README.md

---
license: mit
datasets:
- lmms-lab/GQA
- dmarsili/Omni3D-Bench
- cambridgeltl/vsr_random
- snowclipsed/TallyQA
language:
- en
base_model:
- ShilongLiu/GroundingDINO
pipeline_tag: object-detection
tags:
- object-detection
- computer-vision
---
# Model Card for VALOR-GroundingDINO

This is the verified-tuned GroundingDINO model from the paper: [No Labels, No Problem: Training Visual Reasoners with Multimodal Verifiers](https://glab-caltech.github.io/valor/)

For further information please refer to the [project webpage](https://glab-caltech.github.io/valor/), [paper](https://arxiv.org/abs/2512.08889), and [repository](https://github.com/damianomarsili/VALOR).

## Citation

If you use VALOR in your research, please consider citing our work:

**BibTeX:**
```
@misc{marsili2025labelsproblemtrainingvisual,
      title={No Labels, No Problem: Training Visual Reasoners with Multimodal Verifiers}, 
      author={Damiano Marsili and Georgia Gkioxari},
      year={2025},
      eprint={2512.08889},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2512.08889}, 
}
```