<p>In recent years, deep learning techniques have been extensively used for the identification and classification of lithium-ion batteries. However, these models typically require a costly and labor-intensive labeling process, often influenced by commercial or proprietary concerns. In this study, we introduce RecyBat24, a publicly accessible image dataset for the detection and classification of three battery types: Pouch, Prismatic, and Cylindrical. Our dataset is designed to support both academic research and industrial applications, closely replicating real-world scenarios during the acquisition process and employing data augmentation techniques to simulate various external conditions. Additionally, we demonstrate how the RecyBat24’s detection-oriented annotations can be used to create a second version of RecyBat24for instance-segmentation tasks. Finally, we demonstrate that recent lightweight machine learning models achieve high accuracy, highlighting their potential for classification and segmentation applications where computational resources are constrained.</p>