Florence 2 large no flash attn EU AI Act Compliance Profile

Florence-2 (without flash-attn): Advancing a Unified Representation for a Variety of Vision Tasks

⚠️ This is a modified version of Florence 2 that modifies the custom modeling_florence2.py file to remove the need for installing flash-attn package (by hijacking the flash-attn methods and replacing with regular attention). It probably has impact in performance.

Model Summary

This Hub repository contains a HuggingFace's transformers implementation of Florence-2 model from Microsoft.

Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model.

Resources and Technical Documentation:

• Florence-2 technical report.
• Jupyter Notebook for inference and visualization of Florence-2-large

| Model | Model size | Model Description | | ------- | ------------- | ------------- |
| Florence-2-base[HF] | 0.23B | Pretrained model with FLD-5B
| Florence-2-large[HF] | 0.77B | Pretrained model with FLD-5B
| Florence-2-base-ft[HF] | 0.23B | Finetuned model on a colletion of downstream tasks | Florence-2-large-ft[HF] | 0.77B | Finetuned model on a colletion of downstream tasks

How to Get Started with the Model

Use the code below to get started

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