This is a fork of Caffe that enables training of heatmap regressor ConvNets for the general problem of regressing (x,y) positions in images.
Multiple implementations are provided, using the PyCaffe interface for Python3, the MatCaffe interface for Matlab, and the standard C++ Caffe API.

• Fusion model trained on FLIC
• Fusion model trained on ChaLearn

• Training
• Testing
• Note these files require multfact=282 in both training and testing data layers

For example codes for running the FLIC model on a video, use the following:
demo.m (on Matlab version 2015 or earlier)
python python/pose/demo.py
./build/examples/cpp_heatmap/demo.bin (generated automatically on running "make")

Please cite our ICCV'15 paper in your publications if this code helps your research:

  @InProceedings{Pfister15a,
    author       = "Pfister, T. and Charles, J. and Zisserman, A.",
    title        = "Flowing ConvNets for Human Pose Estimation in Videos",
    booktitle    = "IEEE International Conference on Computer Vision",
    year         = "2015",
  }

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!

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}
}