Welcome to the Florence-2 repository! This repository contains a Hugging Face's transformers implementation of the Florence-2 model, developed by Microsoft.
Florence-2 is a cutting-edge vision foundation model designed to handle a diverse array of vision and vision-language tasks through a prompt-based approach. This model can interpret simple text prompts to perform tasks such as captioning, object detection, and segmentation. It utilizes the FLD-5B dataset, which includes 5.4 billion annotations across 126 million images, to excel in multi-task learning. Florence-2's sequence-to-sequence architecture allows it to perform exceptionally well in both zero-shot and fine-tuned settings, making it a competitive and versatile vision foundation model.
For more details, you can read the technical paper.
- Prompt-based Approach: Handles a wide range of vision tasks with simple text prompts.
- Multi-task Learning: Leverages the extensive FLD-5B dataset to master multiple tasks.
- Sequence-to-Sequence Architecture: Excels in zero-shot and fine-tuned settings.
- Vision and Vision-Language Tasks: Capable of captioning, object detection, segmentation, and more.
