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},
}
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