Aim

To study transfer learning in deep CNNs by fine-tuning pretrained models such as VGG19 and MobileNetV2 on the Oxford Flowers dataset. To analyze how layer freezing, fine-tuning depth, learning rate, and dataset size affect feature extraction, training dynamics, representation quality, gradient flow, overfitting, and final inference performance. To implement and execute the models step by step to understand their architecture, transfer learning workflow, training process, and inference behavior.