This is the PyTorch implementation of VGG network trained on CIFAR10 dataset
[PyTorch] (https://github.com/pytorch/pytorch)
[torchvision] (https://github.com/pytorch/vision)
Adding support for CPU. Add --cpu
can make the training or evaluation in cpu mode.
The trained VGG model. 92.4% Accuracy VGG
# 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
./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.