Task
Zero Shot Object Detection
G
IDEA-Research/grounding-dino-tiny
Zero Shot Object Detection·IDEA-Research· 1.2M· 105
transformers
apache-2.0
172.3M params
arxiv:2303.05499license:apache-2.0region:us
The Grounding DINO model was proposed in Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang. Grounding DINO extends a closed-set object detection model with a text encoder, enabling open-set object detection. The model achieves remarkab
# pull & run locally
pip install mlforge-sdk && mlforge pull IDEA-Research/grounding-dino-tiny
pip install mlforge-sdk && mlforge pull IDEA-Research/grounding-dino-tiny
Model details
Provider
IDEA-Research
Framework
transformers
Parameters
172.3M
Size
3.9 GB
License
apache-2.0
Downloads
1.2M
Likes
105
Paper
arXiv:2303.05499
Updated
2024-05-12
About IDEA-Research/grounding-dino-tiny
The Grounding DINO model was proposed in Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang. Grounding DINO extends a closed-set object detection model with a text encoder, enabling open-set object detection. The model achieves remarkable results, such as 52.5 AP on COCO zero-shot.