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LICENSE (35141, 2018-03-27)
cifar100vgg.py (8435, 2018-03-27)
cifar10vgg.py (8381, 2018-03-27)

# cifar-vgg This is a Keras model based on VGG16 architecture for CIFAR-10 and CIFAR-100. it can be used either with pretrained weights file or trained from scratch. This package contains 2 classes one for each datasets, the architecture is based on the VGG-16 [1] with adaptation to CIFAR datasets based on [2]. By running the py files you can get a sample of a trining and estimation of validation error. The CIFAR-10 reaches a validation accuracy of 93.56% CIFAR-100 reaches validation accuracy of 70.48%. On instantiation the model can either be trained or loaded from previous saved weight file. [cifar-100 weights](https://drive.google.com/open?id=0B4odNGNGJ56qTEdnT1RjTU44Zms) [cifar-10 weights](https://drive.google.com/open?id=0B4odNGNGJ56qVW9JdkthbzBsX28) References: [1] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. [2] Shuying Liu and Weihong Deng. Very deep convolutional neural network based image classifi- cation using small training sample size. In Pattern Recognition (ACPR), 2015 3rd IAPR Asian Conference on, pages 730“734. IEEE, 2015.

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