Retail tagging involves assigning metadata to products, shelves, or retail environments. This metadata could include product names, descriptions, prices, categories, and more. With accurate retail tagging, businesses can streamline inventory management, optimize product placement, and deliver personalized shopping experiences to customers.

Price tagging involves annotating products with their corresponding prices. This annotation enables pricing algorithms, inventory systems, and retail staff to quickly identify and update product prices. In addition to facilitating sales transactions, price tagging assists in pricing analysis, demand forecasting, and promotional strategies.

In image annotation, a 2D bounding box is a rectangular annotation drawn around objects or regions of interest within an image. This annotation technique is commonly used in object detection and localization tasks. In retail applications, 2D bounding boxes may be utilized to identify and categorize products on store shelves or within inventory databases.

Similar to 2D bounding boxes, 3D bounding boxes represent the three-dimensional extent of objects within a scene or environment. This annotation technique is particularly useful in scenarios where depth information is critical, such as in autonomous driving, robotics, or augmented reality applications. In retail, 3D bounding boxes could aid in inventory management, spatial analysis, and virtual store simulations.

Sports tagging involves annotating video footage or images with metadata related to specific sports events, actions, or players. For instance, in baseball tagging, annotations may include player positions, ball trajectories, game events (e.g., home runs, strikeouts), and more. Similarly, tennis tagging may involve annotating matches with player movements, shot types, rally durations, and match outcomes. Accurate sports tagging enhances sports analytics, coaching insights, and fan engagement experiences.