We propose the challenging ChartBench to evaluate the chart recognition of MLLMs.
We improve the Acc+ metric to avoid the randomly guessing situations.
We collect a larger set of unlabeled charts to emphasize the MLLM's ability to interpret visual information without the aid of annotated data points.
Please follow the official repository instructions below to set up the local environment.
./Repos/utils.py
CKPT_PATH
in ./Repos/{MODEL_NAME}/infer.py
load_model
and model_gen
functions./Result/raw/{MODEL_NAME}.jsonl
by default./Stat/gpt_filter.py
to extract number values in NQA task./Stat/stat_all_metric.py
and the statistical results are saved in ./Stat/Paper_Table
@article{ChartBench,
title={ChartBench: A Benchmark for Complex Visual Reasoning in Charts},
author={Zhengzhuo Xu and Sinan Du and Yiyan Qi and Chengjin Xu and Chun Yuan and Jian Guo},
journal={ArXiv},
year={2023},
volume={abs/2312.15915},
url={https://api.semanticscholar.org/CorpusID:266550948}
}