This implementation of SegNet  is built on top of the Caffe deep learning library. The first step is to download the SegNet source code, which can be found on our GitHub repository here. Our code to support SegNet is licensed for non-commercial use (license summary). To install SegNet, please follow the Caffe installation instructions here. Make sure you also compile Caffe’s python wrapper.
Get SegNet Tutorial – https://github.com/alexgkendall/SegNet-Tutorial
Your file structure should now look like this:
/SegNet/ CamVid/ test/ testannot/ train/ trainannot/ test.txt train.txt Models/ # SegNet and SegNet-Basic model files for training and testing Scripts/ compute_bn_statistics.py test_segmentation_camvid.py caffe-segnet/ # caffe implementation
Testing SegNet Live: http://mi.eng.cam.ac.uk/projects/segnet/demo.php#demo
This is a tutorial on Bayesian SegNet , a probabilistic extension to SegNet. By the end of this tutorial you will be able to train a model which can take an image like the one on the left, and produce a segmentation (center) and a measure of model uncertainty (right).
- Caffe Segnet
- Large Scale Visual Recognition Challenge 2016
- NOAA Whale Recognition
Happy machine learning, have fun!!