pytorch-classification
Classification on CIFAR-10/100 and ImageNet with PyTorch.
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
)
Install
- Install PyTorch
- Clone recursively
git clone --recursive https://github.com/bearpaw/pytorch-classification.git
Training
Please see the Training recipes for how to train the models.
Results
CIFAR
Top1 error rate on the CIFAR-10/100 benchmarks are reported. You may get different results when training your models with different random seed. Note that the number of parameters are computed on the CIFAR-10 dataset.
Model | Params (M) | CIFAR-10 (%) | CIFAR-100 (%) |
---|---|---|---|
alexnet | 2.47 | 22.78 | 56.13 |
vgg19_bn | 20.04 | 6.66 | 28.05 |
ResNet-110 | 1.70 | 6.11 | 28.86 |
PreResNet-110 | 1.70 | 4.94 | 23.65 |
WRN-28-10 (drop 0.3) | 36.48 | 3.79 | 18.14 |
ResNeXt-29, 8x64 | 34.43 | 3.69 | 17.38 |
ResNeXt-29, 16x64 | 68.16 | 3.53 | 17.30 |
DenseNet-BC (L=100, k=12) | 0.77 | 4.54 | 22.88 |
DenseNet-BC (L=190, k=40) | 25.62 | 3.32 | 17.17 |
ImageNet
Single-crop (224x224) validation error rate is reported.
Model | Params (M) | Top-1 Error (%) | Top-5 Error (%) |
---|---|---|---|
ResNet-18 | 11.69 | 30.09 | 10.78 |
ResNeXt-50 (32x4d) | 25.03 | 22.6 | 6.29 |
Pretrained models
Our trained models and training logs are downloadable at OneDrive.
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
- Wide Residual Networks
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).
To Dingfan:
python cifar.py -a resnet50_1x1lmp -d cifar100 -j 0 --epochs 300 --schedule 150 225 -c xxx --layer 99 --img_size 32 --manualSeed 6 --DA --gpu-id 0