The power of image augmentation: an experiment

Image augmentation, a technique in computer vision, enhances datasets by creating new images through modifications such as rotation, cropping, and adding noise, thereby increasing the sample size and improving model performance. In an experiment using datasets of varying sizes, including packages, raccoons, and potholes, augmentation significantly improved model metrics like mean average precision (mAP), precision, and recall, especially in datasets with fewer original images. However, the effectiveness of augmentation depends on the type and number of augmentations used, with more augmentations generally leading to better performance, although excessive or inappropriate augmentations can negatively impact results. The study highlights the importance of selecting suitable augmentation techniques based on the dataset characteristics and emphasizes that while augmentation can enhance model accuracy, it is not a cure-all, and understanding the nuances of each dataset is crucial for optimal results.