SegNet and Bayesian SegNet Tutorial
This repository contains all the files for you to complete the 'Getting Started with SegNet' and the 'Bayesian SegNet' tutorials here: http://mi.eng.cam.ac.uk/projects/segnet/tutorial.html
Please see this link for detailed instructions.
SegNet requires a modified version of Caffe to run. Please download and compile caffe-segnet to use these models: https://github.com/alexgkendall/caffe-segnet
This version supports cudnn v2 acceleration. @TimoSaemann has a branch supporting a more recent version of Caffe (Dec 2016) with cudnn v5.1: https://github.com/TimoSaemann/caffe-segnet-cudnn5
Getting Started with Live Demo
If you would just like to try out an example model, then you can find the model used in the SegNet webdemo in the folder
Example_Models/. You will need to download the weights separately using the link in the SegNet Model Zoo.
Scripts/webcam_demo.py and edit line 14 to match the path to your installation of SegNet. You will also need a webcam, or alternatively edit line 39 to input a video file instead. To run the demo use the command:
python Scripts/webcam_demo.py --model Example_Models/segnet_model_driving_webdemo.prototxt --weights /Example_Models/segnet_weights_driving_webdemo.caffemodel --colours /Scripts/camvid12.png
Getting Started with Docker
Use docker to compile caffe and run the examples. In order to run caffe on the gpu using docker, please install nvidia-docker (see https://github.com/NVIDIA/nvidia-docker or using ansbile: https://galaxy.ansible.com/ryanolson/nvidia-docker/)
to run caffe on the CPU:
docker build -t bvlc/caffe:cpu ./cpu # check if working docker run -ti bvlc/caffe:cpu caffe --version # get a bash in container to run examples docker run -ti --volume=$(pwd):/SegNet -u $(id -u):$(id -g) bvlc/caffe:cpu bash
to run caffe on the GPU:
docker build -t bvlc/caffe:gpu ./gpu # check if working docker run -ti bvlc/caffe:gpu caffe device_query -gpu 0 # get a bash in container to run examples docker run -ti --volume=$(pwd):/SegNet -u $(id -u):$(id -g) bvlc/caffe:gpu bash
A number of example models for indoor and outdoor road scene understanding can be found in the SegNet Model Zoo.
For more information about the SegNet architecture:
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 software 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/