  In the rapidly evolving domain of machine learning, ensuring model
generalizability remains a quintessential challenge. Overfitting, where a model
exhibits superior performance on training data but falters on unseen data, is a
recurrent concern. This paper introduces the Overfitting Index (OI), a novel
metric devised to quantitatively assess a model's tendency to overfit. Through
extensive experiments on the Breast Ultrasound Images Dataset (BUS) and the
MNIST dataset using architectures such as MobileNet, U-Net, ResNet, Darknet,
and ViT-32, we illustrate the utility and discernment of the OI. Our results
underscore the variable overfitting behaviors across architectures and
highlight the mitigative impact of data augmentation, especially on smaller and
more specialized datasets. The ViT-32's performance on MNIST further emphasizes
the robustness of certain models and the dataset's comprehensive nature. By
providing an objective lens to gauge overfitting, the OI offers a promising
avenue to advance model optimization and ensure real-world efficacy.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition