This course teaches you how to fine-tune powerful vision models and optimize their training for real-world performance. You’ll start by applying transfer learning with a pre-trained ViT-B/16 model, learning how to freeze and selectively unfreeze layers to adapt general visual representations to domain-specific datasets such as retail product images. You’ll then analyze and compare learning-rate schedules, including cosine decay and the one-cycle policy, to understand how each strategy shapes training stability, convergence speed, and validation accuracy. Through hands-on labs, experiment logging, and training-curve interpretation, you’ll practice making informed decisions about which layers to update, which LR schedule to select, and how to balance accuracy with training efficiency. By the end of the course, you’ll be able to fine-tune transformer-based models effectively and choose learning-rate strategies that reduce training time without sacrificing performance.