This is a modified version of caffe(https://github.com/BVLC/caffe) which supports the SegNet architecture
As described in SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla [http://arxiv.org/abs/1511.00561]
For a detailed introduction to this software please see the tutorial here:
http://mi.eng.cam.ac.uk/projects/segnet/tutorial.html
Prepare a text file of space-separated paths to images (jpegs or pngs) and corresponding label images alternatively e.g. /path/to/im1.png /another/path/to/lab1.png /path/to/im2.png /path/lab2.png ...
Label images must be single channel, with each value from 0 being a separate class. The example net uses an image size of 360 by 480.
Example net specification and solver prototext files are given in examples/segnet.
To train a model, alter the data path in the data layers in net.prototxt to be your dataset.txt file (as described above).
In the last convolution layer, change num_output to be the number of classes in your dataset.
In solver.prototxt set a path for snapshot_prefix. Then in a terminal run
./build/tools/caffe train -solver ./examples/segnet/solver.prototxt
Though in the paper SegNet is trained with a layer-wise LBFGS method, here we train all layers simulataneously using ADAGRAD.
If you use this software in your research, please cite our publications:
http://arxiv.org/abs/1511.02680 Alex Kendall, Vijay Badrinarayanan and Roberto Cipolla "Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding." arXiv preprint arXiv:1511.02680, 2015.
http://arxiv.org/abs/1511.00561 Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation." arXiv preprint arXiv:1511.00561, 2015.
This extension to the Caffe library is released under a creative commons license which allows for personal and research use only. You can view a license summary here: http://creativecommons.org/licenses/by/3.0/deed.en_US