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

• https://github.com/CoinCheung/fixmatch
• https://github.com/kekmodel/FixMatch-pytorch
• official implement https://github.com/google-research/fixmatch