Florence-2-base

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Introduce：

Florence-2 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 and performing tasks such as 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. 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 handle visual and visual-linguistic tasks such as image description, object detection, and image segmentation. Florence-2’s multi-task learning ability and sequence-to-sequence architecture make it ideal for these tasks.
Usage Scenario Examples:

• Use Florence-2 to generate image descriptions
• Florence-2 was used for target detection
• Image segmentation is realized by Florence-2

The features of the tool:

• Image to text conversion
• Prompt based text generation
• Visual and visual-linguistic task processing
• Multitasking learning
• Zero sample and fine tuning performance
• Sequence-to-sequence architecture

Steps for Use:

• 1. Import the necessary libraries and models: AutoModelForCausalLM and AutoProcessor.
• 2. Load the pre-trained model and processor from Hugging Face.
• 3. Define the task prompt to execute.
• 4. Load or obtain the image to be processed.
• 5. Convert text and images into an acceptable input format for the model through the processor.
• 6. Use the model to generate output, such as text descriptions or object detection boxes.
• 7. Post-process the generated output to obtain the final result.
• 8. Print or otherwise display the results.

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