01322nas a2200133   4500000000100000000000100001008004100002260009600043100001900139245003800158856026000196520071000456020002201166       2025                            d  c01/2025b1st International Conference PUT STEM Day 2025: Book of AbstractsaPoznań, Polska1 aMarzena Halama00aOptimization for Pre-trained CNNs  uhttps://putpoznanpl-my.sharepoint.com/personal/stemday_put_poznan_pl/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fstemday%5Fput%5Fpoznan%5Fpl%2FDocuments%2FKN%20PUT%20STEM%2FStemDay%2FSTEM%20Day%202026%2FPUT%20STEM%20Day%202025%2F1st%20International%20Confe3 aConvolutional 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.  a978-83-955437-7-7