From Pixels to Precision: Techniques for Image Dataset Refinement

Abstract

Preparing and processing image datasets is a crucial step in deep learning and computer vision applications. However, many challenges arise in creating high-quality image datasets that can make deep learning models more accurate and generic. We provide a thorough review of best practices in this document for preparing and processing image datasets, from raw data collection to final dataset creation. We discuss various techniques and tools that can be used to enhance image quality, reduce noise, and address issues such as class imbalance and bias. Additionally, we provide practical tips for data cleaning, normalization, and augmentation, which are essential for improving the accuracy and robustness of machine learning procedures. Consequently, we demonstrate the effectiveness of our approach by applying it to real-time images and converting them to datasets suitable for several computer vision applications, such as object detection and classification.