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 \url{https://recognize-anything.github.io/} to foster the advancements of large models in computer vision.

翻译：我们提出了“识别万物模型”（RAM）：一个用于图像标注的强大基础模型。RAM能够高精度地识别任意常见类别。RAM引入了一种新的图像标注范式，利用大规模图文对进行训练而无需人工标注。RAM的开发包含四个关键步骤。首先，通过自动文本语义解析，大规模获取无标注的图像标签。随后，通过统一描述和标注任务，由原始文本和解析后的标签分别监督，训练一个初步模型用于自动标注。第三，采用数据引擎生成额外标注并清理错误标注。最后，使用处理后的数据重新训练模型，并使用更小但更高质量的数据集进行微调。我们在多个基准测试上评估了RAM的标注能力，观察到其强大的零样本性能，显著优于CLIP和BLIP。值得注意的是，RAM甚至超越了全监督方法，并与谷歌API的性能相匹敌。我们在\url{https://recognize-anything.github.io/}上发布RAM，以促进计算机视觉领域大模型的发展。