Florence 2 Large Ft by microsoft

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  Arxiv:2311.06242   Autotrain compatible   Custom code   Florence2   Image-text-to-text   Pytorch   Region:us   Vision

Florence 2 Large Ft Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Florence 2 Large Ft (microsoft/Florence-2-large-ft)

Florence 2 Large Ft Parameters and Internals

Model Type 
image-text-to-text
Use Cases 
Areas:
Research, Commercial applications
Applications:
Image captioning, Object detection, Segmentation, Vision-language tasks
Primary Use Cases:
Captioning, Object detection, Segmentation, Vision-language tasks
Additional Notes 
Used in research and commercial applications without explicit censoring.
Training Details 
Data Sources:
FLD-5B dataset
Data Volume:
5.4 billion annotations across 126 million images
Methodology:
Prompt-based approach, sequence-to-sequence architecture
Model Architecture:
Sequence-to-sequence architecture
Input Output 
Input Format:
Input format for vision tasks through prompts.
Accepted Modalities:
Text, Image
Output Format:
Text-based descriptions and symbols for image annotations.
Performance Tips:
Switch prompts to trigger different tasks.
LLM NameFlorence 2 Large Ft
Repository ๐Ÿค—https://huggingface.co/microsoft/Florence-2-large-ft 
Required VRAM1.5 GB
Updated2025-01-24
Maintainermicrosoft
Model Typeflorence2
Model Files  1.5 GB
Model ArchitectureFlorence2ForConditionalGeneration
Licensemit
Model Max Length1024
Transformers Version4.41.0.dev0
Vocabulary Size51289
Torch Data Typefloat16

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227