Caffe2 is a deep learning framework that was open-sourced by Facebook in April 2017. Caffe2 has been explicitly built for large-scale production deployment and for use in a constrained resource environment such as mobile devices.
Caffe2 is an open-source deep 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.
Caffe2 is an open-source deep 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.
