Florence-2

Preview：

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Introduce：

Florence-2 is a new vision foundation model capable of handling a variety of computer vision and visual-language tasks through a unified, cue-based representation. It is designed to accept text prompts as task instructions and generate the desired result in text form, whether it is image description, object detection, positioning, or segmentation. This multitasking learning setup requires large-scale, high-quality annotated data. To this end, we co-developed FLD-5B, which contains 5.4 billion comprehensive visual annotations covering 126 million images, using an iterative strategy of automated image annotation and model refinement. We used a sequence-to-sequence structure to train Florence-2 to perform diverse and comprehensive visual tasks. Extensive evaluation has shown that Florence-2 is a strong contender for the visual base model, with unprecedented zero sample and fine-tuning capabilities.

Stakeholders:
The Florence-2 model is suitable for researchers and developers who need to handle complex visual tasks, especially in the areas of image description, object detection, visual localization and segmentation. Its multi-task learning ability and powerful data processing capabilities make it an important tool for advancing computer vision and visual-language research.
Usage Scenario Examples:

In the image description task, Florence-2 can generate accurate description text according to the input image.
In object detection tasks, Florence-2 can recognize multiple objects in an image and report their positions in text.
In visual positioning tasks, Florence-2 is able to associate text descriptions with specific areas in an image.

The features of the tool:

Text prompts are used as input for task instructions.
Generate the desired results in text form for a variety of visual tasks.
Large, high-quality FLD-5B data set support.
Iterative strategies for automated image annotation and model refinement.
Sequence to sequence structure, improve the diversity and comprehensiveness of tasks.
Zero sample and fine-tuning ability to adapt to tasks of varying complexity.

Steps for Use:

Step 1: Visit the Hugging Face page for the Florence-2 model.
Step 2: Choose the model version that suits your needs, such as the basic or large version.
Step 3: Read the model documentation to learn how to use text prompts to guide the model to perform tasks.
Step 4: Prepare your input data, which can be an image file or a text description associated with the image.
Step 5: Using the API or interface provided by the model, pass the input data to Florence-2.
Step 6: Obtain the results of the model output and perform further processing or analysis as needed.
Step 7: Adjust model parameters or input data based on feedback to optimize task performance.

Tool’s Tabs: Visual models, multi-task learning