This repo is the official implementation of the ECCV2022 paper: Efficient One Pass Self-distillation with Zipf's Label Smoothing.
[2022.9] Pytorch Zipf's label smoothing is uploaded! CIFAR, TinyImageNet, ImageNet, INAT-21 are now supported by our training codes.
[2022.7] MegEngine Zipf's label smoothing is uploaded!
| Method | DenseNet121 | DenseNet121 | ResNet18 | ResNet18 |
|---|---|---|---|---|
| Arch | CIFAR100 | TinyImageNet | CIFAR100 | TinyImageNet |
| Pytorch Baseline | 77.86±0.26 | 60.31±0.36 | 75.51±0.28 | 56.41±0.20 |
| Pytorch Zipf's LS | 79.03±0.32 | 62.64±0.30 | 77.38±0.32 | 59.25±0.20 |
| Megengine Baseline | 77.97±0.18 | 60.78±0.31 | 75.29±0.29 | 56.03±0.34 |
| Megengine Zipf's LS | 79.85±0.27 | 62.35±0.32 | 77.08±0.28 | 59.01±0.23 |
train_baseline_cifar100_resnet18:
python3 train.py --ngpus 1 --dataset CIFAR100 --data_dir cifar100_data --arch CIFAR_ResNet18 --loss_lambda 0.0 --alpha 0.0 --densetrain_ZipfsLS_cifar100_resnet18:
python3 train.py --ngpus 1 --dataset CIFAR100 --data_dir cifar100_data --arch CIFAR_ResNet18 --loss_lambda 0.1 --alpha 0.1 --denseSee more examples in Makefile.
Zipf's LS is released under the Apache 2.0 license. See LICENSE for details.
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