OpenVision: A Fully-Open, Cost-Effective Family of Advanced Vision
  Encoders for Multimodal Learning
OpenAI's CLIP, released in early 2021, have long been the go-to choice of
vision encoder for building multimodal foundation models. Although recent
alternatives such as SigLIP have begun to challenge this status quo, to our
knowledge none are fully open: their training data remains proprietary and/or
their training recipes are not released. This paper fills this gap with
OpenVision, a fully-open, cost-effective family of vision encoders that match
or surpass the performance of OpenAI's CLIP when integrated into multimodal
frameworks like LLaVA. OpenVision builds on existing works -- e.g., CLIPS for
training framework and Recap-DataComp-1B for training data -- while revealing
multiple key insights in enhancing encoder quality and showcasing practical
benefits in advancing multimodal models. By releasing vision encoders spanning
from 5.9M to 632.1M parameters, OpenVision offers practitioners a flexible
trade-off between capacity and efficiency in building multimodal models: larger
models deliver enhanced multimodal performance, while smaller versions enable
lightweight, edge-ready multimodal deployments.
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Xianhang Li
681d1be954749869547df71f
Yanqing Liu
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2025-09-04T08:48:30.048Z
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Liuyanqing
user
Yanqing0327
681d1be954749869547df720
Haoqin Tu
claimed_verified
2025-09-02T07:57:35.231Z
604ae011caabafacfa48e3de
https://cdn-avatars.huggingface.co/v1/production/uploads/1615519738679-noauth.jpeg
Haoqin Tu
user
PahaII
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Hongru Zhu
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Cihang Xie
claimed_verified
2025-07-31T08:16:29.568Z
645eb61da3c5cd8a16efffff
/avatars/9112bfeed598dfabf9e077e69e09ecc9.svg
Cihang Xie
user
cihangxie
OpenVision: A Fully-Open, Cost-Effective Family of Advanced Vision
  Encoders for Multimodal Learning
OpenAI's CLIP, released in early 2021, have long been the go-to choice of
vision encoder for building multimodal foundation models. Although recent
alternatives such as SigLIP have begun to challenge this status quo, to our
knowledge none are fully open: their training data remains proprietary and/or
their training recipes are not released. This paper fills this gap with
OpenVision, a fully-open, cost-effective family of vision encoders that match
or surpass the performance of OpenAI's CLIP when integrated into multimodal
frameworks like LLaVA. OpenVision builds on existing works -- e.g., CLIPS for
training framework and Recap-DataComp-1B for training data -- while revealing
multiple key insights in enhancing encoder quality and showcasing practical
benefits in advancing multimodal models. By releasing vision encoders spanning
from 5.9M to 632.1M parameters, OpenVision offers practitioners a flexible
trade-off between capacity and efficiency in building multimodal models: larger
models deliver enhanced multimodal performance, while smaller versions enable
lightweight, edge-ready multimodal deployments.
OpenVision provides a fully-open, cost-effective family of vision encoders that matches or surpasses OpenAI's CLIP in multimodal frameworks, offering flexible trade-offs between model size and performance.
CLIP SigLIP multimodal foundation models LLaVA Recap-DataComp-1B vision encoders