bvlc/caffe

Caffe is a deep learning framework developed by Berkeley AI Research (BAIR) in the mid-2010s, built for training and deploying convolutional neural networks, the type of neural network that became foundational for computer vision tasks like image classification and object detection. The problem it was designed to solve is that training deep neural networks requires both a highly structured computation graph and fast numerical execution, especially on GPU hardware. Caffe provided an organized, modular system for defining, training, and running these networks while achieving some of the fastest training speeds of its era. Caffe works through a layer-based architecture. You define a network as a configuration file that specifies a sequence of layers, convolutional layers, pooling layers, activation functions, fully connected layers, loss functions, and others, along with their parameters. The framework then compiles this definition and runs forward passes (computing predictions) and backward passes (computing gradients for learning) efficiently in C++, with the heavy numerical work offloaded to a GPU using CUDA. There is also a Python interface for scripting and experimentation. A model zoo provided pre-trained reference models that researchers could download and fine-tune for their specific tasks. Caffe was particularly influential in academic computer vision research and early industrial deep learning applications during 2014 to 2017. You would encounter it today mainly when working with legacy research code, reproducing results from papers published in that era, or running pre-trained models from its model zoo. For new projects, more modern frameworks like PyTorch or TensorFlow have since superseded it. The project is written in C++ with Python bindings, and is licensed under the BSD 2-Clause open-source license.