Caffe

Caffe is a deep learning framework developed at the University of California, Berkeley, designed for speed, expressiveness, and modularity. It is open-source under a BSD license and is implemented in C++ with a Python interface, making it suitable for both research and industrial applications. Caffe excels in image processing and computer vision tasks, particularly with deep neural networks like Convolutional Neural Networks (CNNs), which are ideal for handling image and video data. The framework supports training and inference on large datasets using GPU acceleration, enabling efficient model deployment for real-time applications. Additionally, Caffe features a Model Zoo that provides a collection of community-contributed pre-trained models, facilitating quick starts on common tasks. While it is recognized for its speed and mature architecture, users may encounter a steeper learning curve compared to newer frameworks, and its primary focus on image processing may limit its versatility for other deep learning applications. Despite a slowdown in development relative to some newer alternatives, Caffe remains a well-established choice in the deep learning landscape, backed by an active community of users and contributors.