Keras Implementation of The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation by (Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, Yoshua Bengio)

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation:

Work In Progress, Results can't be replicated yet with the models here

• UPDATE: April 28th: Skip_Connection added thanks to the reviewers, check model model-tiramasu-67-func-api.py

feel free to open issues for suggestions:)

• Keras2 + TF used for the recent updates, which might cause with some confilict from previous version I had in here

What is The One Hundred Layers Tiramisu?

• A state of art (as in Jan 2017) Semantic Pixel-wise Image Segmentation model that consists of a fully deep convolutional blocks with downsampling, skip-layer then to Upsampling architecture.
• An extension of DenseNets to deal with the problem of semantic segmentation.

Fully Convolutional DensNet = (Dense Blocks + Transition Down Blocks) + (Bottleneck Blocks) + (Dense Blocks + Transition Up Blocks) + Pixel-Wise Classification layer

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation (Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, Yoshua Bengio) arXiv:1611.09326 cs.CV

Requirements:

• Keras==2.0.2
• tensorflow-gpu==1.0.1
• or just go ahead and do: pip install -r requirements.txt

Model Strucure:

• DenseBlock: BatchNormalization + Activation [ Relu ] + Convolution2D + Dropout

• TransitionDown: BatchNormalization + Activation [ Relu ] + Convolution2D + Dropout + MaxPooling2D

• TransitionUp: Deconvolution2D (Convolutions Transposed)

Model Params:

• RMSprop is used with Learnining Rete of 0.001 and weight decay 0.995
  • However, using those got me nowhere, I switched to SGD and started tweaking the LR + Decay myself.
• There are no details given about BatchNorm params, again I have gone with what the Original DenseNet paper had suggested.
• Things to keep in mind perhaps:
  • the weight inti: he_uniform (maybe change it around?)
  • the regualzrazation too agressive?

Repo (explanation):

• Download the CamVid Dataset as explained below:
  • Use the data_loader.py to crop images to 224, 224 as in the paper implementation.
• run model-tiramasu-67-func-api.py or python model-tirmasu-56.py for now to generate each models file.
• run python train-tirmasu.py to start training:
  • Saves best checkpoints for the model and data_loader included for the CamVidDataset
• helper.py contains two methods normalized and one_hot_it, currently for the CamVid Task

Dataset:

1. In a different directory run this to download the dataset from original Implementation.

   • git clone [email protected]:alexgkendall/SegNet-Tutorial.git
   • copy the /CamVid to here, or change the DataPath in data_loader.py to the above directory

2. The run python data_loader.py to generate these two files:

   • /data/train_data.npz/ and /data/train_label.npz
   • This will make it easy to process the model over and over, rather than waiting the data to be loaded into memory.

• Experiments:

Models	Acc	Loss	Notes
FC-DenseNet 67			150 Epochs, RMSPROP

To Do:

[x] FC-DenseNet 103
[x] FC-DenseNet 56
[x] FC-DenseNet 67
[ ] Replicate Test Accuracy CamVid Task
[ ] Replicate Test Accuracy GaTech Dataset Task
[ ] Requirements

• Original Results Table: