Florence-2-large-ft
| Property | Value |
|---|---|
| Parameter Count | 0.77B |
| License | MIT |
| Paper | View Paper |
| Model Type | Vision Foundation Model |
What is Florence-2-large-ft?
Florence-2-large-ft is Microsoft's advanced vision foundation model that represents a significant breakthrough in unified visual understanding. It's a fine-tuned version of the base Florence-2 architecture, specifically optimized for multiple downstream tasks. With 0.77B parameters, it leverages the FLD-5B dataset containing 5.4 billion annotations across 126 million images.
Implementation Details
The model employs a sequence-to-sequence architecture implemented using HuggingFace's transformers library. It operates in float16 precision on compatible hardware and can process both images and text inputs through its unified architecture.
- Supports multiple vision-language tasks through prompt-based interaction
- Implements efficient processing with PyTorch backend
- Features comprehensive post-processing capabilities for various output formats
Core Capabilities
- Image Captioning (from basic to detailed descriptions)
- Object Detection with bounding box outputs
- OCR with region detection
- Dense Region Captioning
- Caption-to-Phrase Grounding
- Visual Question Answering
Frequently Asked Questions
Q: What makes this model unique?
Florence-2-large-ft stands out for its ability to handle multiple vision tasks through a single unified model, achieving competitive performance with significantly fewer parameters than alternatives. Its fine-tuned nature makes it particularly effective for practical applications while maintaining strong zero-shot capabilities.
Q: What are the recommended use cases?
The model excels in various scenarios including automated image captioning, detailed scene understanding, text extraction from images, and visual question answering. It's particularly suitable for applications requiring multiple vision tasks within a single pipeline.
