Fine-grained classification

Fine-grained classification is a type of classification problem where the goal is to distinguish between highly similar categories or subcategories. This is different from traditional classification problems where the categories are more distinct and easier to differentiate. Fine-grained classification is often used in fields such as biology, geology, and computer vision, where the differences between similar categories may be subtle but important. Machine learning techniques such as deep learning and ensemble learning are often used to solve fine-grained classification problems.