Caffe is a framework for state-of-the-art deep learning algorithms and a collection of reference models. It is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying convolutional neural networks (CNNs) efficiently on commodity architectures. Caffe uses CUDA GPU computation to process over 40 million images per day on a single GPU. It allows experimentation and seamless switching between platforms for development and deployment. Caffe is maintained by the Berkeley Vision and Learning Center (BVLC) with contributions from an active community. It powers research, industrial applications, and startup prototypes in vision, speech, and multimedia. Caffe provides a complete toolkit for training, testing, finetuning, and deploying models. It is modular, allowing easy extension to new data formats, network layers, and loss functions. It separates model representation from implementation, enabling fast and efficient development. Caffe has well-documented examples and provides pre-trained reference models for visual tasks, including AlexNet and R-CNN. It supports both CPU and GPU modes, and can be run in the cloud. Caffe is designed for vision but has been adopted in speech recognition, robotics, neuroscience, and astronomy. It is open-source and welcomes contributions. Caffe's architecture includes data storage in 4-dimensional arrays called blobs, and layers that perform operations like convolution, pooling, and nonlinearities. Networks are end-to-end systems, with data layers loading data and loss layers computing objectives. Training uses stochastic gradient descent, and finetuning allows adaptation to new tasks. Caffe has been used in numerous research projects and by industry partners like Facebook and Adobe. It enables state-of-the-art object classification, detection, and semantic feature extraction. It has applications in open-vocabulary object retrieval and image style recognition. Caffe is available on GitHub and has a web interface for documentation. It is licensed under BSD and is available on Amazon EC2. Caffe is acknowledged for GPU donations and community contributions.Caffe is a framework for state-of-the-art deep learning algorithms and a collection of reference models. It is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying convolutional neural networks (CNNs) efficiently on commodity architectures. Caffe uses CUDA GPU computation to process over 40 million images per day on a single GPU. It allows experimentation and seamless switching between platforms for development and deployment. Caffe is maintained by the Berkeley Vision and Learning Center (BVLC) with contributions from an active community. It powers research, industrial applications, and startup prototypes in vision, speech, and multimedia. Caffe provides a complete toolkit for training, testing, finetuning, and deploying models. It is modular, allowing easy extension to new data formats, network layers, and loss functions. It separates model representation from implementation, enabling fast and efficient development. Caffe has well-documented examples and provides pre-trained reference models for visual tasks, including AlexNet and R-CNN. It supports both CPU and GPU modes, and can be run in the cloud. Caffe is designed for vision but has been adopted in speech recognition, robotics, neuroscience, and astronomy. It is open-source and welcomes contributions. Caffe's architecture includes data storage in 4-dimensional arrays called blobs, and layers that perform operations like convolution, pooling, and nonlinearities. Networks are end-to-end systems, with data layers loading data and loss layers computing objectives. Training uses stochastic gradient descent, and finetuning allows adaptation to new tasks. Caffe has been used in numerous research projects and by industry partners like Facebook and Adobe. It enables state-of-the-art object classification, detection, and semantic feature extraction. It has applications in open-vocabulary object retrieval and image style recognition. Caffe is available on GitHub and has a web interface for documentation. It is licensed under BSD and is available on Amazon EC2. Caffe is acknowledged for GPU donations and community contributions.