VLMEvalKit

VLMEvalKit

VLMEvalKit is an open-source evaluation toolkit designed for large vision-language models (LVLMs). It simplifies the evaluation process by allowing one-command evaluations across various benchmarks without the need for extensive data preparation. The toolkit supports over 220 LVLMs and 80 benchmarks, making it a comprehensive resource for researchers and developers in the field of AI.

Key Features:

• One-command Evaluation: Evaluate LVLMs effortlessly on multiple benchmarks.
• Wide Support: Compatible with 220+ LMMs and 80+ benchmarks, including commercial APIs and open-source models.
• Generation-based Evaluation: Utilizes generation-based methods for accurate assessments of model performance.
• Community Contributions: Encourages contributions from the community, with recognition for significant contributors.

Benefits:

• Ease of Use: Streamlined evaluation process reduces the workload for researchers.
• Reproducibility: Designed to reproduce accuracy numbers reported in original papers, enhancing reliability.
• Flexibility: Supports custom benchmarks and models, allowing for tailored evaluations.

Highlights:

• Comprehensive survey on evaluation methodologies for multi-modality models.
• Active community engagement through Discord and GitHub for feedback and contributions.
• Regular updates and support for the latest transformer versions to ensure compatibility.

For more information, visit the VLMEvalKit GitHub page.