cifar-10-cnn

This repository is about some CNN Architecture's implementations for cifar10.

I just use Keras and Tensorflow to implementate all of these CNN models.

Requirements

• Python (3.5.2)
• Keras (2.0.6)
• tensorflow-gpu (1.2.1)

Architectures and papers

• The first CNN model: LeNet
  • LeNet-5 - Yann LeCun
• Network in Network
  • Network In Network
• Vgg19 Network
  • Very Deep Convolutional Networks for Large-Scale Image Recognition
• Residual Network
  • Deep Residual Learning for Image Recognition
  • Identity Mappings in Deep Residual Networks
• Wide Residual Network
  • Wide Residual Networks
• ResNeXt(TODO)
  • Aggregated Residual Transformations for Deep Neural Networks
• DenseNet(TODO)
  • Densely Connected Convolutional Networks

Accuracy of all my implementations

network	dropout	preprocess	GPU	epochs	training time	accuracy(%)
Lecun-Network	-	meanstd	GTX980TI	180	30 min	76.27
Network-in-Network	0.5	meanstd	GTX1060	164	1 h 30 min	91.15
Vgg19-Network	0.5	meanstd	GTX980TI	164	4 hours	93.43
Residual-Network50	-	meanstd	GTX980TI	200	8 h 58 min	94.10
Wide-resnet 16x8	-	meanstd	GTX1060	200	11 h 32 min	95.14