Recognize Anything: A Strong Image Tagging Model
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.
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Youcai Zhang
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2023-06-12T14:00:42.788Z
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Youcai Zhang
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Xinyu Huang
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2023-06-12T14:01:19.910Z
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Xinyu Huang
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Jinyu Ma
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2023-06-07T10:10:11.507Z
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Ma Jinyu
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Zhaoyang Li
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Zhaochuan Luo
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Yanchun Xie
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2023-06-12T14:02:29.955Z
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yanchun xie
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Yuzhuo Qin
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2023-06-12T14:02:46.548Z
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Yuzhuo Qin
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Tong Luo
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Yaqian Li
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Shilong Liu
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2025-05-29T07:48:25.036Z
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Shilong Liu
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ShilongLiu
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Yandong Guo
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Lei Zhang
Recognize Anything: A Strong Image Tagging Model
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.
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.
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