Published August 11, 2025 | Version v1
Model Open

Fine-Tuned MM-GROUDING-DINO Model for Handwriting Detection

Authors/Creators

  • 1. ROR icon Microsoft (United States)
  • 2. ROR icon University of Washington

Contributors

Project leader:

  • 1. EDMO icon University of Washington

Description

The Microsoft AI For Good Lab and University of Washington Center for Environmental Forensic Science collaborated on an object detection model fine-tuned on images of seized illicit elephant ivory. The model detects handwritten markings on the tusks. For more information, see: https://github.com/microsoft/signature-marking-detector

Files

Files (937.5 MB)

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md5:3e1b8ed411b756476deb8b9154af8453
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Additional details

References

  • S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, Q. Jiang, C. Li, J. Yang, H. Su, J. Zhu, L. Zhang, Grounding DINO: Marrying DINO with Grounded Pre-Training for Open Set Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 19606–19616 (2023).
  • K. Chen, J. Wang, J. Pang, Y. Cao, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Xu, Z. Zhang, D. Cheng, C. Zhu, T. Cheng, Q. Zhao, B. Li, X. Lu, R. Zhu, Y. Wu, J. Dai, J. Wang, J. Shi, W. Ouyang, C. C. Loy, D. Lin, MMDetection: Open MMLab Detection Toolbox and Benchmark. arXiv preprint arXiv:1906.07155 (2019).
  • X. Zhao, Y. Chen, S. Xu, X. Li, X. Wang, Y. Li, H. Huang, MM-Grounding-DINO: An Open and Comprehensive Pipeline for Unified Object Grounding and Detection. arXiv preprint arXiv:2401.02361 (2024)