Caffe is an open-source framework for deep learning, designed to provide a clean and modifiable environment for multimedia scientists and practitioners. It is a BSD-licensed C++ library with Python and MATLAB bindings, optimized for efficient training and deployment of convolutional neural networks (CNNs) on commodity architectures, including CUDA GPU computation. Caffe supports large-scale media processing, handling over 40 million images per day on a single GPU. The framework separates model representation from implementation, allowing seamless switching between CPU and GPU modes and easy development and deployment across various platforms. Caffe includes pre-trained reference models for visual tasks, such as AlexNet and R-CNN, and is widely used in research and industrial applications, particularly in vision, speech, robotics, neuroscience, and astronomy. Key features include modularity, test coverage, and support for Python and MATLAB bindings. Caffe has been instrumental in achieving state-of-the-art performance in object classification, semantic feature extraction, and object detection, and is actively maintained by the Berkeley Vision and Learning Center (BVLC) with contributions from an active community.Caffe is an open-source framework for deep learning, designed to provide a clean and modifiable environment for multimedia scientists and practitioners. It is a BSD-licensed C++ library with Python and MATLAB bindings, optimized for efficient training and deployment of convolutional neural networks (CNNs) on commodity architectures, including CUDA GPU computation. Caffe supports large-scale media processing, handling over 40 million images per day on a single GPU. The framework separates model representation from implementation, allowing seamless switching between CPU and GPU modes and easy development and deployment across various platforms. Caffe includes pre-trained reference models for visual tasks, such as AlexNet and R-CNN, and is widely used in research and industrial applications, particularly in vision, speech, robotics, neuroscience, and astronomy. Key features include modularity, test coverage, and support for Python and MATLAB bindings. Caffe has been instrumental in achieving state-of-the-art performance in object classification, semantic feature extraction, and object detection, and is actively maintained by the Berkeley Vision and Learning Center (BVLC) with contributions from an active community.