Finedefics Open-Source Multimodal Large Language Model - Free Deployment to Boost Fine-Grained Visual Recognition

Finedefics

Developed by StevenHH2000

Finedefics is an open-source multimodal large language model (MLLM) that enhances fine-grained visual recognition (FGVR) capabilities by incorporating object attribute descriptions.

Image-to-Text

Safetensors

#Fine-grained Visual Recognition #Multimodal Large Language Model #Object Attribute Enhancement

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Release Time : 2/12/2025

Model Overview

Finedefics is an autoregressive language model based on the Transformer architecture, primarily used for research in fine-grained multimodal large language models, applicable to fields such as computer vision and natural language processing.

Model Features

Enhanced Fine-grained Visual Recognition

By incorporating object attribute descriptions during the training phase, the model's fine-grained visual recognition capabilities are significantly improved.

Multimodal Capabilities

Combines visual and textual information to support image-to-text conversion and understanding.

Open-source Model

Built upon the open-source model Idefics2-8b, licensed under Apache 2.0, facilitating research and application.

Model Capabilities

Fine-grained Visual Recognition

Multimodal Understanding

Image-to-Text Conversion

Use Cases

Computer Vision Research

Fine-grained Object Classification

Used for identifying and classifying fine-grained objects, such as different breeds of dogs, birds, etc.

Natural Language Processing

Multimodal Question Answering

Performs question-answering tasks by combining image and text information.

🚀 Finedefics Model Card

Finedefics is an open - source MLLM that focuses on enhancing fine - grained visual recognition (FGVR) capabilities. It incorporates object attribute descriptions during training and is based on a transformer - based auto - regressive language model.

🚀 Quick Start

No specific quick - start steps are provided in the original document.

✨ Features

Finedefics is an open - source MLLM.
It enhances the model's FGVR capability by incorporating informative attribute descriptions of objects into the training phase.
It is an auto - regressive language model based on the transformer architecture.

📦 Installation

No installation steps are provided in the original document.

📚 Documentation

Model details

Property	Details
Model Type	Finedefics is an open - source MLLM that enhances the model's FGVR capability by incorporating informative attribute descriptions of objects into the training phase. It is an auto - regressive language model, based on the transformer architecture. Base MLLM: [HuggingFaceM4/idefics2 - 8b](https://huggingface.co/HuggingFaceM4/idefics2 - 8b)
Paper or resources for more information	OpenReview: https://openreview.net/forum?id=p3NKpom1VL Arxiv: https://arxiv.org/abs/2501.15140

Intended use

Primary intended uses: The primary use of Finedefics is research on Fine - grained MLLM.

Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

Training and evaluation datasets

A collection of 6 fine - grained visual recognition datasets, including Stanford Dog - 120, Bird - 200, FGVC - Aircraft, Flower - 102, Oxford - IIIT Pet - 37, and Stanford Car - 196.

📄 License

Idefics2 is licensed under the Apache 2.0 license, and we release the Finedefics checkpoints under the same license.

Where to send questions or comments about the model: https://github.com/PKU-ICST-MIPL/Finedefics_ICLR2025/issues

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