【ML Paper】Explanation of all of YOLO series Part 3

This is an summary of paper that explains yolov1 to v8 in one.
Let's see the history of the yolo with this paper.
This article is part 3, part 2 is here.

Original Paper: https://arxiv.org/pdf/2304.00501

3. Object Detection Metrics and Non-Maximum Suppression (NMS)

3.1 Average Precision (AP)

The Average Precision (AP), commonly referred to as Mean Average Precision (mAP), serves as the primary metric for evaluating the performance of object detection models. AP calculates the average precision across all categories, offering a unified value to compare different models. In the context of the COCO dataset, there is no distinction between AP and mAP; hence, this metric will be referred to as AP throughout.

3.2 Datasets Utilized

Initially, YOLOv1 and YOLOv2 employed the PASCAL VOC 2007 and VOC 2012 datasets for training and benchmarking. From YOLOv3 onward, the Microsoft COCO (Common Objects in Context) dataset has been the standard. The computation of AP varies between these datasets, which will be elucidated in subsequent sections.

3.3 Precision and Recall Metrics

AP is fundamentally based on precision and recall metrics. Precision assesses the accuracy of the model’s positive predictions, while recall evaluates the proportion of actual positive instances correctly identified by the model. A balance between precision and recall is crucial, as enhancing recall by detecting more objects can lead to a decrease in precision due to an increase in false positives. AP addresses this trade-off by integrating the precision-recall curve, which plots precision against recall across different confidence thresholds. The area under this curve provides a balanced measure of both precision and recall.

3.4 Handling Multiple Object Categories

Object detection models are required to identify and localize multiple object categories within an image. AP manages this complexity by calculating the average precision for each category individually and then averaging these values across all categories. This method ensures a comprehensive evaluation of the model’s performance across diverse categories, enhancing the reliability of the overall performance assessment.

3.5 Intersection over Union (IoU)

Accurate localization of objects is achieved through the prediction of bounding boxes, and AP incorporates the Intersection over Union (IoU) metric to evaluate their quality. IoU is defined as the ratio of the area where the predicted bounding box and the ground truth bounding box overlap to the area covered by both boxes combined. This measure quantifies the degree of overlap between the predicted and actual bounding boxes. The COCO benchmark employs multiple IoU thresholds to assess the model’s localization accuracy at various levels, ensuring a robust evaluation of detection performance.