A Data-Centric Perspective on the Influence of Image Data Quality in Machine Learning Models

• URL: http://arxiv.org/abs/2509.24420v1
• Date: Mon, 29 Sep 2025 08:09:21 GMT
• Title: A Data-Centric Perspective on the Influence of Image Data Quality in Machine Learning Models
• Authors: Pei-Han Chen, Szu-Chi Chung,
• Abstract summary: This study investigates methods for systematically assessing image dataset quality.<n>We identify common quality issues and quantify their impact on training.<n>We develop a pipeline that integrates two community-developed tools, CleanVision and Fastdup.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In machine learning, research has traditionally focused on model development, with relatively less attention paid to training data. As model architectures have matured and marginal gains from further refinements diminish, data quality has emerged as a critical factor. However, systematic studies on evaluating and ensuring dataset quality in the image domain remain limited. This study investigates methods for systematically assessing image dataset quality and examines how various image quality factors influence model performance. Using the publicly available and relatively clean CIFAKE dataset, we identify common quality issues and quantify their impact on training. Building on these findings, we develop a pipeline that integrates two community-developed tools, CleanVision and Fastdup. We analyze their underlying mechanisms and introduce several enhancements, including automatic threshold selection to detect problematic images without manual tuning. Experimental results demonstrate that not all quality issues exert the same level of impact. While convolutional neural networks show resilience to certain distortions, they are particularly vulnerable to degradations that obscure critical visual features, such as blurring and severe downscaling. To assess the performance of existing tools and the effectiveness of our proposed enhancements, we formulate the detection of low-quality images as a binary classification task and use the F1 score as the evaluation metric. Our automatic thresholding method improves the F1 score from 0.6794 to 0.9468 under single perturbations and from 0.7447 to 0.8557 under dual perturbations. For near-duplicate detection, our deduplication strategy increases the F1 score from 0.4576 to 0.7928. These results underscore the effectiveness of our workflow and provide a foundation for advancing data quality assessment in image-based machine learning.

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