This is a modified version of 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]
Please refer to Alex Kendalls caffe-segnet for tutorial and a guide how to use it (https://github.com/alexgkendall/caffe-segnet).
This repository should work fine when it comes to testing segnet. Training SegNet should work fine as well, but it is not tested yet. If you encounter issues feel free to open an issue or to submit a pull request for fix.
Since the original caffe-segnet supports just cuDNN v2, which is not supported for new pascal based GPUs, it was possible to decrease the inference time by 25 % to 35 % with caffe-segnet-cudnn5 using Titan X Pascal.
I recommend to use my trained weights (CityScapes Model) for semantic segmenation of traffic scenes, which you can find in segnet model zoo: https://github.com/alexgkendall/SegNet-Tutorial/blob/master/Example_Models/segnet_model_zoo.md
If you use this software in your research, please cite their 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. For a commercial license please contact the authors. You can view a license summary here: http://creativecommons.org/licenses/by-nc/4.0/