GroundingGPT is a multimodal grounding model designed for accurate comprehension and robust grounding across images, audio, and video. It targets researchers and developers working on advanced multimodal AI, offering a unified approach to complex grounding tasks and providing a valuable, diverse training dataset to advance the field.

How It Works

GroundingGPT employs a language-enhanced architecture to integrate multimodal inputs. It leverages pre-trained models like ImageBind and BLIP-2, fine-tuning them for enhanced spatial and temporal understanding. This approach aims to improve accuracy and robustness in grounding by effectively combining linguistic context with visual and auditory information.

Quick Start & Requirements

• Install: Clone the repository, create a conda environment (conda create -n groundinggpt python=3.10), activate it, and install dependencies (pip install -r requirements.txt).
• Prerequisites: Requires checkpoints for ImageBind (imagebind_huge.pth) and BLIP-2 (blip2_pretrained_flant5xxl.pth), which need to be downloaded and placed in the ./ckpt/ directory. Various datasets (LLaVA, COCO, GQA, etc.) are also required, with instructions to follow their respective repositories.
• Demo: Launch a Gradio web demo with python3 lego/serve/gradio_web_server.py after downloading the GroundingGPT-7B model and updating the model path.
• Inference: Run inference using python3 lego/serve/cli.py after downloading the GroundingGPT-7B model and updating the model path.
• Links: Project page: https://github.com/lzw-lzw/GroundingGPT

Highlighted Details

• End-to-end multimodal grounding model.
• Supports images, audio, and video inputs.
• Includes a diverse, high-quality multimodal training dataset with spatial and temporal information.
• Accepted to ACL 2024.

Maintenance & Community

The project is associated with the ACL 2024 conference. Further community or maintenance details are not explicitly provided in the README.

Licensing & Compatibility

The repository does not explicitly state a license. Given the academic nature (ACL 2024) and reliance on other models, users should verify licensing for commercial use and closed-source integration.

Limitations & Caveats

The setup requires downloading multiple large checkpoints and datasets, which can be time-consuming and resource-intensive. The README implies a focus on research and may not be optimized for production deployment without further adaptation.