bupt-priv pytorch-classification
Classification on CIFAR-10/100 and ImageNet with PyTorch.
Disclaimer
The official pytorch-classification code is available here, thanks for bearpaw's job!
The bupt-priv pytorch-classification is modified version of pytorch-classification, and not for commercial use.
Features
- Unified interface for different network architectures
- Multi-GPU support
- Training progress bar with rich info
- Training log and training curve visualization code (see
./utils/logger.py) - Add Aligned Inception Resnet/ResNeXt (by soeaver)
- Add ResNeXt26-32x4d (by soeaver)
Install
- Install PyTorch
- Clone recursively
git clone --recursive https://github.com/soeaver/pytorch-classification
Training
Please see the Training recipes for how to train the models.
Results
CIFAR
Top1 error rate on the CIFAR-10/100 benchmarks can be found in official pytorch-classification
ImageNet
Single-crop (224x224) validation error rate is reported.
| Model | Params (M) | Top-1 Error (%) | Top-5 Error (%) |
|---|---|---|---|
| ResNet18 | 11.69 | 30.09 | 10.78 |
| ResNeXt50-32x4d | 25.03 | 22.60 | 6.29 |
| ResNet18 (bupt-priv) | 11.69 | 29.11 | 10.07 |
| ResNeXt26-32x4d (bupt-priv) | 15.39 | 24.93 | 7.75 |
| Se-ResNeXt50-32x4d (bupt-priv) | 27.56 | 21.91 | 6.02 |
| AIR101 (bupt-priv) | 64.38 | 20.74 | 5.56 |
| AIRX50-24x4d (bupt-priv) | 28.09 | 21.88 | 5.95 |
| AIRX101-32x4d (bupt-priv) | 64.17 | 20.62 | 5.50 |
| AIRXt152-32x4d (bupt-priv) | 89.34 | 20.29 | 5.24 |
- All the bupt-priv models are trained for 130/140 epochs, the lr schedule is 61 91 121.
- AIR/AIRX is short for Aligned-Inception-ResNe(X)t.
Supported Architectures
CIFAR-10 / CIFAR-100
Since the size of images in CIFAR dataset is 32x32, popular network structures for ImageNet need some modifications to adapt this input size. The modified models is in the package models.cifar:
- AlexNet
- VGG (Imported from pytorch-cifar)
- ResNet
- Pre-act-ResNet
- ResNeXt (Imported from ResNeXt.pytorch)
- Wide Residual Networks (Imported from WideResNet-pytorch)
- DenseNet
ImageNet
- All models in
torchvision.models(alexnet, vgg, resnet, densenet, inception_v3, squeezenet) - ResNeXt
- AIR/AIRX
Contribute
Feel free to create a pull request if you find any bugs or you want to contribute (e.g., more datasets and more network structures).