xiaopingzeng/fixmatch-1

pytorch implementation of fixmatch on cifar with randaugment. 90%+ acc with 40 labeled training samples

FixMatch

This is my implementation of the experiment in the paper of fixmatch.

Environment setup

My platform is:

• 2080ti gpu
• ubuntu-16.04
• python3.6.9
• pytorch-1.3.1 installed via conda
• cudatoolkit-10.1.243
• cudnn-7.6.3 in /usr/lib/x86_64-linux-gpu

Dataset

download cifar-10 dataset:

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

Train the model

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

    $ python train.py --n-labeled 40 

where 40 is the number of labeled sample during training.

Results

After training the model with 40 labeled samples for 5 times with the command:

    $ python train.py --n-labeled 40 

I observed top-1 accuracy like this:

#No.	1	2	3	4	5
acc	91.81	91.29	89.51	91.32	79.42

Note: I only implemented experiements on cifar-10 dataset without CTAugment.