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

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

• Python (3.5.2)
• Keras (2.0.8)
• tensorflow-gpu (1.3.0)

• 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
  • Aggregated Residual Transformations for Deep Neural Networks
• DenseNet
  • Densely Connected Convolutional Networks
• SENet
  • Squeeze-and-Excitation Networks

Now, I fixed some bugs and used 1080TI to retrain all of the following models.

In particular：
Change the batch size according to your GPU's memory.
Modify the learning rate schedule may imporve the results of accuracy!

Because I don't have enough machines to train the larger networks.
So I only trained the smallest network described in the paper.
You can see the results in liuzhuang13/DenseNet and prlz77/ResNeXt.pytorch