florence-2

streamline the fine-tuning process for multimodal models: PaliGemma 2, Florence-2, and Qwen2.5-VL

Tag manager and captioner for image datasets

AI-Powered Watermark Remover using Florence-2 and LaMA Models: A Python application leveraging state-of-the-art deep learning models to effectively remove watermarks from images with a user-friendly PyQt6 interface.

Use Segment Anything 2, grounded with Florence-2, to auto-label data for use in training vision models.

VLM driven tool that processes surveillance videos, extracts frames, and generates insightful annotations using a fine-tuned Florence-2 Vision-Language Model. Includes a Gradio-based interface for querying and analyzing video footage.

Watermark remover tool that leverages the capabilities of Microsoft Florence and Lama Cleaner models.

Use Florence 2 to auto-label data for use in training fine-tuned object detection models.

Florence-2

Florence-2 is a novel vision foundation model with a unified, prompt-based representation for a variety of computer vision and vision-language tasks.

A Python base cli tool for caption images with WD series, Joy-caption-pre-alpha,meta Llama 3.2 Vision Instruct and Qwen2 VL Instruct models.

vision language models finetuning notebooks & use cases (paligemma - florence .....)

Local LLM Discord Bot

Run SOTA Vision-Language Model Florence-2 on your data!

Simple Video Summarization using Text-to-Segment Anything (Florence2 + SAM2) This project provides a video processing tool that utilizes advanced AI models, specifically Florence2 and SAM2, to detect and segment specific objects or activities in a video based on textual descriptions.

Simple Gradio application integrated with Hugging Face Multimodals to support visual question answering chatbot and more features

TextSnap: Demo for Florence 2 model used in OCR tasks to extract and visualize text from images.

ecko-cli is a simple CLI tool that streamlines the process of processing images in a directory, generating captions, and saving them as text files. Additionally, it provides functionalities to create a JSONL file from images in the directory you specify. Images will be captioned using the Microsoft Florence-2-large model and ONNX

Video Synopsis: Intelligent Video Object Summarization using Florence/OWL-ViT and SAM. It uses OWL-ViT or Florence 2 for object detection, SAM for segmentation, and a custom video synopsis algorithm to produce optimized outputs.

Microsoft の軽量VLMのFlorence-2のColaboratory上でのサンプル

The Power of Florence-2 with OpenVINO & FiftyOne: Real-World Applications in Image Analysis