Recognize Anything: A Strong Image Tagging Model

Youcai Zhang, Xinyu Huang, Jinyu Ma, Zhaoyang Li, Zhaochuan Luo, Yanchun Xie, Yuzhuo Qin, Tong Luo, Yaqian Li, Shilong Liu, Yandong Guo, Lei Zhang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 1724-1732

We present the Recognize Anything Model (RAM): a strong foundation model for image tagging. RAM makes a substantial step for foundation models in computer vision demonstrating the zero-shot ability to recognize any common category with high accuracy. By leveraging large-scale image-text pairs for training instead of manual annotations RAM introduces a new paradigm for image tagging. 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 captioning 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 capability of RAM on numerous benchmarks and observe an impressive zero-shot performance which significantly outperforms CLIP and BLIP. Remarkably RAM even surpasses fully supervised models and exhibits a competitive performance compared with the Google tagging API. We have released RAM at https://recognize-anything.github.io/ to foster the advancement of foundation models in computer vision.