Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC). Renowned for its performance and efficiency, Caffe is designed with speed, modularity, and expressiveness in mind. It provides a clean and straightforward way for researchers and developers to build and train models for various machine learning tasks, primarily focusing on image processing and classification.The framework supports many different types of deep learning architectures geared towards image classification and segmentation. Users can switch between CPU and GPU processing, tailoring the performance to the task at hand. Caffe's pre-trained models and capabilities to fine-tune and extend these models make it a valuable tool for both academic researchers and industry practitioners.The official website for Caffe, https://caffe.berkeleyvision.org, offers comprehensive resources including documentation, tutorials, pre-trained models, as well as a user forum for support. Here, both new users and seasoned developers can find useful information to get started or enhance their existing projects using Caffe. Whether one is involved in academic research, product development, or simply experimenting with neural networks, Caffe provides robust tools to implement deep learning solutions effectively.