2026-10-15T23:11:39Z https://data.mendeley.com/oai

oai:data.mendeley.com/f9k79z29rp.1 2025-09-15T14:28:09Z

Gerz, Fabian Towards Reliable Data Augmentation in Machine Learning Mendeley Data This repository contains the data and code for the paper "Towards Reliable Data Augmentation in Machine Learning: Practices to Prevent Data Leakage." It includes two case studies demonstrating full reproducibility (Tomato Disease Detection and Rice Pest Detection), which show that applying data augmentation before the train-test split leads to severe data leakage and massively inflated performance metrics. These resources enable the complete replication of our findings. Highlights of the paper: - Data leakage from pre-split augmentation inflates mAP50 by up to 66 percentage points. - Two forms of leakage are identified: process-induced and data-inherent data leakage. - Leakage is a pervasive systemic problem across applied ML domains. - Actionable best practices are provided for researchers, reviewers, and educators. Machine Learning Agricultural Health Machine Vision Plant Health Jelali, Mohieddine Dataset doi:10.17632/f9k79z29rp.1 oai:data.mendeley.com/f9k79z29rp.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/f9k79z29rp 2025-09-15T14:28:09Z 2025-12-11T23:59:59Z