Object Segmentation with Florence-2 and Text Prompts

This 19-minute tutorial demonstrates how to implement object segmentation using Microsoft's Florence-2 large model with text prompts in Python. Learn the complete process from installation to implementation, including how to load the Florence-2 model through Hugging Face Transformers, use natural language prompts like "a parrot" for referring expression segmentation, preprocess images, run inference, and visualize output masks with OpenCV. Follow along with step-by-step code explanations covering the entire workflow from model setup to rendering and saving segmented images. The tutorial is organized into three main sections: introduction with demo, installation instructions, and hands-on coding. Complete code for the tutorial is available through the provided link, and additional resources for computer vision and visual language models are referenced for further learning.