Bounding boxes are a fundamental component of object detection, providing a rectangular region around objects of interest in an image. They are used to indicate the spatial location and size of an object, making it easier for the model to understand where the object is within the image. During training, bounding boxes, along with labels, serve as ground truth data, enabling the model to learn how to localize and classify objects. In practical applications, bounding boxes are used in tasks such as tracking objects in video feeds, autonomous vehicle navigation, and retail analytics.
What's the role of bounding boxes in object detection?
- Information Retrieval 101
- Natural Language Processing (NLP) Advanced Guide
- AI & Machine Learning
- The Definitive Guide to Building RAG Apps with LangChain
- Master Video AI
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How do you handle missing data in analytics?
Handling missing data in analytics is a critical task that can significantly impact the accuracy of your results. There
What is agent-based modeling?
Agent-based modeling (ABM) is a computational simulation technique used to understand and analyze complex systems by mod
How does singular value decomposition (SVD) work in recommender systems?
Singular Value Decomposition (SVD) is a powerful mathematical technique used in recommender systems to uncover latent pa