Caffe is a deep learning framework that is supported with interfaces like C, C++, Python, MATLAB as well as the Command Line Interface. It is well known for its speed and transposability and its applicability in modelling Convolution Neural Networks (CNN). The biggest benefit of using Caffe’s C++ library (comes with a Python interface) is accessing available networks from the deep net repository ‘Caffe Model Zoo’ which are pre-trained and can be used immediately. Whether it is modelling CNNs or solving image processing issues, this has got to be the go-to library.

Caffe’s biggest USP is speed. It can process over sixty million images on a daily basis with a single Nvidia K40 GPU. That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions are faster still.

Caffe is a popular deep learning network for vision recognition. However, Caffe does not support fine granularity network layers like those found in TensorFlow or CNTK. Given the architecture, the overall support for recurrent networks and language modeling is quite poor and establishing complex layer types has to be done in low-level language.