Image Classification

Image classification is a fundamental task in computer vision that involves assigning a category label to an input image based on its content. This task can be formalized as a supervised learning problem where a model, typically a convolutional neural network (CNN), is trained on a labeled dataset. The model learns to extract hierarchical features from images, enabling it to differentiate between classes by optimizing a loss function, often cross-entropy, during training. The performance of image classification models is evaluated using metrics such as accuracy, precision, recall, and F1-score. Recent advancements include the use of transfer learning and pre-trained models, such as ResNet and Inception, which leverage large datasets to improve classification performance on smaller, domain-specific datasets.