Convolutional Neural Network (CNN) is a deep learning model that can effectively process high-dimensional data such as images. The main feature of the convolutional neural network is to use the convolutional layer and the pooling layer to extract the local features of the image and reduce the dimension, thereby reducing the number of parameters and the amount of calculation. Convolutional neural networks have many advantages in the field of image recognition, such as:
- Convolutional neural networks can automatically learn the features of images without manual design or selection of feature extractors.
- Convolutional neural network can use the spatial structure information of the image to maintain the translation, rotation and scaling invariance of the image.
- The convolutional neural network can build a deep network structure by stacking multiple convolutional layers and pooling layers, thereby improving the expressive ability and generalization ability of the model.
Convolutional neural network (CNN) is a deep learning model that has significant advantages in the field of image recognition in Beijing . The main feature of CNN is to use convolutional layers and pooling layers to extract local features of images, thereby reducing the number of parameters and computational complexity, and improving the generalization ability of the model. CNN can also build a deep network structure by stacking multiple convolutional and pooling layers to capture high-level semantic information of images. When CNN processes image data, it does not need complex preprocessing or feature extraction, but directly takes the original image as input, allowing the network to automatically learn the optimal feature representation. Therefore, CNN has the advantages of high efficiency, flexibility, and robustness in the field of image recognition.