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?
- Optimizing Your RAG Applications: Strategies and Methods
- Accelerated Vector Search
- Getting Started with Zilliz Cloud
- Vector Database 101: Everything You Need to Know
- Advanced Techniques in Vector Database Management
- 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
What are the challenges of benchmarking NoSQL databases?
Benchmarking NoSQL databases can be challenging for several key reasons. First, NoSQL databases often vary greatly in de
How do I use LangChain with RESTful APIs?
To use LangChain with RESTful APIs, you first need to establish a connection to the API you want to interact with. This
How do streaming systems handle late-arriving data?
Streaming systems handle late-arriving data through several strategies that ensure the timely processing of incoming eve