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### HumanEval-V: A Lightweight Visual Understanding and Reasoning Benchmark for Evaluating LMMs through Coding Tasks

<p align="center"> <a href="https://arxiv.org/abs/2410.12381">📄 Paper</a> • <a href="https://humaneval-v.github.io">🏠 Home Page</a> • <a href="https://github.com/HumanEval-V/HumanEval-V-Benchmark">💻 GitHub Repository</a> • <a href="https://humaneval-v.github.io/#leaderboard">🏆 Leaderboard</a> • <a href="https://huggingface.co/datasets/HumanEval-V/HumanEval-V-Benchmark">🤗 Dataset</a> • <a href="https://huggingface.co/spaces/HumanEval-V/HumanEval-V-Benchmark-Viewer">🤗 Dataset Viewer</a>  </p> 

**HumanEval-V** is a novel and lightweight benchmark designed to evaluate the visual understanding and reasoning capabilities of Large Multimodal Models (LMMs) through coding tasks. The dataset comprises **108 entry-level Python programming challenges**, adapted from platforms like CodeForces and Stack Overflow. Each task includes **visual context that is  indispensable to the problem**, requiring models to perceive, reason, and generate Python code solutions accordingly.

Key features:
- **Visual coding tasks** that require understanding images to solve.
- **Entry-level difficulty**, making it ideal for assessing the baseline performance of foundational LMMs.
- **Handcrafted test cases** for evaluating code correctness through an execution-based metric **pass@k**.