Object Recognition Datasets and Challenges: A Review

TL;DR

This review paper analyzes over 160 object recognition datasets and benchmarks, emphasizing their importance for advancing computer vision research and providing insights into dataset characteristics and evaluation metrics.

Contribution

It offers a comprehensive analysis of existing datasets and benchmarks in object recognition, aiding researchers in understanding dataset features and evaluation standards.

Findings

Over 160 datasets analyzed with detailed statistics

Overview of key benchmarks and competitions

Summary of evaluation metrics used in the field

Abstract

Object recognition is among the fundamental tasks in the computer vision applications, paving the path for all other image understanding operations. In every stage of progress in object recognition research, efforts have been made to collect and annotate new datasets to match the capacity of the state-of-the-art algorithms. In recent years, the importance of the size and quality of datasets has been intensified as the utility of the emerging deep network techniques heavily relies on training data. Furthermore, datasets lay a fair benchmarking means for competitions and have proved instrumental to the advancements of object recognition research by providing quantifiable benchmarks for the developed models. Taking a closer look at the characteristics of commonly-used public datasets seems to be an important first step for data-driven and machine learning researchers. In this survey, we provide a detailed analysis of datasets in the highly investigated object recognition areas. More than 160 datasets have been scrutinized through statistics and descriptions. Additionally, we present an overview of the prominent object recognition benchmarks and competitions, along with a description of the metrics widely adopted for evaluation purposes in the computer vision community. All introduced datasets and challenges can be found online at github.com/AbtinDjavadifar/ORDC.