Florence-2-large-ft

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Introduce：

Florence-2-large-ft is an advanced vision foundation model developed by Microsoft that uses a cue-based approach to a wide range of visual and visual-linguistic tasks. The model can perform tasks such as image description, object detection and segmentation with simple text prompts. It leverages the FLD-5B dataset, which contains 5.4 billion annotations and covers 126 million images for multitasking learning. The sequence-to-sequence architecture of the model makes it perform well in both zero-sample and fine-tuning Settings, proving it to be a competitive visual base model.

Stakeholders:
The target audience is researchers and developers who need to perform image processing and analysis, including but not limited to professionals in the fields of computer vision, natural language processing and machine learning. The product suits them because it provides a powerful tool to handle complex visual tasks and the ability to automate tasks with simple text prompts.
Usage Scenario Examples:

• The researchers used the Florence-2-large-ft model to automatically generate image descriptions to help visually impaired people understand the image content.
• Developers use the model for object detection to improve the perception of autonomous vehicles.
• Companies use the technology for automatic labeling and classification of product images to optimize search and recommendation systems for e-commerce platforms.

The features of the tool:

• Image description: Generates a text description of the image.
• Object detection: Identify and locate objects in an image.
• Segmentation: The segmentation of an image into different areas or objects.
• Area proposal: Generates the area of the image that may contain the target.
• OCR: Recognizes text in an image.
• Area OCR: identifies text in a specific area.

Steps for Use:

• 1. Install necessary libraries such as transformers and PIL.
• 2. Load Florence-2-large-ft model and processor from Hugging Face model library using AutoModelForCausalLM and AutoProcessor.
• 3. Prepare the input data, including text prompts and images.
• 4. Convert text and images into a format acceptable to the model through the processor.
• 5. generate output using the model’s generate method.
• 6. Convert the generated ID back to text using the processor’s batch_decode method.
• 7. Use post-processing functions to parse the generated text, depending on the type of task.
• 8. Output final results, such as image descriptions or bounding boxes and labels for object detection.

Tool’s Tabs: Image processing, natural language processing