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Caffe2 Quick Start Guide

You're reading from Caffe2 Quick Start Guide Modular and scalable deep learning made easy

Product type Paperback

Published in May 2019

Publisher Packt

ISBN-13 9781789137750

Length 136 pages

Edition 1st Edition

Languages

C++

Tools

Caffe

Concepts

Deep Learning

Author (1):

Ashwin Nanjappa

View More author details

Table of Contents (9) Chapters

Preface

1. Introduction and Installation

2. Composing Networks FREE CHAPTER

3. Training Networks

4. Working with Caffe

5. Working with Other Frameworks

6. Deploying Models to Accelerators for Inference

7. Caffe2 at the Edge and in the cloud

8. Other Books You May Enjoy

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Working with Caffe

In Chapter 2, Composing Networks, and Chapter 3, Training Networks, we learned how to compose networks and train them, respectively. In this chapter, we will examine the relationship between Caffe2 and Caffe and look at how to use Caffe models in Caffe2 and vice versa.

The objectives of this chapter are as follows:

The relationship between Caffe and Caffe2
Introduction to AlexNet
Building and installing Caffe
Caffe model file formats
Caffe2 model file formats
Converting a Caffe model to Caffe2
Converting a Caffe2 model to Caffe

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Authors (1)

Ashwin Nanjappa

Ashwin Nanjappa

Ashwin Nanjappa is a senior architect at NVIDIA, working in the TensorRT team on improving deep learning inference on GPU accelerators. He has a PhD from the National University of Singapore in developing GPU algorithms for the fundamental computational geometry problem of 3D Delaunay triangulation. As a post-doctoral research fellow at the BioInformatics Institute (Singapore), he developed GPU-accelerated machine learning algorithms for pose estimation using depth cameras. As an algorithms research engineer at Visenze (Singapore), he implemented computer vision algorithm pipelines in C++, developed a training framework built upon Caffe in Python, and trained deep learning models for some of the world's most popular online shopping portals.

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