This is an unofficial PyTorch implementation of FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. The official Tensorflow implementation is here.
This code is only available in FixMatch (RandAugment). Now only experiments on CIFAR-10 and CIFAR-100 are available.
- Python 3.6+
- PyTorch 1.4
- torchvision 0.5
- tensorboard
- tqdm
- numpy
- apex (optional)
Train the model by 4000 labeled data of CIFAR-10 dataset:
python train.py --dataset cifar10 --num-labeled 4000 --arch wideresnet --batch-size 64 --lr 0.03 --out cifar10@4000
Train the model by 10000 labeled data of CIFAR-100 dataset by using DistributedDataParallel:
python -m torch.distributed.launch --nproc_per_node 4 ./train.py --dataset cifar100 --num-labeled 10000 --arch wideresnet --batch-size 16 --lr 0.03 --out cifar100@10000
tensorboard --logdir=<your out_dir>
| #Labels | 40 | 250 | 4000 |
|---|---|---|---|
| Paper (RA) | 86.19 ± 3.37 | 94.93 ± 0.65 | 95.74 ± 0.05 |
| This code | - | - | 94.72 |
| #Labels | 400 | 2500 | 10000 |
|---|---|---|---|
| Paper (RA) | 51.15 ± 1.75 | 71.71 ± 0.11 | 77.40 ± 0.12 |
| This code | - | - | - |
* Results of this code were evaluated on 1 run.
- Unofficial PyTorch implementation of MixMatch: A Holistic Approach to Semi-Supervised Learning (https://github.com/YU1ut/MixMatch-pytorch)
@article{sohn2020fixmatch,
title={FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence},
author={Kihyuk Sohn and David Berthelot and Chun-Liang Li and Zizhao Zhang and Nicholas Carlini and Ekin D. Cubuk and Alex Kurakin and Han Zhang and Colin Raffel},
journal={arXiv preprint arXiv:2001.07685},
year={2020},
}