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

NVIDIA Caffe (NVIDIA Corporation ©2017) is an NVIDIA-maintained fork of BVLC Caffe tuned for NVIDIA GPUs, particularly in multi-GPU configurations.

Custom Caffe distribution with support to train YOLO v2 using Caffe custom layers.

See the Dockerfile

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}
}

This application uses Open Source components. You can find the source code of their open source projects below. We acknowledge and are grateful to these developers for their contributions to open source.

Project: Caffe-YOLOv2 by gklz for the implementation of reorg layer, region layer, and detection loss layer