ilyakava / MixMatch-pytorch

Code for "MixMatch - A Holistic Approach to Semi-Supervised Learning"

MixMatch

This is an unofficial PyTorch implementation of MixMatch: A Holistic Approach to Semi-Supervised Learning. The official Tensorflow implementation is here.

Experiments on CIFAR-10 and STL-10 are available.

This repository carefully implemented important details of the official implementation to reproduce the results.

Requirements

• Python 3.6+
• PyTorch 1.0
• torchvision 0.2.2 (older versions are not compatible with this code)
• tensorboardX
• progress
• matplotlib
• numpy

Usage

Train

Train the model by 250 labeled data of CIFAR-10 dataset:

python train.py --gpu <gpu_id> --n-labeled 250 --out cifar10@250

Train the model by 4000 labeled data of CIFAR-10 dataset:

python train.py --gpu <gpu_id> --n-labeled 4000 --out cifar10@4000

Train STL-10:

python train.py --resolution <32|48|96> --out stl10 --data_root data/stl10 --dataset STL10 --n-labeled 5000

Monitoring training progress

tensorboard.sh --port 6006 --logdir cifar10@250

Results (Accuracy)

CIFAR10

#Labels	250	500	1000	2000	4000
Paper	88.92 ± 0.87	90.35 ± 0.94	92.25 ± 0.32	92.97 ± 0.15	93.76 ± 0.06
This code	88.71	88.96	90.52	92.23	93.52

(Results of this code were evaluated on 1 run. Results of 5 runs with different seeds will be updated later. )

STL10

Using the entire 5000 point dataset:

Resolution	32	48	96
Paper	-	-	94.41
This code	82.69	86.41	91.33

References

@article{berthelot2019mixmatch,
  title={MixMatch: A Holistic Approach to Semi-Supervised Learning},
  author={Berthelot, David and Carlini, Nicholas and Goodfellow, Ian and Papernot, Nicolas and Oliver, Avital and Raffel, Colin},
  journal={arXiv preprint arXiv:1905.02249},
  year={2019}
}