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.
NVIDIA Caffe (NVIDIA Corporation ©2017) is an NVIDIA-maintained fork of BVLC Caffe tuned for NVIDIA GPUs, particularly in multi-GPU configurations.
Xilinx Caffe (Xilinx Corporation ©2019) is an XILINX-maintained fork of NVIDIA Caffe from branch caffe-0.15. Xilinx Caffe support FPGA friendly model quantization. After quantization, models can be deployed to FPGA devices. Xilinx Caffe is a component of Xilinx Vitis AI, which is Xilinx’s development stack for AI inference on Xilinx hardware platforms.
Build procedure is the same as on bvlc-caffe-master branch. Both Make and CMake can be used. OpenMP will be used automatically if available.
Training and testing procedures are the same as on bvlc-caffe-master branch. For quantization, refer to Vitis AI.
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}
}