This is a quick implementation of the DenseNet model described in the paper "Densely Connected Convolutional Networks" by Huang et al. (arXiv)

It has only been tested on the Cifar-10 dataset without data augmentation, but it should work fine on any dataset.

The basic model is defined in DenseNet.py.

The scipt cifar10_densenet_classification.py provides an example on how to create and use the model on Cifar-10 classification.

Finally, utils.py contains a few helper functions.

• Keras (>= 2) (only tested with Tensorflow backend)
• numpy (>= 1.13)

Below are the results of running both DenseNet and DenseNet-BC models on Cifar-10 dataset with the same hyperparameters and optimization techniques as in the original paper.

• Add data augmentation techniques
• Try different architectures
• Try other optimizers, eg Adam
• Try out transfer learning on ImageNet