VGG19_FCN Semantic Segmentation

Co-authored by Sharif Amit Kamran, Md. Asif Bin Khaled and Sabit Bin Kabir under the supervision of Muhammad Hasan.
http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=6#KEY_VGG19_FCN

License

VGG19_FCN Segmentation is released under the MIT License, you can read the license file included in the reposity for details.

Score and Leaderboard

VGG19_FCN Mean Iou score 68.1 percent VGG19_FCN

Installation

Make caffe with python wrapper. Detailed Instruction below

Demo

Open demo.py and change line 29 for running demo with different images.
Run demoplay.py

Caffe

Caffe is a deep learning framework made with expression, speed, and modularity in mind.
It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

Check out the project site for all the details like

and step-by-step examples.

Custom distributions

Intel Caffe (Optimized for CPU and support for multi-node), in particular Xeon processors (HSW, BDW, Xeon Phi).
- OpenCL Caffe e.g. for AMD or Intel devices.
- Windows Caffe

Community

Please join the caffe-users group or gitter chat to ask questions and talk about methods and models.
Framework development discussions and thorough bug reports are collected on Issues.

Happy brewing!

License and Citation

Caffe is released under the BSD 2-Clause license.
The BAIR/BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}