Exploring Integration methods for Image Data Augmentation

Data augmentation is a critical technique in deep learning, especially for image classification and object detection tasks. It increases the diversity of training data without the need to collect new data. While the efficacy of individual augmentation methods has been extensively studied, the combined impact of multiple augmentation techniques remains underexplored. This research investigates the effectiveness of various combinations of data augmentation methods on the performance of image classification and object detection models. Using the CIFAR-10, and COCO2017 dataset as benchmarks, we apply multiple augmentation strategies, including resizing, random horizontal flipping, random cropping, color jittering, random rotation, and gray-scale conversion. We evaluate the performance of the DenseNet-121 model for image classification and the YOLOv8 model for object detection. The experiments reveal that carefully selected and combined data augmentation methods not only enhance the performance of deep learning models more effectively than single methods but also underscore the importance of choosing augmentation techniques that are specifically tailored to the requirements of different tasks, such as classification or object detection.