TensorTonic | Non-Maximum Suppression

In this video, I explain the Non-Maximum Suppression (NMS) algorithm, one of the most important techniques used in Object Detection models like YOLO, Faster R-CNN, SSD, and other computer vision pipelines.

We start by understanding why multiple bounding boxes are predicted for the same object, then learn how IoU — Intersection over Union helps us compare overlapping boxes. After that, we implement NMS step-by-step in Python and debug the common mistakes that happen while writing the logic.

📌 Topics Covered:

What is Non-Maximum Suppression?
Why NMS is used in object detection
Understanding bounding boxes
IoU formula: Intersection over Union
How to calculate intersection area
How to calculate union area
Sorting boxes by confidence score
Suppressing boxes based on IoU threshold
Python implementation of NMS
Common bugs while implementing NMS

🧠 Core Idea:
NMS keeps the bounding box with the highest confidence score and suppresses other boxes that overlap too much with it. This helps object detection models avoid duplicate detections for the same object.

💻 Useful for:

Computer Vision beginners
Machine Learning learners
Deep Learning students
YOLO/Object Detection learners
Coding interview preparation
TensorTonic problem-solving practice

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Видео TensorTonic | Non-Maximum Suppression канала AlgorithmsUntilRED