What's this

Implementation of GoogLeNet by chainer

Dependencies

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

How to run

python main.py -p ./ -g 0 

Details about my implementation

• 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.

• Auxiliary classifiers
  No implementation

• Learning rate
  As [[1]][Paper] said, learning rate are multiplied by 0.96 at every 8 epochs. The description about initial learning rate can't be found in [[1]][Paper], so initial learning is setted as 0.0015 that is found in [[2]][Paper2].

• Weight decay
  The description about weight decay can't be found in [[1]][Paper], so by using [[2]][Paper2] and [[3]][Paper3] I guessed that weight decay is 2.0*10^-4.

Cifar10 result

network	depth	total accuracy (%)
my implementation	22	91.33

References

Going Deeper with Convolutions [[1]][Paper]
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift [[2]][Paper2]
Rethinking the Inception Architecture for Computer Vision [[3]][Paper3]
[paper]: https://arxiv.org/abs/1409.4842 "Paper" [paper2]: https://arxiv.org/abs/1502.03167 "Paper2" [paper3]: https://arxiv.org/abs/1512.00567 "Paper3"