Deep Learning

Caffe – deep learning framework by BAIR

Caffe (Convolutional Architecture for Fast Feature Embedding) is an open source deep learning framework, originally developed at UC Berkeley.

The software is developed with cleanliness, readability, and speed in mind. Caffe powers academic research projects, startup prototypes, and large-scale industrial applications in vision, speech, and multimedia. The software has been adopted by many large corporations including Facebook, NVIDIA, Intel, Sony, Yahoo! Japan, Samsung, Adobe, A9, Siemens, Pinterest, the Embedded Vision Alliance, and more.

The software can process over 60M images per day with a single NVIDIA K40 GPU.

The next generation of the framework – Caffe2 – is under development.

Features include:

  • Fast, well-tested code.
  • Models and optimization are defined by configuration without hard-coding. Seamless switching between CPU and GPU.
  • Supports many different types of deep learning architectures geared towards image classification and image segmentation.
  • Supports GPU- and CPU-based acceleration computational kernel libraries such as NVIDIA cuDNN and Intel MKL
  • Supports CNN, RCNN, LSTM and fully connected neural network designs.
  • Provides model definitions, optimization setting, with pre-trained models. Caffe comes with models that are set up to use images from the ILSVRC12 challenge.
  • Offers command line, Python, and MATLAB interfaces for day-to-day usage, interfacing with research code, and rapid prototyping:
    • The command line interface – cmdcaffe – is the caffe tool for model training, scoring, and diagnostics.
    • The Python interface – pycaffe – is the caffe module.
    • The MATLAB interface – matcaffe – is the caffe package which you can integrate Caffe in Matlab code.
  • Extensible code – good community support.

Support: Mailing List, GitHub Code Repository
Developer: Berkeley AI Research (BAIR)
License: BSD 2-Clause license

Caffe needs the following software:

  • CUDA is required for GPU mode:
    • library version 7+ and the latest driver version are recommended, but 6.* also works.
    • 5.5, and 5.0 are compatible but considered legacy.
  • BLAS via ATLAS, MKL, or OpenBLAS.
  • Boost >= 1.55.
  • protobuf, glog, gflags, hdf5.

Optional dependencies:

  • OpenCV >= 2.4 including 3.0.
  • IO libraries: lmdb, leveldb (leveldb requires snappy).
  • cuDNN for GPU acceleration (v7).

Caffe is written in C++, with a Python interface. Learn C++ with our recommended free books and free tutorials.

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