Optimization for Pre-trained CNNs

Convolutional neural networks (CNNs) constitute a fundamental cornerstone of computer vision. With increasing complexity, the need for effective optimisation strategies remains crucial. Techniques such as transfer learning (TL), utilising pre-trained networks, enable the deployment of advanced models on mobile devices with limited computing capacity, including autonomous vehicles and educational applications. The study explores optimisation strategies for Keras models, focusing on the impact of different algorithms on performance and accuracy. The results demonstrate that appropriate optimiser selection enhances learning efficiency, mitigates overlearning, and supports accurate image recognition.