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Caffe2 Quick Start Guide

You're reading from  Caffe2 Quick Start Guide

Product type Book
Published in May 2019
Publisher Packt
ISBN-13 9781789137750
Pages 136 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Ashwin Nanjappa Ashwin Nanjappa
Profile icon Ashwin Nanjappa
LinkedIn
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.
See other products by Ashwin Nanjappa

Table of Contents (9) Chapters

Preface 1. Introduction and Installation 2. Composing Networks 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

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)

Profile icon Ashwin Nanjappa
LinkedIn
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.
Read more
See other products by Ashwin Nanjappa

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