title: Recognize Anything: A Strong Image Tagging Model
summary: We present the Recognize Anything Model (RAM): a strong foundation model for
  image tagging. RAM can recognize any common category with high accuracy. RAM
  introduces a new paradigm for image tagging, leveraging large-scale image-text
  pairs for training instead of manual annotations. The development of RAM
  comprises four key steps. Firstly, annotation-free image tags are obtained at
  scale through automatic text semantic parsing. Subsequently, a preliminary
  model is trained for automatic annotation by unifying the caption and tagging
  tasks, supervised by the original texts and parsed tags, respectively. Thirdly,
  a data engine is employed to generate additional annotations and clean
  incorrect ones. Lastly, the model is retrained with the processed data and
  fine-tuned using a smaller but higher-quality dataset. We evaluate the tagging
  capabilities of RAM on numerous benchmarks and observe impressive zero-shot
  performance, significantly outperforming CLIP and BLIP. Remarkably, RAM even
  surpasses the fully supervised manners and exhibits competitive performance
  with the Google API. We are releasing the RAM at
  https://recognize-anything.github.io/ to foster the advancements of large
  models in computer vision.
paper_authors._id: 647fe64773f7b4386b3dacfc
paper_authors.name: Youcai Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-12T14:00:42.788Z
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paper_authors.avatarUrl: /avatars/306decb0cee314df2c4bf8ad61938147.svg
paper_authors.fullname: Youcai Zhang
paper_authors.type: user
paper_authors.user: Coler
paper_authors._id: 647fe64773f7b4386b3dacfd
paper_authors.name: Xinyu Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-12T14:01:19.910Z
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paper_authors.fullname: Xinyu Huang
paper_authors.type: user
paper_authors.user: xinyu1205
paper_authors._id: 647fe64773f7b4386b3dacfe
paper_authors.name: Jinyu Ma
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-07T10:10:11.507Z
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/WTqhKzdDKb5MLUdyNMyUn.png
paper_authors.fullname: Ma Jinyu
paper_authors.type: user
paper_authors.user: majinyu
paper_authors._id: 647fe64773f7b4386b3dacff
paper_authors.name: Zhaoyang Li
paper_authors._id: 647fe64773f7b4386b3dad00
paper_authors.name: Zhaochuan Luo
paper_authors._id: 647fe64773f7b4386b3dad01
paper_authors.name: Yanchun Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-12T14:02:29.955Z
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paper_authors.fullname: yanchun xie
paper_authors.type: user
paper_authors.user: xyc
paper_authors._id: 647fe64773f7b4386b3dad02
paper_authors.name: Yuzhuo Qin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-12T14:02:46.548Z
paper_authors._id: 642bfd89c01eb0e148ff6974
paper_authors.avatarUrl: /avatars/0429680c575ad38d04362361e74d7a67.svg
paper_authors.fullname: Yuzhuo Qin
paper_authors.type: user
paper_authors.user: Jacobqyz
paper_authors._id: 647fe64773f7b4386b3dad03
paper_authors.name: Tong Luo
paper_authors._id: 647fe64773f7b4386b3dad04
paper_authors.name: Yaqian Li
paper_authors._id: 647fe64773f7b4386b3dad05
paper_authors.name: Shilong Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T07:48:25.036Z
paper_authors._id: 638b13c0c1d591879698f4e2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/638b13c0c1d591879698f4e2/X8X4EWMXuzhBpG62wO2xS.jpeg
paper_authors.fullname: Shilong Liu
paper_authors.type: user
paper_authors.user: ShilongLiu
paper_authors._id: 647fe64773f7b4386b3dad06
paper_authors.name: Yandong Guo
paper_authors._id: 647fe64773f7b4386b3dad07
paper_authors.name: Lei Zhang
paper_title: Recognize Anything: A Strong Image Tagging Model
paper_summary: We present the Recognize Anything Model (RAM): a strong foundation model for
  image tagging. RAM can recognize any common category with high accuracy. RAM
  introduces a new paradigm for image tagging, leveraging large-scale image-text
  pairs for training instead of manual annotations. The development of RAM
  comprises four key steps. Firstly, annotation-free image tags are obtained at
  scale through automatic text semantic parsing. Subsequently, a preliminary
  model is trained for automatic annotation by unifying the caption and tagging
  tasks, supervised by the original texts and parsed tags, respectively. Thirdly,
  a data engine is employed to generate additional annotations and clean
  incorrect ones. Lastly, the model is retrained with the processed data and
  fine-tuned using a smaller but higher-quality dataset. We evaluate the tagging
  capabilities of RAM on numerous benchmarks and observe impressive zero-shot
  performance, significantly outperforming CLIP and BLIP. Remarkably, RAM even
  surpasses the fully supervised manners and exhibits competitive performance
  with the Google API. We are releasing the RAM at
  https://recognize-anything.github.io/ to foster the advancements of large
  models in computer vision.
paper_ai_summary: The Recognize Anything Model (RAM) is a powerful image tagging model trained using large-scale image-text pairs, achieving superior zero-shot performance compared to supervised models and Google API.
paper_ai_keywords: foundation model image tagging large-scale image-text pairs annotation-free image tags text semantic parsing caption and tagging tasks data engine zero-shot performance CLIP BLIP