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

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.