This branch of Caffe extend Microsoft Caffe by integrating MKL DNN API accelerations and doing many extra OpenMP/Zero-Copy optimizations to make py-RFCN fasterin Intel platform. We also implemented the CPU version of some layers(like PSROI, box annotator etc.). We can get 20x acceleration compared with Vanilla CPU Caffe in Pascal-VOC RFCN end2end case in Xeon E5 2699-v4.
- go to ./external/mkl and run prepare_mkl.sh. Once it's done, you can see a mklml_lnx_2017.0.2.20161122 folder in ./external/mkl directory
- Update BLAS_INCLUDE and BLAS_LIB's absolute path part in Makefile.config
- make -j8
- make pycaffe
This has been tested in CentOS 7.2, suppose no issues in Linux based OS.
Contact: Matrix YAO([email protected])
Linux (CPU) |
Windows (CPU) |
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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.
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}
}