Caffe is a deep learning framework developed by the Berkeley AI Research (BAIR) group and community contributors. Designed for expression, speed, and modularity, it allows configurations without hard coding for both CPU and GPU training. Known for its rapid processing capabilities, Caffe can handle over 60 million images per day with a single GPU, making it suitable for both research and industry. It supports a variety of applications, ranging from academic research projects to large-scale industrial applications in vision, speech, and multimedia.
