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Caffe is an open-source deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) at UC Berkeley. It is a highly efficient and popular framework for building and training deep neural networks.

Caffe is written in C++ and designed to be both fast and flexible. It can run on both CPUs and GPUs and supports distributed processing across multiple machines. Caffe also provides a Python interface for ease of use.

One of the key features of Caffe is its modular architecture. It allows users to easily add and customize different layers, loss functions, and optimization methods. This makes it highly adaptable to various types of deep learning tasks, from image classification and object detection to natural language processing and speech recognition.

Caffe has a large and active community of developers and users, with extensive documentation and tutorials available online. Additionally, BVLC provides pre-trained models for a variety of tasks that can be fine-tuned for specific use cases.

Overall, Caffe is a powerful and efficient deep learning framework that is well-suited for a wide range of applications.