

Florence 2
Overview :
Florence-2 is a novel visual foundation model that can handle various computer vision and vision-language tasks through a unified, prompt-based representation. Designed to accept text prompts as task instructions and generate expected results in textual format, whether it's image description, object detection, localization, or segmentation. This multi-task learning setup requires large-scale, high-quality annotated data. To this end, we jointly developed FLD-5B, which contains 5.4 billion comprehensive visual annotations across 126 million images, utilizing automated image annotation and model refinement iterative strategies. We employed a sequence-to-sequence structure to train Florence-2, enabling it to perform diverse and comprehensive visual tasks. Extensive evaluations demonstrate that Florence-2 is a powerful competitor within the visual foundation model landscape, exhibiting unprecedented zero-shot and fine-tuning capabilities.
Target Users :
The Florence-2 model is suitable for researchers and developers working on complex visual tasks, especially in areas such as image description, object detection, visual localization, and segmentation. Its multi-task learning capability and powerful data processing ability make it a key tool for advancing computer vision and vision-language research.
Use Cases
In image description tasks, Florence-2 can generate accurate descriptive text based on input images.
In object detection tasks, Florence-2 can identify multiple objects within an image and report their locations in textual format.
In visual localization tasks, Florence-2 can associate textual descriptions with specific regions in images.
Features
Input mechanism using text prompts as task instructions.
Generates textual expected results applicable to various visual tasks.
Supported by the large-scale, high-quality FLD-5B dataset.
Utilizes automated image annotation and model refinement iterative strategies.
Sequence-to-sequence structure, enhancing task diversity and comprehensiveness.
Zero-shot and fine-tuning capabilities, adaptable to tasks of varying complexity.
How to Use
Step 1: Visit the Florence-2 model's Hugging Face page.
Step 2: Select the model version that best suits your needs, such as the base version or the large version.
Step 3: Read the model documentation to understand how to utilize text prompts to guide the model in performing tasks.
Step 4: Prepare your input data, which can be image files or text descriptions related to images.
Step 5: Use the model's provided API or interface to pass the input data to Florence-2.
Step 6: Obtain the model's output results and process or analyze them as needed.
Step 7: Adjust model parameters or input data based on feedback to optimize task performance.
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