PyTorch implementation of MixMatch: MixMatch: A Holistic Approach to Semi-Supervised Learning based on the official tensorflow implementation.
The implementation supports the following datasets:
- CIFAR-10 / CIFAR-100
- SVHN
- Caltech101 / Caltech256
- STL10
- HAM10000
- ImageNet
Required python packages are listed in requirements.txt. All dependencies can be installed using pip
pip install -r requirements.txt
or using conda
conda install --file requirements.txt
MixMatch training (default configuration) is started by running the following command (--pbar to show progress bar during training):
python main.py --pbar
All commandline arguments, which can be used to adapt the configuration of MixMatch are defined and described in arguments.py.
By default the following MixMatch configuration is run:
model: 'wide_resnet28_2'
dataset: 'cifar10'
lr: 0.002
wd: 0.00004
num_labeled: 250 (number of labeled samples, i.e. 25 labeled samples per class for cifar10)
epochs: 1024
iters_per_epoch: 1024
batch_size: 64
device: 'cuda'
out_dir: 'mixmatch'
mu: 1
temperature: 0.5
num_augmentations: 2
wu: 75
alpha: 0.75
In addition to these, the following arguments can be used to further configure the MixMatch training process:
--device <cuda / cpu>: Specify whether training should be run on GPU (if available) or CPU--num-workers <num_workers>: Number of workers used by torch dataloader--resume <path to run_folder>: Resumes training of training run saved at specified path, e.g.'out/mixmatch_training/run_0'. Dataset splits, model state, optimizer state, etc. are loaded and training is resumed with specified arguments.
Alternatively, the polyaxon.yaml-file can be used to start MixMatch training on a polyaxon-cluster:
polyaxon run -f polyaxon.yaml -u
For a general introduction to polyaxon and its commandline client, please refer to the official documentation
The training progress (loss, accuracy, etc.) can be monitored using tensorboard as follows:
tensorboard --logdir <result_folder>
This starts a tensorboard instance at localhost:6006, which can be opened in any common browser.
A trained MixMatch model can be evaluated by running:
python3 eval.py --run-path out/mixmatch_training/run_0 --pbar --device <cuda / cpu>
where --run-path specifies the path at which the run to be evaluated is saved. Alternatively, one can also check all
metrics over all epochs using the tensorboard file.
@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}
}