Caffe deep learning framework

http://caffe.berkeleyvision.org/

Caffe

Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind. It was created by Yangqing Jia during his PhD at UC Berkeley, and is in active development by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Caffe is released under the BSD 2-Clause license.
Check out our web image classification demo!

Why use Caffe?

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 year, it has been forked by over 600 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.

* When files are properly cached, and using the ILSVRC2012-winning SuperVision model. Consult performance details.