Florence-2-large - A versatile visual language model developed by Microsoft for unified visual understanding.

## Overview of Florence-2-large Florence-2-large is a visual language model developed by Microsoft. It is designed to handle a variety of computer vision and visual language tasks using a prompt-based approach. The model employs a sequence-to-sequence learning paradigm and is trained on the FLD-5B dataset, which contains 126 million images and 5.4 billion comprehensive visual annotations. Florence-2-large excels in tasks such as caption generation, object detection, visual grounding, visual segmentation, and OCR, leveraging multi-task learning for unified visual understanding. ## Overview of Florence-2-large Florence-2-large supports a variety of tasks, including caption generation, object detection, visual grounding, visual segmentation, and OCR. The model is capable of interpreting simple text prompts to perform these tasks, making it versatile for a wide range of computer vision applications. ## Overview of Florence-2-large Florence-2-large is trained on the FLD-5B dataset, which contains 126 million images and 5.4 billion comprehensive visual annotations. This large-scale dataset enables the model to handle complex visual data, such as object locations, mask contours, and attributes, effectively. ## Overview of Florence-2-large Florence-2-large employs a sequence-to-sequence architecture, which enhances its flexibility in handling various visual and visual language tasks. This architecture allows the model to perform well in both zero-shot and fine-tuned settings, making it a competitive visual foundation model. ## Accessing Florence-2-large Florence-2-large can be accessed through the Hugging Face Transformers library. Users can find code snippets and Jupyter Notebooks for inference and visualization on the official Hugging Face page for Florence-2-large. The model is trained using float16 precision, and specific usage involves importing the model and performing inference through the Transformers library. ## Performance Metrics of Florence-2-large Florence-2-large achieves a COCO OD AP (Average Precision) of 39.8, indicating its strong performance in object detection tasks. The model's ability to handle complex visual data and perform multiple tasks efficiently makes it a robust choice for various computer vision applications. ## License of Florence-2-large Florence-2-large is released under the MIT license, making it open-source and freely available for use, modification, and distribution. This licensing allows researchers and developers to leverage the model for a wide range of applications without restrictive legal barriers. ### Citation sources: - [Florence-2-large](https://huggingface.co/Florence-2-large) - Official URL Updated: 2025-03-28