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+assets/data_pipeline.pdf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: mit
+task_categories:
+- visual-question-answering
+language:
+- en
+tags:
+- multimodal
+pretty_name: EMVista
+size_categories:
+- 1K<n<10K
+configs:
+  - config_name: default
+    data_files:
+      - split: test
+        path: data/test.parquet
+---
+
+# EMVista Dataset
+<center><h1>EMVista</h1></center>
+
+<p align="center">
+  <img src="./assets/pipeline.png" alt="EMVista" style="display: block; margin: auto; max-width: 70%;">
+</p>
+
+<p align="center">
+| <a href="https://emvista-benchmark.github.io"><b>Website</b></a> |
+<a href="https://arxiv.org/abs/XXXX.XXXXX"><b>Paper</b></a> |
+<a href="https://huggingface.co/datasets/EMVista/EMVista"><b>HuggingFace</b></a> |
+<a href="https://github.com/EMVista/EMVista"><b>Code</b></a> |
+</p>
+
+---
+
+## 🔥 Latest News
+
+- **[2026/01]** EMVista v1.0 is officially released.
+
+<!-- <details>
+<summary>Unfold to see more details.</summary>
+<br>
+
+- EMVista supports **English** prompts.
+
+</details> -->
+
+<!-- ---
+
+## Motivation: TODO
+
+<details>
+<summary>Unfold to see more details.</summary>
+<br>
+
+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**.
+
+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**.
+
+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.
+
+</details>
+
+--- -->
+## Overview
+**EMVista** is a benchmark for evaluating **instance-level microstructural understanding** in electron microscopy (EM) images across **three core capability
+dimensions**:
+
+1. **Microstructural Perception**  
+   Evaluates the ability to detect, delineate, and separate individual
+   microstructural instances in complex EM scenes.
+2. **Microstructural Attribute Understanding**  
+   Measures the capacity to interpret key microstructural attributes, including
+   morphology, density, spatial distribution, layering, and scale variation.
+3. **Robustness in Dense Scenes**  
+   Assesses model stability and accuracy under extreme instance crowding,
+   overlap, and multi-scale complexity.
+
+EMVista contains **expert-annotated EM images** with instance-level labels and
+structured attribute descriptions, designed to reflect **realistic challenges**
+in materials microstructure analysis.
+
+---
+## Dataset Characteristics
+
+- **Task Format**: Visual Question Answering (VQA)
+- **Modalities**: Image + Text
+- **Languages**: English
+- **Annotation**: Expert-verified
+---
+
+### Download EMVista Dataset
+
+You can download the EMVista dataset using the HuggingFace `datasets` library
+(make sure you have installed
+[HuggingFace Datasets](https://huggingface.co/docs/datasets/quickstart)):
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("InnovatorLab/EMVista")
+```
+
+## Evaluations
+
+We use [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) for evaluations. Please see [here](./evaluation/README.md) for detail files.
+
+## License
+
+EMVista is released under the MIT License. See [LICENSE](./LICENSE) for more details.
+
+<!-- ## Reference
+
+If you find EMVista useful in your research, please consider citing the following paper:
+
+```bibtex
+@misc{EMVista,
+      title={xxx}, 
+      author={xxx},
+      year={2026},
+      eprint={2506.10521},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/250xxxxxx}, 
+}
+``` -->

data/test.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:233fe06c519ea2c152bef81c5b8bf3a2fab178b2ff8fc707c782bbbe8d204610
+size 494669871

evaluation/README.md

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+# Evaluations of EMVista
+
+We evaluate the EMVista dataset using lmms-eval. The evaluation codes are listed in this folder.

evaluation/tasks/EMVista/EMVista.yaml

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+dataset_path: "InnovatorLab/EMVista"
+task: "EMVista"
+test_split: "test"
+output_type: "generate_until"
+
+doc_to_visual: !function utils.doc_to_visual
+doc_to_text: !function utils.doc_to_text
+doc_to_target: !function utils.doc_to_target
+
+generation_kwargs:
+  max_new_tokens: 256
+  temperature: 0.0
+  top_p: 1.0
+  num_beams: 1
+  do_sample: false
+
+process_results: !function utils.process_results
+
+metric_list:
+  - metric: exact_match_accuracy
+    aggregation: !function utils.aggregation
+    higher_is_better: true
+
+lmms_eval_specific_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "\nAnswer with the option's letter from the given choices directly."
+
+metadata:
+  - version: 1.0

evaluation/tasks/EMVista/utils.py

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+import re
+from PIL import Image
+
+def doc_to_visual(doc):
+    image = doc.get("image")
+    if isinstance(image, Image.Image):
+        return [image.convert("RGB")]
+    return []
+
+def doc_to_text(doc, lmms_eval_specific_kwargs=None):
+    pre_prompt = lmms_eval_specific_kwargs.get("pre_prompt", "") if lmms_eval_specific_kwargs else ""
+    post_prompt = lmms_eval_specific_kwargs.get("post_prompt", "") if lmms_eval_specific_kwargs else ""
+    content = doc.get("problem", "")
+    return f"{pre_prompt}{content}{post_prompt}"
+
+def doc_to_target(doc, lmms_eval_specific_kwargs=None):
+    full_answer = doc.get("answer", "")
+    match = re.search(r"([A-D])", str(full_answer))
+    return match.group(1) if match else full_answer
+
+def extract_characters_regex(s):
+    if not isinstance(s, str):
+        return ""
+    s = s.strip()
+    matches = re.search(r"\b([A-D])\b|(?<=\()([A-D])(?=\))", s.upper())
+    if matches:
+        return matches.group(1) if matches.group(1) else matches.group(2)
+    for char in ["A", "B", "C", "D"]:
+        if f" {char} " in f" {s.upper()} " or s.upper().startswith(char):
+            return char
+    return ""
+
+def process_results(doc, results):
+    prediction = results[0] if isinstance(results, list) else results
+    pred_ans = extract_characters_regex(prediction)
+    target_ans = doc_to_target(doc)
+    question = doc_to_text(doc)
+    is_correct = (pred_ans == str(target_ans)) if target_ans is not None else False
+    return {
+        "exact_match_accuracy": float(is_correct),
+        "question": question,            
+        "raw_output": prediction,        
+        "ground_truth": target_ans
+    }
+
+def aggregation(results):
+    return sum(results) / len(results) if results else 0.0