A comparative study of YOLOv5 models performance for image localization and classification

被引：0

作者：

Horvat, Marko ^{[1
]}

Jelecevic, Ljudevit ^{[1
]}

Gledec, Gordan ^{[1
]}

机构：

[1] Univ Zagreb, Dept Appl Comp, Fac Elect Engn & Comp, Unska 3, HR-10000 Zagreb, Croatia

来源：

CENTRAL EUROPEAN CONFERENCE ON INFORMATION AND INTELLIGENT SYSTEMS, CECIIS 2022 | 2022年

关键词：

computer vision; image classification; deep learning; deep convolutional neural networks; YOLO; OBJECT DETECTION; TIME;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

YOLOv5 is one of the latest and often used versions of a very popular deep learning neural network used for various machine learning tasks, mainly in computer vision. The YOLO algorithm has steadily gained acceptance in the data science community due to its superior performance in complex and noisy data environments, availability, and ease of use in combination with widely used programming languages such as Python. This paper aims to compare different versions of the YOLOv5 model using an everyday image dataset and to provide researchers with precise suggestions for selecting the optimal model for a given problem type. The obtained results and the implemented YOLOv5 models are available for non-commercial use at: https://github.com/mhorvat/YOLOv5-models-comparison

引用

页码：349 / 356

页数：8

共 37 条

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