/prelu_net

Implementation of PReLUNet by chainer (Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification: https://arxiv.org/abs/1502.01852)

Primary LanguagePythonMIT LicenseMIT

What's this

Implementation of PReLUNet by chainer

Dependencies

git clone https://github.com/nutszebra/prelu_net.git
cd prelu_net
git submodule init
git submodule update

How to run

python main.py -g 0

Details about my implementation

All hyperparameters and network architecture are the same as in [1] except for some parts.

  • Data augmentation
    Train: Pictures are randomly resized in the range of [256, 512], then 224x224 patches are extracted randomly and are normalized locally. Horizontal flipping is applied with 0.5 probability.
    Test: Pictures are resized to 384x384, then they are normalized locally. Single image test is used to calculate total accuracy.

  • SPP net Instead of spp, I use global average pooling.

  • Learning rate schedule Learning rate is divided by 10 at [150, 225] epoch. The total number of epochs is 300.

Cifar10 result

network total accuracy (%)
my implementation(model A) 94.98

loss

total accuracy

References

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification [1]