Convolutional Neural Networks (CNNs) are a type of artificial neural network commonly used for image processing tasks such as image classification, object detection, and segmentation. CNNs are particularly effective at processing images because they can automatically learn and extract relevant features from the input data.
CNNs consist of several layers, including convolutional layers, pooling layers, and fully connected layers. Convolutional layers apply filters to the input image to extract relevant features, such as edges, textures, or shapes. Pooling layers reduce the spatial dimensions of the feature maps by aggregating neighboring pixels. Fully connected layers use the output from the previous layers to produce a final prediction.
The basic architecture of a CNN consists of an input layer, several convolutional layers, pooling layers, and fully connected layers, and an output layer. During training, the network adjusts the weights of the filters to minimize a loss function, such as cross-entropy, using backpropagation.
One of the key advantages of CNNs is their ability to learn hierarchical representations of the input data. The lower layers learn basic features, such as edges or corners, while the higher layers learn more complex features, such as shapes or objects. This allows the network to classify or detect objects in the image based on their constituent parts.
CNNs have been highly successful in a variety of image processing tasks, including image classification, object detection, and segmentation. They have also been used in applications such as face recognition, autonomous driving, and medical imaging.