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You're reading from  Caffe2 Quick Start Guide

Product typeBook
Published inMay 2019
Reading LevelBeginner
PublisherPackt
ISBN-139781789137750
Edition1st Edition
Languages
C++
Tools
Caffe
Concepts
Deep Learning
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Author (1)
Ashwin Nanjappa
Ashwin Nanjappa
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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.
Read more about Ashwin Nanjappa

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Summary

In this chapter, we introduced the Caffe deep learning framework and examined the relationship between Caffe and Caffe2. We examined the Caffe and Caffe2 model file formats. Using AlexNet as an example network, we looked at how to convert a Caffe model to Caffe2 format. Finally, we looked at the difficulties in converting a Caffe2 model to Caffe.

Caffe is a DL framework that has reached its end of life and no new features are being added to it. In the next chapter, we will look at contemporary DL frameworks, such as TensorFlow and PyTorch, and see how we can exchange models to and from Caffe2 and these other frameworks.

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Caffe2 Quick Start Guide
Published in: May 2019Publisher: PacktISBN-13: 9781789137750
© 2019 Packt Publishing Limited All Rights Reserved

Author (1)

author image
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.
Read more about Ashwin Nanjappa

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