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

References

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