Advancing Image Classification Through Self-teachable Machine Models and Transfer Learning

Automated Machine Learning (AutoML) has progressively established its role in alleviating the complexities associated with traditional model selection and hyperparameter tuning. This research paper introduces a novel amalgamation of AutoML with the benefits of Transfer Learning for image classification [23] through Convolutional Neural Networks [23] (CNNs). By leveraging pre-trained models as a foundation, our framework reduces training time and improves model robustness. Furthermore, a sophisticated early stopping mechanism is integrated, ensuring optimal convergence while mitigating overfitting. The empirical evidence suggests that the fusion of AutoML, Transfer Learning, and Early Stopping paves the way for a new era in efficient and effective image classification, offering a blend of agility and precision.