Review: Caffe deep learning conquers image classification

Caffe offers a strong brew for image processing, but the project shows signs of stalling

Like superheroes, deep learning packages usually have origin stories. Yangqing Jia created the Caffe project while earning his doctorate at U.C. Berkeley. The project continues as open source under the auspices of the Berkeley Vision and Learning Center (BVLC), with community contributions. The BVLC is now part of the broader Berkeley Artificial Intelligence Research (BAIR) Lab. Similarly, the scope of Caffe has been expanded beyond vision to include nonvisual deep learning problems, although the published models for Caffe are still overwhelmingly related to images and video.

Caffe is a deep learning framework made with expression, speed, and modularity in mind. Among the promised strengths are the way Caffe’s models and optimization are defined by configuration without hard-coding, as well as the option to switch between CPU and GPU by setting a single flag to train on a GPU machine, then deploy to commodity clusters or mobile devices.

Meanwhile, as we enter 2017, Caffe has been stuck at version 1.0.0 RC 3 for almost a year. While there have been code check-ins and visible progress, the project is still not stable. My experience was marred by installation problems, inability to run Jupyter notebooks, and unanswered requests for help. An outsider might get the impression that the project stalled while the deep learning community moved on to TensorFlow, CNTK, and MXNet.

caffe image classification InfoWorld

Among the Caffe demos is a web-based example of image classification using a convolutional neural network, one of Caffe’s strong suits. The demo works fine on the provided examples, but unfortunately not on any of my own images, even when I reduce them to conform to the size expected.

Caffe features and use cases

To continue reading this article register now