--- dataset_info: features: - name: question_id dtype: string - name: question dtype: string - name: choices dtype: string - name: answer dtype: string - name: query_image dtype: image - name: choice_image_0 dtype: image - name: choice_image_1 dtype: image - name: ques_type dtype: string - name: label dtype: string - name: grade dtype: string - name: skills dtype: string splits: - name: val num_bytes: 329185883.464 num_examples: 21488 - name: test num_bytes: 333201645.625 num_examples: 21489 download_size: 667286379 dataset_size: 662387529.089 configs: - config_name: default data_files: - split: val path: data/val-* - split: test path: data/test-* ---

# Large-scale Multi-modality Models Evaluation Suite > Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval` 🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) # This Dataset This is a formatted version of [ICONQA](https://iconqa.github.io/). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @inproceedings{lu2021iconqa, title = {IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning}, author = {Lu, Pan and Qiu, Liang and Chen, Jiaqi and Xia, Tony and Zhao, Yizhou and Zhang, Wei and Yu, Zhou and Liang, Xiaodan and Zhu, Song-Chun}, booktitle = {The 35th Conference on Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks}, year = {2021} } ```