TrainingBox, PyTorch Hub, random split, pretrained models for CIFAR-10 and CIFAR-100 datasets
New features
- Added TrainingBox to train models without teachers (PR #39)
- Supported PyTorch Hub in registry (PR #40)
- Supported random split e.g., split training dataset into training and validation datasets (PR #41)
- Added reimplemented models for CIFAR-10 and CIFAR-100 datasets (PR #41)
Pretrained models
Referred to the following repositories for training methods.
- ResNet: https://github.com/facebookarchive/fb.resnet.torch
- WRN (Wide ResNet): https://github.com/szagoruyko/wide-residual-networks
- DenseNet-BC: https://github.com/liuzhuang13/DenseNet
Note that there are some accuracy gaps between these and those reported in their original studies.
| CIFAR-10 | CIFAR-100 | |
|---|---|---|
| ResNet-20 | 91.92 | N/A |
| ResNet-32 | 93.03 | N/A |
| ResNet-44 | 93.20 | N/A |
| ResNet-56 | 93.57 | N/A |
| ResNet-110 | 93.50 | N/A |
| WRN-40-4 | 95.24 | 79.44 |
| WRN-28-10 | 95.53 | 81.27 |
| WRN-16-8 | 94.76 | 79.26 |
| DenseNet-BC (k=12, depth=100) | 95.53 | 77.14 |