What are the core components of the Caffe framework?

The core components of the Caffe framework include:

1. Blob: Blob is a data structure in the Caffe framework used to store data and gradients in a network.
2. Layer in the Caffe framework is a network layer used to organize the structure of neural networks.
3. Net is the network class in the Caffe framework, responsible for managing the forward and backward propagation of the entire neural network.
4. Solver is a class in the Caffe framework that is used for training neural networks and updating network parameters.
5. Pre-trained Models: Pre-trained models in the Caffe framework are models that have already been trained and can be fine-tuned or used directly by users.
6. The Data Layer in Caffe framework is the data input layer used for loading and processing training data.
7. Loss Layer is a layer in the Caffe framework that is used to calculate the loss value of the network.
8. The Activation Layer is a function in the Caffe framework that introduces non-linear transformations.
9. Convolution Layer: This is the convolutional layer in the Caffe framework, used for feature extraction.
10. Pooling Layer is a layer in the Caffe framework used for dimensionality reduction and reducing computational complexity.