This project was a small test to see if SegNet is able to run on an iPhone. It does work, but the inference takes way too much time and memory. I managed it to run on an iPhone X. The inference needs approximility 4GB of memory.

The repo here is just for anybody who want to do futher tests.

The build system of this repo is based on the project caffe-mobile of @solrex.

This is a modified version of Caffe which supports the SegNet architecture on iOS

As described in SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla, PAMI 2017 [http://arxiv.org/abs/1511.00561]

To compile SegNet, run the following commands:

$ git clone https://github.com/gi097/segnet-ios.git
$ ./tools/build_ios.sh

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." PAMI, 2017.

This modification and 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/