{"id":29316,"date":"2022-03-30T11:10:07","date_gmt":"2022-03-30T11:10:07","guid":{"rendered":"https:\/\/www.devopsschool.com\/blog\/?p=29316"},"modified":"2022-12-23T06:20:00","modified_gmt":"2022-12-23T06:20:00","slug":"what-is-caffe-and-how-it-works-an-overview-and-its-use-cases-2","status":"publish","type":"post","link":"https:\/\/www.devopsschool.com\/blog\/what-is-caffe-and-how-it-works-an-overview-and-its-use-cases-2\/","title":{"rendered":"What is Caffe and How it works? An Overview and Its Use Cases"},"content":{"rendered":"

History & Origin of Caffe<\/h3>\n

Deep learning is the new big trend in machine learning. It had many recent successes in computer vision, automatic speech recognition and natural language processing.<\/p>\n

The goal of this blog post is to give you a hands-on introduction to deep learning. To do this, we will build a Cat\/Dog image classifier using a deep learning algorithm called convolutional neural network (CNN) and a\u00a0Kaggle dataset.<\/p>\n

This post is divided into 2 main parts. The first part covers some core concepts behind deep learning, while the second part is structured in a hands-on tutorial format.<\/p>\n

In the first part of the hands-on tutorial (section 4), we will build a Cat\/Dog image classifier using a convolutional neural network from scratch. In the second part of the tutorial (section 5), we will cover an advanced technique for training convolutional neural networks called transfer learning. We will use some Python code and a popular open source deep learning framework called Caffe to build the classifier. Our classifier will be able to achieve a classification accuracy of 97%.<\/p>\n

By the end of this post, you will understand how convolutional neural networks work, and you will get familiar with the steps and the code for building these networks.<\/p>\n

What is Caffe<\/h3>\n

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors.\u00a0Yangqing Jia\u00a0created the project during his PhD at UC Berkeley. Caffe is released under the\u00a0BSD 2-Clause license.<\/p>\n

Expressive architecture<\/strong>\u00a0encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.<\/p>\n

Extensible code<\/strong>\u00a0fosters active development. In Caffe\u2019s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.<\/p>\n

Speed<\/strong>\u00a0makes Caffe perfect for research experiments and industry deployment. Caffe can process\u00a0over 60M images per day<\/strong>\u00a0with a single NVIDIA K40 GPU*. That\u2019s 1 ms\/image for inference and 4 ms\/image for learning and more recent library versions and hardware are faster still. We believe that Caffe is among the fastest convnet implementations available.<\/p>\n

How Caffe works aka Caffe architecture?<\/h3>\n

Classification using a machine learning algorithm has 2 phases:<\/p>\n