Description:

Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind.

Clean architecture enables rapid deployment. Networks are specified in simple config files, with no hard-coded parameters in the code. Switching between CPU and GPU is as simple as setting a flag – so models can be trained on a GPU machine, and then used on commodity clusters.

Readable & modifiable implementation fosters active development. In Caffe’s first six months, it has been forked by over 300 developers on Github, and many have pushed significant changes.

Speed makes Caffe perfect for industry use. Caffe can process over 40M images per day with a single NVIDIA K40 or Titan GPU*. That’s 5 ms/image in training, and 2 ms/image in test. We believe that Caffe is the fastest CNN implementation available.

Community: Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. There is an active discussion and support community on Github.

Changes to previous version:

LOTS of stuff: https://github.com/BVLC/caffe/releases/tag/v0.9999

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Unix, Osx

Data Formats: Agnostic

Tags: Deep Learning, Computer Vision, Pattern Recognition, Convolutional Nets

Archive: download here

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