FixMatch

# Motivation

# Summary of paper

# Commands

• for training,

# You want to train FixMatch using single gpu.
python train.py --use_gpu 0 --number_of_labels 40 
python train.py --use_gpu 1 --number_of_labels 250 
python train.py --use_gpu 2 --number_of_labels 4000 

# You want to train FixMatch using multiple gpus.
python train.py --use_gpu 0,1,2,3 --number_of_labels 40 
python train.py --use_gpu 0,1,2,3 --number_of_labels 250 
python train.py --use_gpu 0,1,2,3 --number_of_labels 4000 

• random seed is 0.

The number of labels	40	250	4000
Official implementation (with RA)	86.19 ± 3.37	94.93 ± 0.65	95.74 ± 0.05
My implementation (with RA)	92.39	95.14	95.62

# References

• Official Tensorflow implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence" (google-research/fixmatch) [Code]
• Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence" [Code]
• FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence [Paper]