Introduction

This repo contains the code to train and evaluate FC-DenseNets as described in The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. We investigate the use of Densely Connected Convolutional Networks for semantic segmentation, and report state of the art results on datasets such as CamVid.

Installation

You need to install :

• Theano. Preferably the last version
• Lasagne
• The dataset loader (Not yet available)
• (Recommend) The new Theano GPU backend. Compilation will be much faster.

Run experiments

The architecture of the model is defined in FC-DenseNet.py. To train a model, you need to prepare a configuration file (folder config) where all the parameters needed for creating and training your model are precised. DenseNets contain lot of connections making graph optimization difficult for Theano. We strongly recommend to use the flags described further.

To train a model, use the command : THEANO_FLAGS='device=cuda,optimizer=fast_compile,optimizer_including=fusion' python train.py -c config_path -e experiment_name. All the logs of the experiments are stored in the folder experiment_name.

On a Titan X 12GB, for the model FC-DenseNet103 (see folder config), compilation takes around 400 sec and 1 epoch 120 sec for training and 40 sec for validation.

Use a pretrained model

We publish the weights of our model FC-DenseNet103. Metrics claimed in the paper (jaccard and accuracy) can be verified running THEANO_FLAGS='device=cuda,optimizer=fast_compile,optimizer_including=fusion' python test.py