Caffe aims to provide computer vision scientists with a clean, modifiable implementation of state-of-the-art deep learning algorithms. For example, network structure is easily specified in separate config files, with no mess of hard-coded parameters in the code.
At the same time, Caffe fits industry needs, with blazing fast C++/Cuda code for GPU computation. Caffe is currently the fastest GPU CNN implementation publicly available, and is able to process more than 20 million images per day on a single Tesla K20 machine.
Caffe also provides seamless switching between CPU and GPU, which allows one to train models with fast GPUs and then deploy them on non-GPU clusters with one line of code: Caffe::set_mode(Caffe::CPU).
Even in CPU mode, computing predictions on an image takes only 20 ms when images are processed in batch mode.
Caffe was developed by Yangqing Jia and is maintained by the Berkeley Vision and Learning Center.
We provide well-maintained Python and Matlab bindings.
- Changes to previous version:
Initial Announcement on mloss.org.
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