VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models

TL;DR

VLMEvalKit is an open-source, comprehensive toolkit designed to evaluate large multi-modality models, supporting over 200 models and 80 benchmarks, and facilitating reproducible research in vision-language and future modalities.

Contribution

It introduces a unified, user-friendly framework for evaluating multi-modality models, streamlining data handling, inference, and metrics, and hosts a leaderboard to track progress.

Findings

Evaluated over 200 models across 80 benchmarks.

Streamlined evaluation process with a single interface.

Supports future modalities like audio and video.

Abstract

We present VLMEvalKit: an open-source toolkit for evaluating large multi-modality models based on PyTorch. The toolkit aims to provide a user-friendly and comprehensive framework for researchers and developers to evaluate existing multi-modality models and publish reproducible evaluation results. In VLMEvalKit, we implement over 200+ different large multi-modality models, including both proprietary APIs and open-source models, as well as more than 80 different multi-modal benchmarks. By implementing a single interface, new models can be easily added to the toolkit, while the toolkit automatically handles the remaining workloads, including data preparation, distributed inference, prediction post-processing, and metric calculation. Although the toolkit is currently mainly used for evaluating large vision-language models, its design is compatible with future updates that incorporate additional modalities, such as audio and video. Based on the evaluation results obtained with the toolkit, we host OpenVLM Leaderboard, a comprehensive leaderboard to track the progress of multi-modality learning research. The toolkit is released on https://github.com/open-compass/VLMEvalKit and is actively maintained.