Meta Pseudo Labels

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

Results

• February 2022, Retested.

Usage

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

python main.py \
    --seed 2 \
    --name cifar10-4K.2 \
    --expand-labels \
    --dataset cifar10 \
    --num-classes 10 \
    --num-labeled 4000 \
    --total-steps 300000 \
    --eval-step 1000 \
    --randaug 2 16 \
    --batch-size 128 \
    --teacher_lr 0.05 \
    --student_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 \
    --student-wait-steps 3000 \
    --teacher-dropout 0.2 \
    --student-dropout 0.2 \
    --finetune-epochs 625 \
    --finetune-batch-size 512 \
    --finetune-lr 3e-5 \
    --finetune-weight-decay 0 \
    --finetune-momentum 0.9 \
    --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 2 \
    --name cifar100-10K.2 \
    --dataset cifar100 \
    --num-classes 100 \
    --num-labeled 10000 \
    --expand-labels \
    --total-steps 300000 \
    --eval-step 1000 \
    --randaug 2 16 \
    --batch-size 128 \
    --teacher_lr 0.05 \
    --student_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 \
    --student-wait-steps 3000 \
    --teacher-dropout 0.2 \
    --student-dropout 0.2 \
    --finetune-epochs 250 \
    --finetune-batch-size 512 \
    --finetune-lr 3e-5 \
    --finetune-weight-decay 0 \
    --finetune-momentum 0.9 \
    --amp

Monitoring training progress

tensorboard

tensorboard --logdir results

or

Use wandb

Requirements

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