Notably, this dataset consists solely of original images, with no augmented images included. This feature ensures that models trained on this dataset will be evaluated on genuine, real-world data, providing a robust foundation for developing and testing image classification algorithms. The dataset’s structure and diversity make it an excellent resource for training deep learning models in automotive part identification, offering a range of complexities and variations to challenge and improve model performance.