Discuss Microsoft's Florence foundation model
What is Microsoft's Florence foundation model?
Image Analysis 4.0 is powered by Microsoft's Florence foundation model, trained on billions of text-images pairs. By using universal visual-language representations in the form of text-image pair data, the Florence model can be easily adapted to various computer vision tasks such as classification, retrieval, object detection, and captioning. The Florence foundation model provides state-of-the-art computer vision capabilities and is a significant step forward in delivering game-changing visual recognition functionality.
Florence significantly improves the Vision Image Analysis capabilities, including enhanced image captioning and groundbreaking customization capabilities with few-shot learning. Few-shot learning refers to using computer vision algorithms to make predictions based on a limited number of sample images. The goal of few-shot learning is to create models capable of recognizing similarities and differences between images and "learning" to associate similar pictures with the same labels, much like humans can learn to recognize objects from a small sample of photos.
Massive training datasets are required to build computer vision models from scratch. Microsoft’s vision capabilities greatly reduce the engineering required to achieve enterprise-ready models. Before the Florence foundation model's development, Microsoft’s Custom Vision Service enabled model training using a minimum of 15 images. Now with Florence, users can train models using an even smaller minimum of four images.
The improved Image Analysis services in the Florence foundation model enables developers to create robust, market-ready computer vision applications capable of connecting their data to natural language interactions, unlocking powerful insights from their image and video content, and extracting information from visual features.