Draw-and-Understand

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💥 News

• [2025.01.23] 🎉 Draw-and-Understand is accepted by ICLR 2025

• [2024.03.28] 🔥 We released the MDVP-Data dataset, MDVP-Bench benchmark, and the SPHINX-V-13B model.

• [2024.03.28] 🚀 We release the arXiv paper.

• [2024.03.28] 🚀 We released the traning and evaluation code.

👀 Introduction

The interaction between humans and artificial intelligence (AI) is a crucial factor that reflects the effectiveness of multimodal large language models (MLLMs). However, current MLLMs primarily focus on image-level comprehension and limit interaction to textual instructions, thereby constraining their flexibility in usage and depth of response. Therefore, we introduce the Draw-and-Understand project: a new model, a multi-domain dataset, and a challenging benchmark for visual prompting.

Specifically, the model is named SPHINX-V, a new multimodal large language model designed for visual prompting, equipped with a novel visual prompt encoder and a two-stage training strategy. SPHINX-V supports multiple visual prompts simultaneously across various types, significantly enhancing user flexibility and achieve a fine-grained and open-world understanding of visual prompts.

🚀 Examples Show

🛠️ Install

1. Clone this repository and navigate to Draw-and-Understand folder

git clone https://github.com/AFeng-x/Draw-and-Understand.git
cd Draw-and-Understand

2. Install packages

# Create a new conda environment named 'sphinx-v' with Python 3.10
conda create -n sphinx-v python=3.10 -y
# Activate the 'sphinx-v' environment
conda activate sphinx-v
# Install required packages from 'requirements.txt'
pip install -r requirements.txt

3. Optional: Install Flash-Attention

# Draw-and-Understand is powered by flash-attention for efficient attention computation.
pip install flash-attn --no-build-isolation

4. Install Draw-and-Understand as Python Package

# go to the root directory of Draw-and-Understand
cd Draw-and-Understand
# install Draw-and-Understand
pip install -e .
# After this, you will be able to invoke “import SPHINX_V” without the restriction of working directory.

5. To enable the segmentation ability shown in our official demo, SAM is also needed:

pip install git+https://github.com/facebookresearch/segment-anything.git

🤖️ Checkpoints

SPHINX-V-13b Stage-1 Pre-training Weight: 🤗Hugging Face / Baidu

SPHINX-V-13b Stage-2 Fine-tunings Weight: 🤗Hugging Face / Baidu

Other required weights and configurations: 🤗Hugging Face

Please download them to your own machine. The file structure should appear as follows:

accessory/checkpoints/sphinx-v/stage2
├── consolidated.00-of-02.model.pth
├── consolidated.01-of-02.model.pth
├── tokenizer.model
├── config.json
└── meta.json

accessory/checkpoints/llama-2-13b
├── params.json

accessory/checkpoints/tokenizer
├── tokenizer.model

📁 MDVP-Dataset

• MDVP-Data is a comprehensive dataset for multi-domain visual-prompt instruction tuning. This dataset encompasses data for both point-level and region-level understanding, designed to enhance a model’s comprehension ability and robustness.

• Based on MDVP-Data, we also introduce MDVP-Bench, a challenging benchmark designed to evaluate tasks that require a combination of detailed description referrals, inter-relationship analysis, and complex reasoning.

🚀 Training

• Prepare data

  • Please download the annotations of our pre-training data and images. (Refer to the Dataset Preparation)

• Stage 1: Image-Visual Prompt-Text Alignment Pre-training

  • Download the pretrained SPHINX-v2-1k Weights from Hugging face or Baidu(88z0). Place the model in the "accessory/checkpoints/sphinx-v2-1k" directory.
  • Download the ViT-H SAM model and place the model in the "accessory/checkpoints/sam" directory.
  • Pre-training configuration is vp_pretrain.yaml. Please ensure that all annotations are included and update the image paths in each JSON file to reflect the paths on your machine.
  • Update the model paths in the run script.
  • Run bash scripts/train_sphinx-v_pretrain_stage1.sh.

• Stage 2: Multi-Task End-to-End Supervised Finetuning

  • Download SPHINX-V Stage-1 Pre-training Weights from 🤖️Checkpoints. Alternatively, you may use your own model weights trained from Stage 1.
  • Place the model in the "accessory/checkpoints/sphinx-v/stage1" directory.
  • Fine-tuning configuration is vp_finetune.yaml. Please ensure that all annotations are included and update the image paths in each JSON file to reflect the paths on your machine.
  • Update the model paths in the run script.
  • Run bash scripts/train_sphinx-v_finetune_stage2.sh.

📈 Evaluation

See evaluation for details.

🛩️ Inference

We provide a simple example for inference in inference.py

You can launch this script with torchrun --master_port=1112 --nproc_per_node=1 inference.py

🪁 Host Local Demo

💻 requirments:

1. For this demo, it needs to prepare the SPHINX-V stage-2 checkpoints and ViT-H SAM model, and place them in the accessory/checkpoints/ directory.
2. Make sure you have installed Segment Anything.
3. Run.

cd accessory/demos
bash run.sh

💌 Acknowledgement

• LLaMA-Accessory: the codebase we built upon.
• SAM: the demo also uses the segmentation result from SAM.

🖊️: Citation

If you find our Draw-and-Understand project useful for your research and applications, please kindly cite using this BibTeX:

@misc{lin2024drawandunderstand,
      title={Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want}, 
      author={Weifeng Lin and Xinyu Wei and Ruichuan An and Peng Gao and Bocheng Zou and Yulin Luo and Siyuan Huang and Shanghang Zhang and Hongsheng Li},
      year={2024},
      eprint={2403.20271},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}