chengyangfu / Pytorch Vgg Cifar10
Licence: mit
This is the PyTorch implementation of VGG network trained on CIFAR10 dataset
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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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