SY-Xuan / Pink

Pink: Unveiling the Power of Referential Comprehension for Multi-modal LLMs

Pink: Unveiling The Power of Referential Comprehension for Multi-modal LLMs.

Contents

• Data Download
• Install
• Training
• Evaluation
• Demo

Pink Weights

• Base: Pink_Base
• Base_Object365: Pink_Object365
• Base_RefCOCO: Pink_Refcoco

Data Download

Pretraining Dataset

The pretraining dataset used in this release is the same as in LLaVA which is a subset of CC-3M dataset. Please see here for a detailed description on the dataset structure and how to download the images.

Instruction Tuning Dataset

The datasets mentioned in the image need to be downloaded manually.

• COCO: train2017
• VisualGenome: part1, part2, objects, relationships, region descriptions
• Object365: Object365
• A-OKVQA: A-OKVQA
• LLaVA-158K: LLaVA-158K

We also provide the converted dataset used in the instruction tuning:

https://huggingface.co/datasets/SY-Xuan/Pink_sft/

LLaMA2 Weight Download

Our model is based on Llama-2-7b-chat-hf. You need to download the weights manually.

• Llama-2-7b-chat-hf: Llama-2-7b-chat-hf

Install

1. Install Package

conda create -n pink python=3.10 -y
conda activate pink
pip install --upgrade pip  # enable PEP 660 support
pip install -e .

Training

Stage 1

Please refer to scripts/stage1.sh.

Stage 2

Please refer to scripts/stage2.sh.

Stage 2 with Object365

Please refer to scripts/stage2_with_object365.sh.

Self-consistent Bootstrapping

We convert the *.json of Object365. Please refer to dataset_generation/object365_detection.py

Bootstrapping

Please refer to scripts/object365_generate.sh.

Self-consistent

Please refer to pink/eval/object365_filter.py

Evaluation

Please refer to inference.ipynb and scripts/eval_refcoco.sh.

Demo

To launch a Gradio web demo, use the following command.

python demo.py --checkpoint-path /path/to/pink --llama-path /path/to/llama2

Citation

If you find Pink useful for your research and applications, please cite using this BibTeX:

@article{xuan2023pink,
  title={Pink: Unveiling the power of referential comprehension for multi-modal llms},
  author={Xuan, Shiyu and Guo, Qingpei and Yang, Ming and Zhang, Shiliang},
  journal={arXiv preprint arXiv:2310.00582},
  year={2023}
}

Acknowledgement

This code inherits some codes from LLaVA and Shikra. Thanks for these outstanding implementations.

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