VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality
  Models
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 70 different large multi-modality
models, including both proprietary APIs and open-source models, as well as more
than 20 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
at https://github.com/open-compass/VLMEvalKit and is actively maintained.
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Haodong Duan
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Xinyu Fang
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Xinyu Fang
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Lin Chen
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Lin Chen
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Yuan Liu
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Yuan Liu
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Xiaoyi Dong
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Yuhang Zang
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Yuhang Zang
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Pan Zhang
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Jiaqi Wang
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Jiaqi Wang
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Dahua Lin
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Kai Chen
VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality
  Models
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 70 different large multi-modality
models, including both proprietary APIs and open-source models, as well as more
than 20 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
at https://github.com/open-compass/VLMEvalKit and is actively maintained.
VLMEvalKit is a PyTorch-based toolkit for evaluating large multi-modality models with a focus on vision-language models, offering a comprehensive framework for reproducible results and a leaderboard for research tracking.
multi-modality models PyTorch multi-modal benchmarks distributed inference prediction post-processing metric calculation OpenVLM Leaderboard