https://github.com/alexgkendall/caffe-segnet

Implementation of SegNet: A Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-Wise Labelling
https://github.com/alexgkendall/caffe-segnet

Last synced: 11 days ago
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Implementation of SegNet: A Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-Wise Labelling

• Host: GitHub
• URL: https://github.com/alexgkendall/caffe-segnet
• Owner: alexgkendall
• License: other
• Created: 2015-08-07T15:21:09.000Z (almost 9 years ago)
• Default Branch: segnet-cleaned
• Last Pushed: 2018-03-15T08:10:01.000Z (over 6 years ago)
• Last Synced: 2024-01-14T12:38:04.704Z (5 months ago)
• Language: C++
• Homepage: http://mi.eng.cam.ac.uk/projects/segnet/
• Size: 28.4 MB
• Stars: 1,077
• Watchers: 78
• Forks: 462
• Open Issues: 81
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• Awesome-Caffe - SegNet
• awesome-stars - alexgkendall/caffe-segnet - Implementation of SegNet: A Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-Wise Labelling (C++)
• awesome-android-cpp - alexgkendall/caffe-segnet - Implementation of SegNet: A Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-Wise Labelling (TODO scan for Android support in followings)

README

        
# Caffe SegNet

**This is a modified version of [Caffe](https://github.com/BVLC/caffe) which supports the [SegNet architecture](http://mi.eng.cam.ac.uk/projects/segnet/)**

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]

# Updated Version:

**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 Example Model and Webcam Demo

If you would just like to try out a pretrained example model, then you can find the model used in the [SegNet webdemo](http://mi.eng.cam.ac.uk/projects/segnet/) and a script to run a live webcam demo here:

https://github.com/alexgkendall/SegNet-Tutorial

For a more detailed introduction to this software please see the tutorial here:

http://mi.eng.cam.ac.uk/projects/segnet/tutorial.html

### Dataset

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.

### Net specification

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.

### Training

In solver.prototxt set a path for ```snapshot_prefix```. Then in a terminal run

```./build/tools/caffe train -solver ./examples/segnet/solver.prototxt```

## Publications

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. 

## License

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/