Work In Progress, Results can't be replicated yet with the model
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
- Keras==2.0.2
- tensorflow-gpu==1.0.1
- or just go ahead and do:
pip install -r requirements.txt
-
DenseBlock:
BatchNormalization
+Activation [ Relu ]
+Convolution2D
+Dropout
-
TransitionDown:
BatchNormalization
+Activation [ Relu ]
+Convolution2D
+Dropout
+MaxPooling2D
-
TransitionUp:
Deconvolution2D
(Convolutions Transposed)
- Download the CamVid Dataset as explained below:
- Use the
data_loader.py
to crop images to224, 224
as in the paper implementation.
- Use the
- run
python model-tirmasu-103.py
orpython 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 theCamVidDataset
- Saves best checkpoints for the model and
helper.py
contains two methodsnormalized
andone_hot_it
, currently for the CamVid Task
-
In a different directory run this to download the dataset from original Implementation.
git clone git@github.com:alexgkendall/SegNet-Tutorial.git
- copy the
/CamVid
to here, or change theDataPath
indata_loader.py
to the above directory
-
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 | 100 Epochs, SGD |
[x] FC-DenseNet 103
[x] FC-DenseNet 56
[x] FC-DenseNet 67
[ ] Replicate Test Accuracy CamVid Task
[ ] Replicate Test Accuracy GaTech Dataset Task
[ ] Requirements