- The results of BestAcc have been updated! Note that we still have some experiments running in Azure, the current results will still be upadted and we will update all results and upload logs if all things done. I use single P100 for CIFAR-10 and SVHN, single P40 for STL-10, signle V100-32G for CIFAR-100.
- I have been using hundreds of GPUs of Azure to re-run all the experiments in the paper. I will clarify the gpu for each algorithm and dataset. Besides I will make all the log files available in TorchSSL git repo. Please wait for my updates. I will gradually upload all log files in 2 months.
- If you want to join TorchSSL team, please e-mail Yidong Wang ([email protected]; [email protected]) for more information. We plan to add more SSL algorithms and expand TorchSSL from CV to NLP and Speech.
An all-in-one toolkit based on PyTorch for semi-supervised learning (SSL). We implmented 9 popular SSL algorithms to enable fair comparison and boost the development of SSL algorithms.
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling(https://arxiv.org/abs/2110.08263)
We support fully supervised training + 9 popular SSL algorithms as listed below:
- PiModel [1]
- MeanTeacher [2]
- PseudoLabel [3]
- VAT [4]
- MixMatch [5]
- UDA [6]
- ReMixMatch [7]
- FixMatch [8]
- FlexMatch [9]
Besides, we implement our Curriculum Pseudo Labeling (CPL) method for Pseudo-Label (Flex-Pseudo-Label) and UDA (Flex-UDA).
We support 5 popular datasets in SSL research as listed below:
- CIFAR-10
- CIFAR-100
- STL-10
- SVHN
- ImageNet
- Prepare conda
- Run
conda env create -f environment.yml
It is convenient to perform experiment with TorchSSL. For example, if you want to perform FlexMatch algorithm:
- Modify the config file in
config/flexmatch/flexmatch.yaml
as you need - Run
python flexmatch.py --c config/flexmatch/flexmatch.yaml
Please download the ImageNet 2014 dataset (unchanged from 2012) from the official site (link: https://image-net.org/challenges/LSVRC/2012/2012-downloads.php)
Extract the train and the test set into subfolders (the val set is not used), and put them under train/
and val/
respectively. Each subfolder will represent a class.
Note: the offical test set is not zipped into subfolders by classes, you may want to use: https://github.com/jiweibo/ImageNet/blob/master/valprep.sh, which is a nice script for preparing the file structure.
If you want to write your own algorithm, please follow the following steps:
- Create a directory for your algorithm, e.g.,
SSL
, write your own model fileSSl/SSL.py
in it. - Write the training file in
SSL.py
- Write the config file in
config/SSL/SSL.yaml
The results are best accuracies with standard errors. We use random seed 0,1,2 for all experiments. All configs are included under the config/
folder.
CIFAR-10 | CIFAR100 | ||||||
---|---|---|---|---|---|---|---|
40 | 250 | 4000 | 400 | 2500 | 10000 | ||
FullySupervised | 95.38±0.05 | 95.39±0.04 | 95.38±0.05 | 80.7±0.09 | 80.7±0.09 | 80.73±0.05 | |
PiModel [1] | 25.66±1.76 | 53.76±1.29 | 86.87±0.59 | 13.04±0.8 | 41.2±0.66 | 63.35±0.0 | |
PseudoLabel [3] | 26.26±1.96 | 53.86±1.81 | 85.25±0.19 | 14.28±0.46 | 43.88±0.51 | 64.4±0.15 | |
PseudoLabel_Flex [9] | 26.26±1.96 | 53.86±1.81 | 85.25±0.19 | 14.28±0.46 | 43.88±0.51 | 64.4±0.15 | |
