Various caffe forks uses different protobufs due to framework and layer customizations. In order for ACUITY to import from different Caffe forks, we merged several of these customized protobufs together to generate new caffe_pb2.py.
NOTE This branch is only used for generating protobuf, it does not contain layer implementation, so do not use this repo as Caffe framework.
Caffe forks merged
- SSD https://github.com/weiliu89/caffe
- Enet https://github.com/TimoSeamnn/caffe-enet
- Segnet https://github.com/alexgkendall/caffe-segnet
- Faster-RCNN https://github.com/sanghoon/caffe
INSTRUCTIONS Rebuild Caffe and use resulting caffe_pb2.py for ACUITY
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, SKX, 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}
}