What are the main functions of the Caffe framework?

The main features of the Caffe framework include:

1. Efficient Convolutional Neural Network (CNN) implementation: Caffe is a deep learning framework specifically designed for building and training CNN models. It offers a variety of layer types such as convolutional layers, pooling layers, fully connected layers, as well as various activation functions and loss functions, allowing users to flexibly construct different CNN models.
2. Support for multiple models and data: Caffe supports various models and data, including image classification, object detection, semantic segmentation, etc. It provides popular models such as AlexNet, VGGNet, GoogLeNet, allowing users to easily train and test with these models. Furthermore, Caffe supports common image data formats like LMDB, LevelDB, HDF5.
3. Efficient computation and memory management: Caffe achieves efficient computation and memory management by utilizing C++ and CUDA. It leverages GPU acceleration for fast model training and inference. Additionally, Caffe offers features such as memory optimization and distributed training to effectively handle large-scale data and models.
4. Caffe provides user-friendly interfaces and tools such as a simple Python interface for defining models, configuring training parameters, and reading data. Additionally, Caffe offers command line tools like caffe train and caffe test to make model training and testing easier for users.
5. Open source and active community support: Caffe is an open-source project with a large user and developer community. Users can access various models and code examples from the community to solve problems and share experiences. The developers of Caffe also regularly release new versions, fix bugs, and improve features to ensure the stability and reliability of the framework.