---
license: apache-2.0
pretty_name: EPIC-Bench
task_categories:
  - visual-question-answering
  - object-detection
language:
  - en
Modalities:
  - Image
  - Text
tags:
  - embodied-perception
  - mask-grounding
  - vision-language-models
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: metadata.jsonl
---


<div align="center">
  
# 🎯 EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models

[![arXiv](https://img.shields.io/badge/arXiv-coming_soon-b31b1b.svg)](https://epic-bench.github.io/EPIC-Bench/)
[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://epic-bench.github.io/EPIC-Bench/)
[![Dataset](https://img.shields.io/badge/🤗-Dataset-yellow)](https://huggingface.co/datasets/rxc205/EPIC-Bench)
[![Evaluation Toolkit](https://img.shields.io/badge/⚙️-Evaluation_Toolkit-6366f1.svg)](https://github.com/rxc205/EPIC-Bench-Eval#-epic-bench-evaluation-toolkit)
[![License](https://img.shields.io/badge/License-Apache_2.0-green.svg)](https://github.com/rxc205/EPIC-Bench-Eval/blob/main/LICENSE)
</div>

## 📃 Overview


📚 **EPIC-Bench** is a **Mask-Grounding-based** benchmark designed to evaluate a VLM’s **Visual Perception** capability in **Embodied Scenarios**. EPIC-Bench covers **3 High-Level Categories** and **23 Task Types**, following the realistic **Embodied Workflow**:

- 🎯 **TargetLocalization**: **Pinpoint** the right object in the scene from a natural-language instruction.
- 🧭 **Navigation**: **Approach** the target step by step by reading key visual cues along the way.
- 🤲 **Manipulation**: **Operate** on the target through fine-grained, action-oriented **Grounded Perception**.

<p align="center">
  <img src="https://epic-bench.github.io/EPIC-Bench/img/20260302-192636.png" alt="EPIC-Bench teaser" width="100%"/>
</p>

The goal is to measure whether models can reliably perceive the critical **Visual** information required throughout the **Embodied Process**.

<p align="center">
  <img src="https://raw.githubusercontent.com/rxc205/EPIC-Bench-Eval/refs/heads/main/images/bmk_cases.png" alt="EPIC-Bench bmk_cases" width="100%"/>
</p>
<p align="center">
  <em>Example visualization of EPIC-Bench. For more, visit our <a href="https://epic-bench.github.io/EPIC-Bench/">Project Page</a> or <a href="https://huggingface.co/datasets/rxc205/EPIC-Bench">download the dataset</a> to explore the full benchmark locally.</em>
</p>

## ✨ Highlights

-  **Embodied-Scenario** evaluation of VLM **Visual Perception** capability.
-  Focus on **Visual Grounding / Perception** without language shortcut exploitation.
-  **Diverse** and **Fine-Grained** task design.

## 📰 News

- [2026.5.15] 🚀 [HuggingFace](https://huggingface.co/datasets/rxc205/EPIC-Bench) and [ModelScope](https://www.modelscope.cn/datasets/macarich/EPIC-Bench) Dataset are available!
- [2026.5.15] 🚀 [Project Page](https://epic-bench.github.io/EPIC-Bench/) and [Evaluation Code](https://github.com/rxc205/EPIC-Bench-Eval) are released, the arXiv paper will come soon.

## 📋 Todo

- [x] Evaluation code for EPIC-Bench
- [x] The EPIC-Bench datasets
- [ ] Make the evaluation pipeline compatible with mask outputs


## 🏆 Leaderboard and Benchmark

Please refer to the [EPIC-Bench Homepage](https://epic-bench.github.io/EPIC-Bench/) for:

- Leaderboard
- Full dataset downloads
- EPIC-Bench data examples

## 📚 Citation

```BibTeX
@article{EPIC-Bench,
  title={EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models},
  author={XXX, XXX, XXX},
  journal={},
  year={2026}
}
```

## 📜 License


This project is licensed under the Apache License 2.0 - see the [LICENSE](https://github.com/rxc205/EPIC-Bench-Eval/blob/main/LICENSE) file for details.

## 🙏 Acknowledgements

- **ms-swift** for open-source VLM inference: [ms-swift](https://swift.readthedocs.io/zh-cn/latest/)
- **lmms-eval** for API/closed-source evaluation: [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval)