Florence-2-large

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Introduce：

Florence-2-large is an advanced vision foundation model developed by Microsoft that takes a cue-based approach to a wide range of visual and visual-linguistic tasks. The model is capable of interpreting simple text prompts to perform tasks such as image description, object detection, and segmentation. It leverages a FLD-5B dataset of 540 million images containing 5.4 billion annotations and is proficient in multitasking learning. Its sequence-to-sequence architecture makes it perform well in both zero-sample and fine-tuning Settings, proving to be a competitive visual base model.

Stakeholders:
The Florence-2-large model is suitable for developers and researchers who need image analysis and understanding. Whether exploring the frontiers of visual recognition in academic research or realizing automatic labeling and description of image content in commercial applications, this model can provide powerful support.
Usage Scenario Examples:

• Automatically generate descriptive text for images on social media.
• Provide target detection and classification services of product pictures for e-commerce websites.
• In the field of autonomous driving, it is used to identify roads and traffic signs.

The features of the tool:

• Image description: Generate descriptive text based on the content of the image.
• Object detection: Identify objects in an image and label their location.
• Segmentation: Distinguishing between different areas of the image, such as objects and backgrounds.
• Dense area description: Generates detailed descriptions of dense areas in the image.
• Area proposal: Proposes areas of the image that may contain objects.
• OCR: Recognizes and extracts text from images.
• OCR and region: Text recognition combined with region information.

Steps for Use:

• Import necessary libraries such as requests, PIL, Image, and transformers.
• Load the Florence-2-large model from the pre-trained model using AutoModelForCausalLM and AutoProcessor.
• Define task prompts that need to be performed, such as image description or object detection.
• Load or get the image data that needs to be processed.
• Text prompts and image data are converted by the model and processor into an input format acceptable to the model.
• Call the generate method of the model to generate the results.
• The generated ID is converted to text using the processor’s batch_decode method.
• Depending on the type of task, the generated text is parsed using post-processing methods to obtain the final result.

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