LVIS-INSTRUCT4V

Introduction

We introduce a fine-grained visual instruction dataset, LVIS-INSTRUCT4V, which contains 220K visually aligned and context-aware instructions produced by prompting the powerful GPT-4V with images from LVIS. Please refer to the arxiv paper for more details.

Usage

Please follow LLaVA to set up the code.

LVIS-INSTRUCT4V is available at LVIS-INSTRUCT4V. To achieve better results on the QA benchmarks, we follow LLaVA 1.5 to mix LVIS-INSTRUCT4V with academic task related data (see the Table 1 & 7 in LLaVA 1.5 paper), which can be found at LVIS-INSTRUCT4V-Nodetail-mix619k, LVIS-INSTRUCT4V-mix730k, and LVIS-Instruct4V-LLaVA-Instruct-mix880k.

Model Zoo

Version	Data	Size	Schedule	Checkpoint	VQAv2	GQA	VizWiz	SQA	T-VQA	POPE	MME	MM-Bench	MM-Bench-CN	SEED	LLaVA-Bench-Wild	MM-Vet
LLaVA-1.5	LVIS-Instruct4V-Nodetail-mix619k	7B	full_ft-1e	LVIS-Instruct4V-Nodetail-mix619k-7b	79.2	62.6	52.5	68.4	57.6	84.0	1472.9	67.1	60.0	60.8	70.4	34.6
LLaVA-1.5	LVIS-Instruct4V-mix730k	7B	full_ft-1e	LVIS-Instruct4V-mix730k-7b	79.4	62.6	52.6	68.9	58.4	85.1	1495.3	66.6	59.6	60.5	67.1	33.3
LLaVA-1.5	LVIS-Instruct4V-LLaVA-Instruct-mix880k	7B	full_ft-1e	LVIS-Instruct4V-LLaVA-Instruct-mix880k-7b	79.6	62.6	51.8	68.3	58.7	86.0	1528.2	66.2	60.4	60.6	67.0	31.5
LLaVA-1.51	LVIS-Instruct4V-Nodetail-mix619k	13B	full_ft-1e	LVIS-Instruct4V-Nodetail-mix619k-13b	80.1	63.8	51.4	69.0	62.1	85.3	1572.0	67.8	61.0	62.5	76.7	40.2
LLaVA-1.5	LVIS-Instruct4V-LLaVA-Instruct-mix880k	13B	full_ft-1e	LVIS-Instruct4V-LLaVA-Instruct-mix880k-13b	80.7	63.6	57.2	70.6	62.5	86.0	1574.9	68.0	61.1	61.6	71.3	37.4

Reference

If you find our work useful for your research or applications, please cite using this BibTeX:

@article{wang2023instruct4v,
  title={To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning},
  author={Wang, Junke and Meng, Lingchen and Weng, Zejia and He, Bo and Wu, Zuxuan and Jiang, Yu-Gang},
  journal={arXiv preprint arXiv:2311.07574},
  year={2023}
}

Acknowledgement

We thank the authors of LLaVA for their contribution to the open-source community.

Footnotes

1. We find TextQA is sensitive to the beam number, and for 13B models, we use beam = 3 on TextQA. ↩