The model was trained on our self-built Infinity-MM dataset, which contains approximately 40 million image-text pairs. This dataset is a combination of open-source data collected from the internet and synthetic instruction data generated using open-source VLM models.
We have open-sourced Infinity-MM dataset and related resources. We hope you enjoy using them!
2024/11/19: We have released intermediate checkpoints obtained during different stages of training. Please feel free to use these models for analysis and experimentation.
We evaluated the model using the VLMEvalKit tool. Whenever possible, we prioritized using the OpenAI API for test sets that support API-based evaluation.
Benchmark
MiniCPM-V-2
InternVL2-2B
XinYuan-VL-2B
Qwen2-VL-2B-Instruct
Aquila-VL-2B
MMBench-ENtest
69.4
73.4
78.9
74.9
78.8
MMBench-CNtest
65.9
70.9
76.1
73.9
76.4
MMBench_V1.1test
65.2
69.7
75.4
72.7
75.2
MMT-Benchtest
54.5
53.3
57.2
54.8
58.2
RealWorldQA
55.4
57.3
63.9
62.6
63.9
HallusionBench
36.8
38.1
36.0
41.5
43.0
SEEDBench2plus
51.8
60.0
63.0
62.4
63.0
LLaVABench
66.1
64.8
42.4
52.5
68.4
MMStar
41.6
50.2
51.9
47.8
54.9
POPE
86.6
85.3
89.4
88.0
83.6
MMVet
44.0
41.1
42.7
50.7
44.3
MMMUval
39.6
34.9
43.6
41.7
47.4
ScienceQAtest
80.4
94.1
86.6
78.1
95.2
AI2Dtest
64.8
74.4
74.2
74.6
75.0
MathVistatestmini
39.0
45.0
47.1
47.9
59.0
MathVersetestmini
19.8
24.7
22.2
21.0
26.2
MathVision
15.4
12.6
16.3
17.5
18.4
DocVQAtest
71.0
86.9
87.6
89.9
85.0
InfoVQAtest
40.0
59.5
59.1
65.4
58.3
ChartQAtest
59.6
71.4
57.1
73.5
76.5
TextVQAval
74.3
73.5
77.6
79.9
76.4
OCRVQAtestcore
54.4
40.2
67.6
68.7
64.0
VCRen easy
27.6
51.6
67.7
68.3
70.0
OCRBench
613
784
782
810
772
Average
53.5
58.8
60.9
62.1
64.1
For comparison models, evaluations were conducted in a local environment, so the scores may differ slightly from those reported in papers or on the official VLMEvalKit leaderboard.
If you find this useful, please cite the following work
@misc{gu2024infinitymmscalingmultimodalperformance,
title={Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data},
author={Shuhao Gu and Jialing Zhang and Siyuan Zhou and Kevin Yu and Zhaohu Xing and Liangdong Wang and Zhou Cao and Jintao Jia and Zhuoyi Zhang and Yixuan Wang and Zhenchong Hu and Bo-Wen Zhang and Jijie Li and Dong Liang and Yingli Zhao and Yulong Ao and Yaoqi Liu and Fangxiang Feng and Guang Liu},
year={2024},
eprint={2410.18558},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.18558},
}