The script fine-tunes a Vision Transformer (ViT) to spot if a hot dog is in the image, hitting a 97% accuracy, while CNNs like VGG and MobileNet max out around 85% in the same time. ViT rocks because it looks at the whole image at once, catching details CNNs miss with their narrow focus on local areas. The key idea is to treat images like sequences, using transformer attention to pull out global features more effectively. That extra awareness is why ViT crushes it for tasks like this!

For training, I used Google Colab as it provides access to CUDA cores, which are essential for speeding up the fine-tuning process of Vision Transformer (ViT). ViT models require significant computational power due to their self-attention mechanism, and leveraging GPU resources ensures faster and more efficient training.