# Caffe2

Caffe is an open-source deep learning framework developed at UC Berkeley, known for its speed and modularity, and later merged into PyTorch. It supports convolutional neural networks and other architectures for image classification and segmentation.

Caffe (Convolutional Architecture for Fast Feature Embedding) is an open-source deep learning framework originally developed at the University of California, Berkeley. It is written in C++ with a Python interface and is designed for speed and modularity, making it popular for computer vision tasks. The framework was later merged into PyTorch, but its legacy continues in research and industry.

## History

Caffe was created by Yangqing Jia during his PhD at UC Berkeley, while working in the lab of Trevor Darrell. The first version, initially called "DeCAF," appeared in Spring 2013 and was used for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). The project was renamed to Caffe and released publicly in December 2013. It gained widespread adoption due to its clean architecture and efficient GPU support. In 2017, Facebook (now Meta) announced Caffe2, a rewritten version with improved mobile and production capabilities. In March 2018, Caffe2 was merged into PyTorch, and development of the original Caffe ceased, with end-of-support announced in 2018.

## Features

Caffe supports a variety of deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and fully connected networks. It provides GPU- and CPU-based acceleration through libraries such as NVIDIA cuDNN and Intel Math Kernel Library (MKL). The framework uses a model definition format based on protocol buffers, allowing easy configuration and experimentation. Caffe also includes a large model zoo with pre-trained networks for tasks like image classification and object detection.

## Applications

Caffe has been used in academic research, startup prototypes, and large-scale industrial applications. It has been applied to computer vision tasks such as image classification, segmentation, and object detection, as well as speech and multimedia processing. Yahoo! integrated Caffe with Apache Spark to create CaffeOnSpark, a distributed deep learning framework for large-scale data processing. The framework's speed and modularity made it a preferred choice for many early deep learning projects.

## Caffe2 and Merger with PyTorch

Caffe2, announced in April 2017, introduced new features including support for recurrent neural networks and improved mobile deployment. It was designed for production environments with a focus on performance and scalability. In early 2018, Facebook merged Caffe2 with PyTorch, combining Caffe2's production capabilities with PyTorch's flexible research interface. The merged project continues as PyTorch, which remains one of the leading deep learning frameworks as of 2025.

## See Also

- [deep-learning](https://www.wikiprompt.org/wiki/deep-learning)
- [convolutional-neural-network](https://www.wikiprompt.org/wiki/convolutional-neural-network)
- [pytorch](https://www.wikiprompt.org/wiki/pytorch)
- [computer-vision](https://www.wikiprompt.org/wiki/computer-vision)
- open-source-software

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Source: https://www.wikiprompt.org/wiki/caffe2
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-08T15:33:46.094328+00:00