This is an unofficial PyTorch implementation of Meta Pseudo Labels. The official Tensorflow implementation is here.
| 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 | - | - |
- I have experienced some difficulties while reproducing paper's result.
- Please let me know where to modify my code! (issue)
Train the model by 4000 labeled data of CIFAR-10 dataset:
python main.py --seed 5 --name cifar10-4K.5 --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 --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 cifar100-10K.5 --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 --amp
Monitoring training progress
tensorboard --logdir results
- python 3.6+
- torch 1.7+
- torchvision 0.8+
- tensorboard
- wandb
- numpy
- tqdm