An Interactive Guide to 4 Fundamental Computer Vision Tasks Using Transformers

This article introduces an interactive Streamlit application that enables users to compare the performance of transformer-based modelsViT, DETR, BLIP, and ViLTacross four fundamental computer vision tasks: image classification, image segmentation, image captioning, and visual question answering. By providing a practical implementation guide, it highlights how these models leverage transformer architectures to address diverse visual understanding challenges, emphasizing their technical distinctions and capabilities. The development underscores the growing importance of transformer models in computer vision, offering a hands-on tool for researchers and practitioners to evaluate and understand their performance in real-world scenarios. This approach