Recognize Anything: A Strong Image Tagging Model
Authors: name: Youcai Zhang; name: Xinyu Huang; name: Jinyu Ma; name: Zhaoyang Li; name: Zhaochuan Luo; name: Yanchun Xie; name: Yuzhuo Qin; name: Tong Luo; name: Yaqian Li; name: Shilong Liu; name: Yandong Guo; name: Lei Zhang
Published: 2023-06-06 09:00:10

Abstract: 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.