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Mean Average Precision (mAP) | Explanation and Implementation for Object Detection

In this video we go over Mean Average Precision (mAP) , Non-Maximum Suppression (NMS), anIn this video we go over Mean Average Precision (mAP) , Non-Maximum Suppression (NMS), and Intersection over Union (IOU) in object detection. We dive deep into understanding these crucial concepts for improving the accuracy of object detection algorithms. We first discuss Intersection over Union (IOU) as a measure for quality of a predicted box. We then see how to implement a method that computes Intersection over Union (IOU) for object detection in python. Moving on to Non-Maximum Suppression (NMS), we demonstrate its role in eliminating redundant bounding boxes and improving detection precision. We see different steps involved in NMS and put all of those steps into a method that given a set of boxes, implements Non-Maximum Suppression for object detection in python, optimizing the model's output . Finally, we explain how to calculate Mean Average Precision (MAP) step by step. We first look at different components that are required to understand what is Mean Average Precision for object detection. We then see how to calculate mean average precision for object detection task using a toy example. And finally we take a look at a practical implementation, making it easier for you to calculate Mean Average Precision for your models. ⏱️ Timestamps 00:00 Intro 00:34 Intersection over Union Explained 03:34 Intersection over Union (IOU) Implementation 05:30 Non-Maximum Suppression For Object Detection 07:24 Non-Maximum Suppression (NMS) Implementation 09:44 Mean Average Precision Explained 14:52 Calculate Mean Average Precision for Object Detection 22:16 Mean Average Precision (mAP) Implementation 27:49 Outro 🔔 Subscribe : https://tinyurl.com/exai-channel-link 🔗 Related Tags: #objectdetection Background Track - Fruits of Life by Jimena Contreras Email - [email protected]