#Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation

This work shows how to back propagate Structured SVM to Convolutional Neural Network.

LICENSE: Free for everything. THis is the public work. Please cite: ( to be available ) http://www.sciencedirect.com/science/article/pii/S0893608017300321

Installation

1. Install and Compile Caffe
2. cd PARSE_createDb_and_CheckResults
3. matlab
4. in Matlab prompt: run_pycaffe_HOGssvm26_lmdb_preparation.m
5. Close Matlab, cd $ROOTDIR
6. ./examples/PARSE_HogConvssvm26/check_model_mat.sh
7. python examples/PARSE_HogConvssvm26/python_create_lmdb.py
8. python examples/PARSE_HogConvssvm26/python_train.py

Caffe

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by 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
• BVLC reference models and the community model zoo
• Installation instructions

and step-by-step examples.

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 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}
}