# FixMatch-pytorch

**Repository Path**: xiaoxu85/FixMatch-pytorch

## Basic Information

- **Project Name**: FixMatch-pytorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-01-05
- **Last Updated**: 2021-01-05

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# FixMatch
This is an unofficial PyTorch implementation of [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://arxiv.org/abs/2001.07685).
The official Tensorflow implementation is [here](https://github.com/google-research/fixmatch).

This code is only available in FixMatch (RandAugment).
Now only experiments on CIFAR-10 and CIFAR-100 are available.

## Requirements
- Python 3.6+
- PyTorch 1.4
- torchvision 0.5
- tensorboard
- tqdm
- numpy
- apex (optional)

## Usage

### Train
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 --expand-labels --seed 5 --out results/cifar10@4000.5
```

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 --wdecay 0.001 --expand-labels --seed 5 --out results/cifar100@10000
```

### Monitoring training progress
```
tensorboard --logdir=<your out_dir>
```

## Results (Accuracy)

### CIFAR10
| #Labels | 40 | 250 | 4000 |
|:---:|:---:|:---:|:---:|
| Paper (RA) | 86.19 ± 3.37 | 94.93 ± 0.65 | 95.74 ± 0.05 |
| This code | 93.60 | 95.31 | 95.77 |
| Acc. curve | [link](https://tensorboard.dev/experiment/YcLQA52kQ1KZIgND8bGijw/) | [link](https://tensorboard.dev/experiment/GN36hbbRTDaBPy7z8alE1A/) | [link](https://tensorboard.dev/experiment/5flaQd1WQyS727hZ70ebbA/) |

\* November 2020. Retested after fixing EMA issues.
### CIFAR100
| #Labels | 400 | 2500 | 10000 |
|:---:|:---:|:---:|:---:|
| Paper (RA) | 51.15 ± 1.75 | 71.71 ± 0.11 | 77.40 ± 0.12 |
| This code | 57.50 | 72.93 | 78.12 |
| Acc. curve | [link](https://tensorboard.dev/experiment/y4Mmz3hRTQm6rHDlyeso4Q/) | [link](https://tensorboard.dev/experiment/mY3UExn5RpOanO1Hx1vOxg/) | [link](https://tensorboard.dev/experiment/EDb13xzJTWu5leEyVf2qfQ/) |

\* Training using the following options `--amp --opt_level O2 --wdecay 0.001`

## References
- [Official TensorFlow implementation of FixMatch](https://github.com/google-research/fixmatch)
- [Unofficial PyTorch implementation of MixMatch](https://github.com/YU1ut/MixMatch-pytorch)
- [Unofficial PyTorch Reimplementation of RandAugment](https://github.com/ildoonet/pytorch-randaugment)
- [PyTorch image models](https://github.com/rwightman/pytorch-image-models)

## Citations
```
@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},
}
```