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Image Classification with Pyramid Representation and Rotated Data Augmentation on Torch 7

Keven (Kedao) Wang
Stanford University
Stanford University, 2015

@article{wang2015image,

   title={Image Classification with Pyramid Representation and Rotated Data Augmentation on Torch 7},

   author={Wang, Keven Kedao},

   year={2015}

}

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This project classifies images in Tiny ImageNet Challenge, a dataset with 200 classes and 500 training examples for each class. Three network architectures are experimented: a traditional architecture with 4 convolutional layers + 2 fully-connected layers; a Tiny GoogleNet with 3 inception layers; and a pyramid representation-based network. Tiny GoogleNet achieved the highest top-1 validation accuracy of 47%. Work is done to reduce overfitting. Dropout improves validation accuracy by 10%. Data-augmentation of random crop and horizontal flip increased validation accuracy by 10%. Rotation does not appear to improve validation accuracy. Pyramid representation shows significant computational efficiency, achieving similar top result 240% faster computation time per batch. Training accuracy converges at 65 – 70% for all three networks. Future work is to increase expressive power of network. Training was done on Torch 7 with Facebook’s Deep Learning Extension.
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