Caffe
Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) for image classification and convolutional neural networks (CNNs). It is written in C++ with a Python interface, known for its speed, modularity, and expressive architecture that allows for easy experimentation with deep neural networks. Caffe is particularly popular in computer vision research and applications, such as object detection and image segmentation.
Developers should learn Caffe when working on computer vision projects, especially in academic or research settings where fast prototyping and high performance are critical. It is ideal for tasks like image classification, object detection, and segmentation due to its optimized CNN implementations and pre-trained models. However, it is less recommended for new projects as it has been largely superseded by more modern frameworks like PyTorch and TensorFlow.