Florence-2-large, developed by Microsoft, is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of visual and visual-language tasks. The model can interpret simple text prompts to perform tasks such as image description, object detection, and segmentation. It is trained on the FLD-5B dataset, which contains 540 million images with 5.4 billion annotations, making it proficient in multi-task learning. Its sequence-to-sequence architecture enables it to perform well in both zero-shot and fine-tuning settings, proving to be a competitive vision foundation model.