MeanTeacher [2] | 29.91±1.6 | 62.54±3.3 | 91.9±0.21 | 18.89±1.44 | 54.83±1.06 | 68.25±0.23 | |
VAT [4] | 25.34±2.12 | 58.97±1.79 | 89.49±0.12 | 14.8±1.4 | 53.16±0.79 | 67.86±0.19 | |
MixMatch [5] | 63.81±6.48 | 86.37±0.59 | 93.34±0.26 | 32.41±0.66 | 60.24±0.48 | 72.22±0.29 | |
ReMixMatch [7] | 90.12±1.03 | 93.7±0.05 | 95.16±0.01 | None | 73.89±0.41 | 79.79±0.07 | |
UDA [6] | 94.56±0.52 | 94.98±0.07 | 95.76±0.06 | 54.83±1.88 | 72.83±0.09 | 78.16±0.01 | |
UDA_Flex [9] | 94.56±0.52 | 94.98±0.07 | 95.76±0.06 | 54.83±1.88 | 72.83±0.09 | 78.16±0.01 | |
FixMatch [8] | 92.53±0.28 | 95.14±0.05 | 95.79±0.08 | 53.22±0.79 | 71.94±0.2 | 77.8±0.12 | |
FlexMatch [9] | 95.03±0.06 | 95.02±0.09 | 95.81±0.01 | 60.06±1.62 | 73.51±0.2 | 78.09±0.18 |
STL-10 | SVHN | ||||||
---|---|---|---|---|---|---|---|
40 | 250 | 1000 | 40 | 250 | 1000 | ||
FullySupervised | None | None | None | 97.87±0.02 | 97.87±0.01 | 97.86±0.01 | |
PiModel [1] | 25.69±0.85 | 44.87±1.5 | 67.22±0.4 | 32.52±0.95 | 86.7±1.12 | 92.84±0.11 | |
PseudoLabel [3] | 25.32±0.99 | 44.55±2.43 | 67.36±0.71 | 35.39±5.6 | 84.41±0.95 | 90.6±0.32 | |
PseudoLabel_Flex [9] | 26.58±2.19 | 47.94±2.5 | 67.95±0.37 | 36.79±3.64 | 79.58±2.11 | 87.95±0.54 | |
MeanTeacher [2] | 28.28±1.45 | 43.51±2.75 | 66.1±1.37 | 63.91±3.98 | 96.55±0.03 | 96.73±0.05 | |
VAT [4] | 25.26±0.38 | 43.58±1.97 | 62.05±1.12 | 25.25±3.38 | 95.67±0.12 | 95.89±0.2 | |
MixMatch [5] | 45.07±0.96 | 65.48±0.32 | 78.3±0.68 | 69.4±8.39 | 95.44±0.32 | 96.31±0.37 | |
ReMixMatch [7] | 67.88±6.24 | 87.51±1.28 | 93.26±0.14 | 75.96±9.13 | 93.64±0.22 | 94.84±0.31 | |
UDA [6] | 60.91±0.0 | 88.78±0.0 | 93.9±0.25 | 94.88±4.27 | 98.08±0.05 | 98.11±0.01 | |
UDA_Flex [9] | 69.09±0.97 | 91.32±0.0 | 93.9±0.25 | 96.58±1.51 | 97.34±0.83 | 97.98±0.05 | |
FixMatch [8] | 65.85±0.0 | 89.82±1.11 | 93.52±0.12 | 96.19±1.18 | 97.98±0.02 | 98.04±0.03 | |
FlexMatch [9] | 72.13±4.58 | 91.98±0.31 | 94.23±0.18 | 91.81±3.2 | 93.41±2.29 | 93.28±0.3 |
If you think this toolkit or the results are helpful to you and your research, please cite our paper:
@article{zhang2021flexmatch},
title={FlexMatch: Boosting Semi-supervised Learning with Curriculum Pseudo Labeling},
author={Zhang, Bowen and Wang, Yidong and Hou, Wenxin and Wu, Hao and Wang, Jindong and Okumura, Manabu and Shinozaki, Takahiro},
booktitle={Neural Information Processing Systems (NeurIPS)},
year={2021}
}
Yidong Wang1, Hao Wu2, Bowen Zhang1, Wenxin Hou1,3, Yuhao Chen4 Jindong Wang3
Shinozaki Lab1 http://www.ts.ip.titech.ac.jp/
Okumura Lab2 http://lr-www.pi.titech.ac.jp/wp/
Microsoft Research Asia3
Megvii4
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[2] Antti Tarvainen and Harri Valpola. Mean teachers are better role models: Weight-averagedconsistency targets improve semi-supervised deep learning results. InNeurIPS, pages 1195–1204, 2017.
[3] Dong-Hyun Lee et al. Pseudo-label: The simple and efficient semi-supervised learning methodfor deep neural networks. InWorkshop on challenges in representation learning, ICML,volume 3, 2013.
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[5] David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and ColinRaffel. Mixmatch: A holistic approach to semi-supervised learning.NeurIPS, page 5050–5060,2019.
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