Welcome to the fascinating world of GroundingGPT! This end-to-end multimodal grounding model leverages the power of language to accurately comprehend and help understand inputs across various modalities such as images, audio, and video. In this blog, we’ll walk you through everything you need to know about installing, preparing, and using GroundingGPT for your projects.

Introduction

GroundingGPT addresses the challenges posed by limited data by introducing a high-quality multimodal training dataset. This resource, enriched with spatial and temporal information, is essential for advancing the understanding of grounding tasks across multiple modalities. Our extensive evaluations show that GroundingGPT excels in comprehending complex input formats.

For detailed insights, you can visit our project page.

Installation Steps

Setting up GroundingGPT involves a few straightforward steps. Let’s break it down:

• Clone the GroundingGPT repository:

git clone https://github.com/lzw-lzw/GroundingGPT.git

• Navigate into the cloned directory:

cd GroundingGPT

• Create a new Conda environment and activate it:

conda create -n groundinggpt python=3.10 -y
conda activate groundinggpt

• Install the required packages:

pip install -r requirements.txt
pip install flash-attn --no-build-isolation

Preparing for Training

Follow the steps below to prepare your training model:

1. Model Checkpoint Preparation

Place the necessary checkpoints into the “.ckpt” directory:

• Download the ImageBind checkpoint and place it in .ckpt/imagebind using the following link: imagebind_huge.pth.
• Download the BLIP2 checkpoint and place it in .ckpt using the following link: blip2_pretrained_flant5xxl.pth.

2. Training Dataset Preparation

Next, gather your training datasets. You’ll need to download and organize them as follows:

• LLaVA
• COCO
• GQA
• And other datasets like Valley, DiDeMO, ActivityNet Captions, Charades-STA, VGGSS, WaveCaps, and Clotho.

Running Inference and Demo

Once you’ve set up everything, it’s time for inference:

Inference

• Download the model from GroundingGPT-7B, and update the model_path in GroundingGPT/legoservecli.py.
• Run the inference script:

python3 legoservecli.py

Demo

• Download the model from GroundingGPT-7B and update the model_path in line 141 of GroundingGPT/legoservegradio_web_server.py.
• Launch the Gradio web demo:

python3 legoservegradio_web_server.py

Troubleshooting

If you run into issues during installation or while running GroundingGPT, here are a few troubleshooting ideas:

• Ensure that all dependencies are correctly installed.
• Double-check the model checkpoint paths if the model fails to load.
• Confirm the dataset paths are correctly set up and accessible.
• If you still face issues, reach out for advice and support.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Now you’re ready to get started with GroundingGPT! Explore the capabilities of this powerful model and see how it can enhance your multimodal projects.