105). This repository is just for caffe

52 Nov 16, 2022

Physics-informed Neural Operator for Learning Partial Differential Equation

PINO Physics-informed Neural Operator for Learning Partial Differential Equation Abstract: Machine learning methods have recently shown promise in sol

107 Jan 02, 2023

Source code for "OmniPhotos: Casual 360° VR Photography"

OmniPhotos: Casual 360° VR Photography Project Page | Video | Paper | Demo | Data This repository contains the source code for creating and viewing Om

144 Dec 30, 2022