Anchor boxes are predefined bounding boxes used in object detection models to predict object locations and scales more effectively. They help in handling objects of varying sizes and aspect ratios by providing a reference point for the model to adjust from during training and inference.

Anchor Boxes

Bounding boxes are rectangular borders used in computer vision to identify and locate objects within an image. They are fundamental in tasks such as object detection, image segmentation, and tracking, providing a simple yet effective way to represent spatial information about objects in a two-dimensional space.

Bounding Boxes

Object detection models

Object locations

Object scales

Handling objects

Varying sizes

Aspect Ratios

A reference point is a basis or standard for evaluation, comparison, or understanding of something, often serving as a benchmark or anchor in various fields such as physics, psychology, and economics. It is crucial for establishing context, measuring change, and making informed decisions by providing a stable framework against which other variables or phenomena can be assessed.

Reference Point

Training and inference

YOLOv4

Bounding Boxes in Object Detection

Understanding YOLO Class Implementation in Object Detection

Class Probabilities in Object Detection

Practical Implementation of YOLO

Deciphering YOLOv9 ONNX Model Outputs: A Comprehensive Guide to 1x290x8400 Dimensions

Concepts

Relevant Degrees

Your Lessons