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--- |
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license: mit |
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task_categories: |
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- visual-question-answering |
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language: |
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- en |
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tags: |
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- multimodal |
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pretty_name: EMVista |
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size_categories: |
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- 1K<n<10K |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/test.parquet |
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--- |
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# EMVista Dataset |
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<center><h1>EMVista</h1></center> |
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<p align="center"> |
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<img src="./assets/pipeline.png" alt="EMVista" style="display: block; margin: auto; max-width: 70%;"> |
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</p> |
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<p align="center"> |
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<a href="https://huggingface.co/datasets/EMVista/EMVista"><b>HuggingFace</b></a> |
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</p> |
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--- |
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## 🔥 Latest News |
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- **[2026/01]** EMVista v1.0 is officially released. |
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<!-- <details> |
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<summary>Unfold to see more details.</summary> |
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<br> |
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- EMVista supports **English** prompts. |
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</details> --> |
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<!-- --- |
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## Motivation: TODO |
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<details> |
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<summary>Unfold to see more details.</summary> |
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<br> |
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Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated impressive performance on generic vision-language benchmarks. However, most existing benchmarks primarily assess **coarse-grained perception** or **commonsense visual understanding**, falling short in evaluating models’ abilities to reason over **complex, expert-level visual information**. |
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In realistic applications—such as scientific analysis, technical inspection, diagram interpretation, and abstract visual reasoning—models must go beyond recognizing objects or captions. They need to **extract structured visual cues**, **understand implicit visual attributes**, and **perform multi-step reasoning across multiple visual sources**. |
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To address this gap, we introduce **EMVista**, a benchmark designed to systematically evaluate multimodal models’ **visual understanding and reasoning capabilities** through carefully curated expert-level visual tasks. |
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</details> |
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--- --> |
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## Overview |
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**EMVista** is a benchmark for evaluating **instance-level microstructural understanding** in electron microscopy (EM) images across **three core capability |
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dimensions**: |
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1. **Microstructural Perception** |
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Evaluates the ability to detect, delineate, and separate individual |
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microstructural instances in complex EM scenes. |
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2. **Microstructural Attribute Understanding** |
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Measures the capacity to interpret key microstructural attributes, including |
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morphology, density, spatial distribution, layering, and scale variation. |
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3. **Robustness in Dense Scenes** |
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Assesses model stability and accuracy under extreme instance crowding, |
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overlap, and multi-scale complexity. |
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EMVista contains **expert-annotated EM images** with instance-level labels and |
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structured attribute descriptions, designed to reflect **realistic challenges** |
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in materials microstructure analysis. |
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--- |
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## Dataset Characteristics |
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- **Task Format**: Visual Question Answering (VQA) |
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- **Modalities**: Image + Text |
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- **Languages**: English |
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- **Annotation**: Expert-verified |
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--- |
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### Download EMVista Dataset |
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You can download the EMVista dataset using the HuggingFace `datasets` library |
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(make sure you have installed |
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[HuggingFace Datasets](https://huggingface.co/docs/datasets/quickstart)): |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("InnovatorLab/EMVista") |
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``` |
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## Evaluations |
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We use [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) for evaluations. Please see [here](./evaluation/README.md) for detail files. |
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## License |
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EMVista is released under the MIT License. See [LICENSE](./LICENSE) for more details. |
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## Citation |
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```bibtex |
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@article{wen2026innovator, |
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title={Innovator-VL: A Multimodal Large Language Model for Scientific Discovery}, |
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author={Wen, Zichen and Yang, Boxue and Chen, Shuang and Zhang, Yaojie and Han, Yuhang and Ke, Junlong and Wang, Cong and others}, |
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journal={arXiv preprint arXiv:2601.19325}, |
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year={2026} |
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} |
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``` |
