Bounding Box Predictions
Explore how YOLO predicts bounding boxes by dividing the input image into grid cells and estimating coordinates relative to each cell. Understand coordinate formats, confidence scores, class predictions using logistic regression, and YOLO's multi-scale approach to detect objects of varying sizes.
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What is a bounding box?
A bounding box is simply a rectangle drawn around an object to identify the exact location of the object in an image. In OD tasks, it also helps us identify what kind of object is present in an image.
How are coordinates represented?
Mathematically, a bounding box is represented as a tensor consisting of information related to the location of the object and confidence scores. In OD tasks, two formats are widely followed to represent location:
(
, , , ): They are also known as top-left and bottom-right coordinates. (
, , , ): They are the center coordinates of an image, along with the width and height of the image.
Time to code!
In this example, we’re given an object’s bounding box annotation in Pascal VOC format. Our task is to convert these coordinates into the YOLO format. We will create a Python function that takes the image dimensions and bounding box coordinates in the PASCAL VOC format as the input and returns the bounding box coordinates in the YOLO format.
Input
Image dimensions (width and height)
Bounding box coordinates in the PASCAL VOC format (
, ...