This is an unofficial PyTorch implementation of Meta Pseudo Labels

Overview

Meta Pseudo Labels

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

Results

CIFAR-10-4K SVHN-1K ImageNet-10%
Paper (w/ finetune) 96.11 ± 0.07 98.01 ± 0.07 73.89
This code (w/o finetune) 94.46 - -
This code (w/ finetune) WIP - -
Acc. curve link - -

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
Owner
Jungdae Kim
AI research engineer
Jungdae Kim
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