Caffe2: Getting Started

Learning framework and competitor to frameworks such as TensorFlow, Apache MXNet and PyTorch

It's focus is on efficiency and works well with constrained environments such as on mobile devices

In this course, Caffe2 Getting Started, you'll learn the fundamentals of building neural nets and working with Caffe2, get introduced to the Caffe2 Model Zoo and see how you can import models from PyTorch to Caffe2 using ONNX

First, you'll discover the basic building blocks of Caffe2, blobs and workspaces, nets and operators, and put those together to build neural networks to perform tasks such as regression and classification

Then, you'll get introduced to common image pre-processing techniques and the Caffe2 Model Zoo which offers a wide variety of pre-trained models for common use cases

Next, you'll focus on interoperability between the PyTorch deep learning framework and Caffe2 using ONNX, an open source framework for exporting models from one framework to another

Last, you'll use ONNX to move a super-resolution model from PyTorch to Caffe2

By the end of this course, you should be comfortable building and executing neural networks using Caffe2, using pre-trained models for common tasks and using ONNX to move from one framework to another