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?

- Evaluating Your RAG Applications: Methods and Metrics
- Master Video AI
- Natural Language Processing (NLP) Advanced Guide
- The Definitive Guide to Building RAG Apps with LangChain
- How to Pick the Right Vector Database for Your Use Case
- 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 Sentence Transformers compare to using contextual embeddings of individual words for tasks like clustering or semantic search?
Sentence Transformers and contextual word embeddings serve different purposes in tasks like clustering or semantic searc
What are the main use cases for big data?
Big data has become a crucial asset across various industries due to its ability to generate insights from large volumes
What are the key industries adopting predictive analytics?
Predictive analytics is gaining traction across various industries as businesses seek to leverage data for better decisi