python main.py \
--dataset CIFAR10 \
--num-labeled 1000 \
--lr-warm-up \
--warm-up-steps 10000 \
--num-steps 100000 \
--batch-size-lab 64 \
--batch-size-unlab 320 \
--confidence-mask 0.6 \
--softmax-temp 0.5 \
--seed 3 \
--rot \
--verbose \
--use-ema
python main.py \
--dataset ImageNet \
--num-classes 1000 \
--percent-labeled 10 \
--lr-warm-up \
--warm-up-steps 10000 \
--num-steps 200000 \
--batch-size-lab 128 \
--batch-size-unlab 128 \
--lr 0.3 \
--wdecay 1e-4 \
--confidence-mask 0.5 \
--softmax-temp 0.4 \
--gpu-id 0,1,2,3,4,5,6,7 \
--seed 3 \
--rot \
--verbose \
--use-ema
Use rot flag for using rotation loss from S4L paper. Run for 400k iterations(num-steps) for improved results.
| # Labels | UDA Paper | This UDA Repo | with Rotation(rot) |
|---|---|---|---|
| 250 | 8.76 | 11 | 8.2 |
| 500 | 6.68 | 9.1 | 6.4 |
| 1000 | 5.87 | 7.8 | 5.8 |
| 2000 | 5.51 | 6.9 | 5.7 |
| 4000 | 5.29 | 6.1 | 5.1 |
Model does not learn with higher learning rate lr=0.3