/pytorch-vgg-cifar10

This is the PyTorch implementation of VGG network trained on CIFAR10 dataset

Primary LanguageJupyter NotebookMIT LicenseMIT

pytorch-vgg-cifar10

This is the PyTorch implementation of VGG network trained on CIFAR10 dataset

Requirements.

[PyTorch] (https://github.com/pytorch/pytorch)

[torchvision] (https://github.com/pytorch/vision)

Update.

Adding support for CPU. Add --cpu can make the training or evaluation in cpu mode.

Download the model

The trained VGG model. 92.4% Accuracy VGG

Evaluation

# CUDA
wget http://www.cs.unc.edu/~cyfu/cifar10/model_best.pth.tar
python main.py --resume=./model_best.pth.tar -e
# or use CPU version
wget http://www.cs.unc.edu/~cyfu/cifar10/model_best_cpu.pth.tar
python main.py --resume=./model_best_cpu.pth.tar -e --cpu

Train with script! (16-bit precision)

./run.sh

Using the run.sh script to generate the training log and models of different versions of VGG in 16-bit or 32-bit precision. Then use the ipython notebook plot.ipynb to view the results.

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