$ python main.py --block_type basic --depth 110 --use_random_erase --random_erase_prob 0.5 --random_erase_area_ratio_range '[0.02, 0.4]' --random_erase_min_aspect_ratio 0.3 --random_erase_max_attempt 20 --outdir results
| Model | Test Error (median of 5 runs) | Training Time |
|---|---|---|
| ResNet-preact-56 w/o RandomErasing | 5.85 | 98 min |
| ResNet-preact-56 w/ RandomErasing | 5.22 | 98 min |
$ python -u main.py --depth 56 --block_type basic --base_lr 0.2 --seed 7 --outdir results/wo_random_erasing/00
$ python -u main.py --depth 56 --block_type basic --base_lr 0.2 --use_random_erase --seed 7 --outdir results/w_random_erasing/00
- Zhong, Zhun, et al. "Random Erasing Data Augmentation." arXiv preprint arXiv:1708.04896 (2017). arXiv:1708.04896, PyTorch implementation