Multi Label Classification

Multi-label classification is a type of supervised learning problem where each instance can be associated with multiple labels, extending the concept of single-label classification (i.e., multi-class or binary classification). It aims to predict all possible labels for given input data through a model, thereby enhancing the accuracy and comprehensiveness of classification. This task holds significant application value in computer vision, capable of handling multi-object recognition and annotation in complex scenarios.