Data
Reader small image

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
Right arrow
Author (1)
Ashwin Nanjappa
Ashwin Nanjappa
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

Right arrow

Converting a Caffe2 model to Caffe

In the previous sections in this chapter, we focused on how to convert a Caffe model to a Caffe2 model. Since Caffe is not being actively developed now, and Caffe2 was, in part, created to supersede Caffe2, this path of migrating a Caffe model to Caffe2 is what the majority of users are interested in.

However, if you need to use a Caffe2 model in Caffe, then that process is bound to be more arduous. There does not seem to be any direct way to convert a Caffe2 model to Caffe. If you are sure that the Caffe2 operators and their arguments are fully supported in Caffe, then you could try going through an intermediary format such as ONNX (see Chapter 5, Working with Other Frameworks).

If the ONNX route is not feasible, then you might have to resort to executing the following tasks manually:

  1. Export Caffe2 operators, arguments, and weights of the model...
lock icon
The rest of the page is locked
Previous Page
Chapter 5 of 9, Page 8
Next Page
You have been reading a chapter from
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

Other recommended products

Related to this chapter

Generative Adversarial Networks Cookbook

Generative Adversarial Networks Cookbook

Generative Adversarial Networks have opened up many new possibilities in the machine learning domain. This book is all you need to implement different types of GANs using TensorFlow and Keras, in order to provide optimized and efficient deep learning solutions.

BookDec 2018268 pages
CUDA Cookbook

CUDA Cookbook

This book is for programmers who want to delve into parallel computing, become part of the high-performance computing community and apply those techniques to build modern applications. Experience with C++ programming is assumed. There are some sample examples on equivalent Fortran code. For Deep Learning enthusiasts python based sample code is also provided.

BookSep 2019508 pages
Mastering Computer Vision with TensorFlow 2.x

Mastering Computer Vision with TensorFlow 2.x

You will learn the principles of computer vision and deep learning, and understand various models and architectures with their pros and cons. You will learn how to use TensorFlow 2.x to build your own neural network model and apply it to various computer vision tasks such as image acquiring, processing, and analyzing.

BookMay 2020430 pages
Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

Cognitive Toolkit is one of the most popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks.

BookMar 2019208 pages
PyTorch Deep Learning Hands-On

PyTorch Deep Learning Hands-On

PyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide to doing deep learning work with Pytorch. It is not an academic textbook and does not try to teach deep learning principles. The book will help you most if you want to get your hands dirty and put PyTorch to work quickly.

BookApr 2019250 pages
Hands-On Machine Learning with C++

Hands-On Machine Learning with C++

This book will help you explore how to implement different well-known machine learning algorithms with various C++ frameworks and libraries. You will cover basic to advanced machine learning concepts with practical and easy to follow examples. By the end of the book, you will be able to build various machine learning models with ease.

BookMay 2020530 pages
Deep Learning with TensorFlow

Deep Learning with TensorFlow

Machine learning is concerned with algorithms for transforming data into actionable intelligence and predictive analytics. Deep learning is a branch of machine learning based on multiple levels of representations. This book introduces the core concepts of deep learning using the latest version of TensorFlow to get implementation and research details on cutting-edge architectures. You will learn deep learning with the hands-on model building, data collection and transformation and even more!

BookApr 2017320 pages
PyTorch Artificial Intelligence Fundamentals

PyTorch Artificial Intelligence Fundamentals

In this book, you will start from the basics of tensor manipulation to all the way releasing your deep learning model to production. Using hands-on recipes you will learn to build deep learning applications and visualize the model performance. It teaches you about CNNs, RNNs, GANs and deep reinforcement learning with Pytorch.

BookFeb 2020200 pages
Practical Convolutional Neural Networks

Practical Convolutional Neural Networks

This book helps you master CNN, from the basics to the most advanced concepts in CNN such as GANs, instance classification and attention mechanism for vision models and more. You will implement advanced CNN models using complex image and video datasets. By the end of the book you will learn CNN’s best practices to implement smart ConvNet models and apply them to solve complex deep learning problems.

BookFeb 2018218 pages
Hands-On Computer Vision with TensorFlow 2

Hands-On Computer Vision with TensorFlow 2

Computer vision is achieving a new frontier of capabilities in fields like health, automobile or robotics. This book explores TensorFlow 2, Google's open-source AI framework, and teaches how to leverage deep neural networks for visual tasks. It will help you acquire the insight and skills to be a part of the exciting advances in computer vision.

BookMay 2019372 pages
Deep Learning with TensorFlow

Deep Learning with TensorFlow

This book introduces the core concepts of deep learning. Get implementation and research details on cutting-edge architectures and apply advanced concepts to your own projects. Develop your knowledge of deep neural networks through hands-on model building and examples of real-world data collection.

BookMar 2018484 pages
Deep Learning Essentials

Deep Learning Essentials

Deep Learning is one of the trending topics in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning. This book will help you take your first steps when it comes to training efficient deep learning models, and apply them in various practical scenarios. You will model, train and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and see their applications in real-world domains such as computer vision, natural language processing, and speech recognition. This book also covers solutions to tackle different problems you might come across while training your models and ensure their high performance. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.

BookJan 2018284 pages3

Personalised recommendations for you

Based on your interests and search pattern

Et al.

Et al.

Ever wonder why speech recognition systems don't understand the Scottish accent, or what would happen if an astronaut only ate mac 'n' cheese, or other spurious reflections you'd have at a bar? We did, then collated those deliberations into absurd research articles with fake figures and methodologies inspired by even more fictionally absurd studies.

BookAug 2023230 pages5
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages4
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages5
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages1
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages5
Mastering Tableau 2023

Mastering Tableau 2023

This book is a comprehensive resource to mastering your Tableau skills and becoming a BI expert. As you progress, you will learn how to build advanced dashboards and improve your storytelling to derive key business insight, as well as make you well-versed with advanced functionalities of Tableau in the business intelligence domain.

BookAug 2023684 pages
Building AI Applications with ChatGPT APIs

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

BookSep 2023258 pages5
Building AI Applications with ChatGPT APIs

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

BookSep 2023258 pages2
Data Engineering with AWS

Data Engineering with AWS

Embark on a journey to master data engineering pipelines on AWS! Our book offers a hands-on experience of AWS services for ingesting, transforming, and consuming data. Whether you're an absolute beginner or someone with basic data engineering experience, this guide is an indispensable resource.

BookOct 2023636 pages5
Modern Data Architecture on AWS

Modern Data Architecture on AWS

Every organization wants an agile, performant, and cost-effective data platform that meets all their current and future business needs. Purpose-built AWS analytics services and their features play a big part in building such a modern data platform. This book brings to you all the design and architectural patterns that’ll help you achieve this goal.

BookAug 2023420 pages5
Practical Guide to Applied Conformal Prediction in Python

Practical Guide to Applied Conformal Prediction in Python

Discover the power of Conformal Prediction with the "Practical Guide to Applied Conformal Prediction in Python." Master the latest techniques to quantify uncertainty in machine learning and computer vision models, and seamlessly apply them to your industry applications.

BookDec 2023240 pages
TinyML Cookbook

TinyML Cookbook

With over 70 project-based recipes, the TinyML Cookbook is a practical guide that will help you to get the most out of your microcontrollers. It provides a comprehensive understanding of the theoretical foundations while giving you hands-on experience training ML models for deployment on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano microcontrollers.

BookNov 2023664 pages