Meta Pseudo Labels

This is an unofficial PyTorch implementation of Meta Pseudo Labels. The official Tensorflow implementation is here.

Results

Usage

Train the model by 4000 labeled data of CIFAR-10 dataset:

python main.py --seed 5 --name [email protected] --dataset cifar10 --num-classes 10 --num-labeled 4000 --expand-labels --total-steps 300000 --eval-step 1000 --randaug 2 16 --batch-size 128 --lr 0.05 --weight-decay 5e-4  --ema 0.995 --nesterov --mu 7 --label-smoothing 0.15 --temperature 0.7 --threshold 0.6 --lambda-u 8 --warmup-steps 5000 --uda-steps 5000 --amp

Train the model by 10000 labeled data of CIFAR-100 dataset by using DistributedDataParallel:

python -m torch.distributed.launch --nproc_per_node 4 main.py --seed 5 --name [email protected] --dataset cifar100 --num-classes 100 --num-labeled 10000 --expand-labels --total-steps 300000 --eval-step 1000 --randaug 2 16 --batch-size 32 --lr 0.05 --weight-decay 5e-4  --ema 0.995 --nesterov --mu 7 --label-smoothing 0.15 --temperature 0.7 --threshold 0.6 --lambda-u 8 --warmup-steps 5000 --uda-steps 5000 --amp

Monitoring training progress

tensorboard --logdir results

Requirements

• python 3.6+
• torch 1.7+
• torchvision 0.8+
• tensorboard
• numpy
• tqdm