<b>Chemistry Lab Image Dataset Covering 25 Apparatus Categories</b>

This dataset contains <b>4,599</b> high-quality, annotated images of <b>25</b> commonly used chemistry lab apparatuses. The images, each containing structures in real-world settings, have been captured from different angles, backgrounds, and distances, while also undergoing variations in lighting to aid in the robustness of object detection models. Every image has been labeled using bounding box annotation in TXT (YOLO) format, alongside the class IDs and normalized bounding box coordinates, making object detection more precise. The annotations and bounding boxes have been built using the Roboflow platform.To achieve a better learning procedure, the dataset has been split into three sub-datasets: training, validation, and testing. The training dataset constitutes <b>70%</b> of the entire dataset, with validation and testing at <b>20%</b> and <b>10%</b> respectively. In addition, all images undergo scaling to a standard of <b>640x640</b> pixels while being auto-oriented to rectify rotation discrepancies brought about by the EXIF metadata. The dataset is structured in three main folders - train, valid, and test, and each contains images/ and labels/ subfolders. Every image contains a label file containing class and bounding box data corresponding to each detected object.The whole dataset features <b>6,960</b> labeled instances per 25 apparatus categories including beakers, conical flasks, measuring cylinders, test tubes, among others. The dataset can be utilized for the development of automation systems, real-time monitoring and tracking systems, tools for safety monitoring, alongside AI educational tools.