Florence-2-large - AI Image Models Tool

Overview

Florence-2-large is a vision foundation model from Microsoft for a broad set of vision and vision-language tasks. It uses a prompt-based, sequence-to-sequence transformer architecture and is pretrained on the FLD-5B dataset. The model supports both zero-shot inference and fine-tuning, and is provided via the Hugging Face model repository for researchers and developers to evaluate and integrate.

Key Features

• Prompt-based sequence-to-sequence transformer architecture
• Pretrained on the FLD-5B dataset
• Designed for captioning, object detection, OCR, and segmentation
• Supports zero-shot inference and fine-tuning
• Published by Microsoft on the Hugging Face model page

Ideal Use Cases

• Automatic image captioning and descriptive text generation
• Zero-shot object detection for novel categories
• Optical character recognition for scanned images
• Semantic segmentation for scene parsing and analysis
• Vision-language tasks requiring multimodal understanding

Getting Started

• Open the model page on Hugging Face to review resources
• Read the model card, usage examples, and license details
• Choose zero-shot inference or prepare a fine-tuning plan
• Prepare an annotated vision or vision-language dataset
• Run inference with sequence-to-sequence prompts or fine-tune
• Validate outputs, adjust prompts, or iterate on training

Pricing

Pricing and commercial hosting terms are not disclosed in the provided tool context. Check the Hugging Face model page or Microsoft for availability and commercial terms.

Key Information

• Category: Image Models
• Type: AI Image Models Tool