Recognize Anything Model
Recognize Anything Model by xinyu1205, a image-to-text model with object detection capabilities. Understand and compare object detection features, benchmarks, and capabilities.
Comparison
| Feature | Recognize Anything Model | Interfaze |
|---|---|---|
| Input Modalities | image | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | Yes | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | unknown | 1M |
| Tool Calling | No | Tool calling supported + built in browser, code execution and web search |
Object Detection Capabilities
| Feature | Recognize Anything Model | Interfaze |
|---|---|---|
| Object Bounding Boxes | No | Yes |
| Object Segmentation Masks | No | Yes |
| Confidence Scores | No | Yes |
| Dense Image Processing | No | Yes |
| Low Quality Images | No | Yes |
| Industry-Specific | No | Yes |
| GUI Element Detection | No | Yes |
Scaling
| Feature | Recognize Anything Model | Interfaze |
|---|---|---|
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |
View model card on Hugging Face
Model card for Recognize Anything: A Strong Image Tagging Model and Tag2Text: Guiding Vision-Language Model via Image Tagging.
Recognition and localization are two foundation computer vision tasks.
- The Segment Anything Model (SAM) excels in localization capabilities, while it falls short when it comes to recognition tasks.
- The Recognize Anything Model (RAM) and Tag2Text exhibits exceptional recognition abilities, in terms of both accuracy and scope.
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| Pull figure from recognize-anything official repo |
TL;DR
Authors from the paper write in the abstract:
We present the Recognize Anything Model~(RAM): a strong foundation model for image tagging. RAM makes a substantial step for large 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. 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.
BibTex and citation info
@article{zhang2023recognize,
title={Recognize Anything: A Strong Image Tagging Model},
author={Zhang, Youcai and Huang, Xinyu and Ma, Jinyu and Li, Zhaoyang and Luo, Zhaochuan and Xie, Yanchun and Qin, Yuzhuo and Luo, Tong and Li, Yaqian and Liu, Shilong and others},
journal={arXiv preprint arXiv:2306.03514},
year={2023}
}
@article{huang2023tag2text,
title={Tag2Text: Guiding Vision-Language Model via Image Tagging},
author={Huang, Xinyu and Zhang, Youcai and Ma, Jinyu and Tian, Weiwei and Feng, Rui and Zhang, Yuejie and Li, Yaqian and Guo, Yandong and Zhang, Lei},
journal={arXiv preprint arXiv:2303.05657},
year={2023}
}