UBER Version

• modified to include a HDFS H5 data layer which allows to stream data from HDFS (HADOOP)

    layer {
        name: "data"
        type: "HDFSHDF5Data"
        top: "data"
        top: "label"
        hdfshdf5_data_param {
            source: "/Sparkie/lgueguen/ocr_h5"   #directory in hdfs containing H5 files
            batch_size: 64
            hdfs_config_dir: "/opt/hadoop/latest/etc/hadoop" #directory containing core-site.xml and hdfs-site.xml
            shuffle: true  #tells to shuffle the list of h5 files
        }
    }

• modified to include a CTC distance layer (include pull request: BVLC#4322 )
• to install into an OPUS docker image:

    cd <this cloned directory>
    opus-build-test -R localhost:5000 -i caffe-hdfsgpu-2-0-0 --push -l DEBUG -d test .

Caffe

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!

License and Citation

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}
}