Caffe: Convolutional Architecture for Fast Feature Embedding

July 10, 2018, 5:24 p.m. By: Kirti Bakshi


Caffe, for state-of-the-art Deep Learning algorithms and a collection of reference models, provides multimedia scientists and practitioners with a framework that is clean and modifiable and is a BSD-licensed C++ library with Python and MATLAB bindings. Caffe by CUDA GPU computation fits the needs of industry as well as internet-scale media, processing on a single K40 or Titan GPU over 40 million images a day.
Caffe, by separating model representation from actual implementation for ease of development and deployment from prototyping machines to cloud environments allows experimentation and seamless switching among platforms. Caffe with the help of an active community of contributors on GitHub is maintained and developed by the Berkeley Vision and Learning Center (BVLC). It powers in vision, speech, and multimedia ongoing research projects, startup prototypes and large-scale industrial applications.


The discovery of effective representations for sensory inputs is a key problem in multimedia data analysis. While in recent years, the performance of conventional, handcrafted features has plateaued in deep compositional architectures new developments have kept performance levels rising. Deep models in many domains have outperformed hand-engineered feature representations. The aim motivation here is by large-scale visual recognition. These Convolutional Neural Networks, or CNN's, are discriminatively trained via back-propagation through layers of Convolutional filters and other operations such as rectification and pooling.

These models have also recently surpassed all known methods for large-scale visual recognition, and for image understanding and search have been adopted by industry heavyweights such as Google, Facebook, and Baidu. While deep neural networks within computer vision and further have attracted enthusiastic interest, replication of published results by a researcher or engineer can involve months of work. Sometimes researchers deem it worthwhile to release trained models advertising their performance along with the paper.

Caffe, A Fully Open-source Framework:

But trained models alone are not sufficient for rapid research progress and emerging commercial applications and in order to address such problems, you are presented Caffe, written in clean, efficient C++, with CUDA used for GPU computation, and nearly complete, well-supported bindings to Python/Numpyand MATLAB that to deep architectures affords clear access. Caffe, by providing unit tests for correctness and experimental rigour and speed for deployment adheres to software engineering best practices.
It, due to the careful modularity of the code, and the clean separation of the network definition from actual implementation is also well-suited for research use. Further, Caffe can even be run in the cloud. While first Caffe was designed for vision, it has been improved and adopted by users in neuroscience, astronomy, speech recognition, as well as robotics.

Highlights Of Caffe:

Caffe, with well-documented examples for all of the tasks, provides a complete toolkit for training, testing, fine-tuning, and deploying models. As such, it for researchers and other developers that are looking to jump into state-of-the-art Machine Learning is an ideal starting point.

  • Modularity.

  • Separation of representation and implementation.

  • Test coverage.

  • Python and MATLAB bindings.

  • Pre-trained reference models.

Comparison to related software:

They summarize the landscape of Convolutional neural network software used in recent publications in Table 1 under this section. While their list is incomplete, they included the toolkits that are most notable. Caffe, in two major ways, differs from other CNN frameworks:

  • It has a C++ based implementation, which into existing C++ systems and interfaces common in industry eases integration. The CPU mode once a model is trained removes the barrier of specialized hardware for deployment and experiments.

  • Reference models, without the need for costly re-learning, are provided off-the-shelf for quick experimentation with state-of-the-art results.

Applications And Examples:

Since its public release, Caffe, in its first six months has already been used in a large number of research projects at UC Berkeley and other universities, achieving on a number of tasks state-of-the-art performance.

  • Object Classification

  • Learning Semantic Features

  • Object Detection

  • Beginners’ Guides

This documentation, in the near future, will be exposed in a standalone web interface. For detailed information regarding the Applications and examples one can go through the link mentioned below:

Source And Information: GitHub

Link To The PDF: Click Here