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.
