Skip to content

Latest commit

 

History

History
214 lines (149 loc) · 7.89 KB

README.md

File metadata and controls

214 lines (149 loc) · 7.89 KB
Raw

🏷️ Recognize Anything: A Strong Image Tagging Model & Tag2Text: Guiding Vision-Language Model via Image Tagging

Official PyTorch Implementation of the Recognize Anything Model (RAM) and the Tag2Text Model.

  • RAM is a strong image tagging model, which can recognize any common category with high accuracy.
  • Tag2Text is an efficient and controllable vision-language model with tagging guidance.

When combined with localization models (Grounded-SAM), Tag2Text and RAM form a strong and general pipeline for visual semantic analysis.

🌞 Helpful Tutorial

💡 Highlight

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.

Tag2Text for Vision-Language Tasks.
  • Tagging. Without manual annotations, Tag2Text achieves superior image tag recognition ability of 3,429 commonly human-used categories.
  • Efficient. Tagging guidance effectively enhances the performance of vision-language models on both generation-based and alignment-based tasks.
  • Controllable. Tag2Text permits users to input desired tags, providing the flexibility in composing corresponding texts based on the input tags.

Advancements of RAM on Tag2Text.
  • Accuracy. RAM utilizes a data engine to generate additional annotations and clean incorrect ones, resulting higher accuracy compared to Tag2Text.
  • Scope. Tag2Text recognizes 3,400+ fixed tags. RAM upgrades the number to 6,400+, covering more valuable categories. With open-set capability, RAM is feasible to recognize any common category.

✨ Highlight Projects with other Models

  • Tag2Text/RAM with Grounded-SAM is trong and general pipeline for visual semantic analysis, which can automatically recognize, detect, and segment for an image!
  • Ask-Anything is a multifunctional video question answering tool. Tag2Text provides powerful tagging and captioning capabilities as a fundamental component.
  • Prompt-can-anything is a gradio web library that integrates SOTA multimodal large models, including Tag2text as the core model for graphic understanding

🔥 News

✍️ TODO

  • Release Tag2Text demo.
  • Release checkpoints.
  • Release inference code.
  • Release RAM demo and checkpoints.
  • Release training codes (until July 8st at the latest).
  • Release training datasets (until July 15st at the latest).

🧰 Checkpoints

name backbone Data Illustration Checkpoint
1 RAM-Swin Swin-Large COCO, VG, SBU, CC-3M, CC-12M Demo version can recognize any common category with high accuracy. Download link
2 Tag2Text-Swin Swin-Base COCO, VG, SBU, CC-3M, CC-12M Demo version with comprehensive captions. Download link

🏃 Model Inference

RAM Inference

  1. Install the dependencies, run:

pip install -r requirements.txt

  1. Download RAM pretrained checkpoints.

  2. Get the English and Chinese outputs of the images:

python inference_ram.py  --image images/1641173_2291260800.jpg \
--pretrained pretrained/ram_swin_large_14m.pth

RAM Zero-Shot Inference is Comming!

Tag2Text Inference

  1. Install the dependencies, run:

pip install -r requirements.txt

  1. Download Tag2Text pretrained checkpoints.

  2. Get the tagging and captioning results:

python inference_tag2text.py  --image images/1641173_2291260800.jpg \
--pretrained pretrained/tag2text_swin_14m.pth
Or get the tagging and sepcifed captioning results (optional): 
python inference_tag2text.py  --image images/1641173_2291260800.jpg \
--pretrained pretrained/tag2text_swin_14m.pth \
--specified-tags "cloud,sky"

✒️ Citation

If you find our work to be useful for your research, please consider citing.

@misc{zhang2023recognize,
  title={Recognize Anything: A Strong Image Tagging Model}, 
  author={Youcai Zhang and Xinyu Huang and Jinyu Ma and Zhaoyang Li and Zhaochuan Luo and Yanchun Xie and Yuzhuo Qin and Tong Luo and Yaqian Li and Shilong Liu and Yandong Guo and Lei Zhang},
  year={2023},
  eprint={2306.03514},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@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}
}

♥️ Acknowledgements

This work is done with the help of the amazing code base of BLIP, thanks very much!

We also want to thank @Cheng Rui @Shilong Liu @Ren Tianhe for their help in marrying Tag2Text with Grounded-SAM.

Pushing update