/FixMatch_pytorch

Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"

Primary LanguagePythonMIT LicenseMIT

FixMatch

The unofficial reimplementation of fixmatch with RandomAugment.

Overview

repo using EMA model to evaluate using EMA model to train update parameters update buffer
ours ✓ - ✓ -
mdiephuis ✓ ✓ ✓ -
kekmodel ✓ - - ✓

2020-03-30_18:07:08.log : annotation decay and add classifier.bias

2020-03-31_09:51:38.log : add interleave and run model once

Dependencies

  • python 3.6
  • pytorch 1.3.1
  • torchvision 0.2.1

The other packages and versions are listed in requirements.txt. You can install them by pip install -r requirements.txt.

Dataset

download cifar-10 dataset:

    $ mkdir -p dataset && cd data
    $ wget -c http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
    $ tar -xzvf cifar-10-python.tar.gz

download cifar-100 dataset:

    $ mkdir -p dataset && cd data
    $ wget -c http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
    $ tar -xzvf cifar-100-python.tar.gz

Train the model

To train the model on CIFAR10 with 40 labeled samples, you can run the script:

    $ CUDA_VISIBLE_DEVICES='0' python train.py --dataset CIFAR10 --n-labeled 40

To train the model on CIFAR100 with 400 labeled samples, you can run the script:

    $ CUDA_VISIBLE_DEVICES='0' python train.py --dataset CIFAR100 --n-labeled 400

Results

CIFAR10

#Labels 40 250 4000
Paper (RA) 86.19 ± 3.37 94.93 ± 0.65 95.74 ± 0.05
ours 89.63(85.65) 93.0832 94.7154

CIFAR100

#Labels 400 2500 10000
Paper (RA) 51.15 ± 1.75 71.71 ± 0.11 77.40 ± 0.12
ours 53.74 67.3169 73.26

References