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.
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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]