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
VGG19_FCN Segmentation is released under the MIT License, you can read the license file included in the reposity for details.
- VGG19_FCN Mean Iou score 68.1 percent VGG19_FCN
Make caffe with python wrapper. Detailed Instruction below
Open demo.py and change line 29 for running demo with different images. Run demoplay.py
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
- DIY Deep Learning for Vision with Caffe
- Tutorial Documentation
- BAIR reference models and the community model zoo
- Installation instructions
and step-by-step examples.
- 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
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!
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}
}