metadata

license: mit
sdk: gradio
emoji: 😻
colorTo: green
pinned: true

:sauropod: Grounding DINO

IDEA-CVR, IDEA-Research

Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang^:email:.

[Paper] [Demo] [BibTex]

PyTorch implementation and pretrained models for Grounding DINO. For details, see the paper Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

:sun_with_face: Helpful Tutorial

:grapes: [Read our arXiv Paper]
:apple: [Watch our simple introduction video on YouTube]
:blossom: [Try the Colab Demo]
:sunflower: [Try our Official Huggingface Demo]
:maple_leaf: [Watch the Step by Step Tutorial about GroundingDINO by Roboflow AI]
:mushroom: [GroundingDINO: Automated Dataset Annotation and Evaluation by Roboflow AI]
:hibiscus: [Accelerate Image Annotation with SAM and GroundingDINO by Roboflow AI]
:white_flower: [Autodistill: Train YOLOv8 with ZERO Annotations based on Grounding-DINO and Grounded-SAM by Roboflow AI]

:sparkles: Highlight Projects

:bulb: Highlight

Open-Set Detection. Detect everything with language!
High Performancce. COCO zero-shot 52.5 AP (training without COCO data!). COCO fine-tune 63.0 AP.
Flexible. Collaboration with Stable Diffusion for Image Editting.

:fire: News

2023/07/18: We release Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. Code and checkpoint are available!
2023/06/17: We provide an example to evaluate Grounding DINO on COCO zero-shot performance.
2023/04/15: Refer to CV in the Wild Readings for those who are interested in open-set recognition!
2023/04/08: We release demos to combine Grounding DINO with GLIGEN for more controllable image editings.
2023/04/08: We release demos to combine Grounding DINO with Stable Diffusion for image editings.
2023/04/06: We build a new demo by marrying GroundingDINO with Segment-Anything named Grounded-Segment-Anything aims to support segmentation in GroundingDINO.
2023/03/28: A YouTube video about Grounding DINO and basic object detection prompt engineering. [SkalskiP]
2023/03/28: Add a demo on Hugging Face Space!
2023/03/27: Support CPU-only mode. Now the model can run on machines without GPUs.
2023/03/25: A demo for Grounding DINO is available at Colab. [SkalskiP]
2023/03/22: Code is available Now!

Description

Paper introduction. ODinW

Marrying Grounding DINO and GLIGEN gd_gligen

:star: Explanations/Tips for Grounding DINO Inputs and Outputs

Grounding DINO accepts an (image, text) pair as inputs.
It outputs 900 (by default) object boxes. Each box has similarity scores across all input words. (as shown in Figures below.)
We defaultly choose the boxes whose highest similarities are higher than a box_threshold.
We extract the words whose similarities are higher than the text_threshold as predicted labels.
If you want to obtain objects of specific phrases, like the dogs in the sentence two dogs with a stick., you can select the boxes with highest text similarities with dogs as final outputs.
Note that each word can be split to more than one tokens with different tokenlizers. The number of words in a sentence may not equal to the number of text tokens.
We suggest separating different category names with . for Grounding DINO.

:medal_military: Results

COCO Object Detection Results

ODinW Object Detection Results

Marrying Grounding DINO with Stable Diffusion for Image Editing

See our example notebook for more details. GD_SD

Marrying Grounding DINO with GLIGEN for more Detailed Image Editing.

See our example notebook for more details. GD_GLIGEN

:sauropod: Model: Grounding DINO

Includes: a text backbone, an image backbone, a feature enhancer, a language-guided query selection, and a cross-modality decoder.

:hearts: Acknowledgement

Our model is related to DINO and GLIP. Thanks for their great work!

We also thank great previous work including DETR, Deformable DETR, SMCA, Conditional DETR, Anchor DETR, Dynamic DETR, DAB-DETR, DN-DETR, etc. More related work are available at Awesome Detection Transformer. A new toolbox detrex is available as well.

Thanks Stable Diffusion and GLIGEN for their awesome models.

:black_nib: Citation

If you find our work helpful for your research, please consider citing the following BibTeX entry.

@article{liu2023grounding,
  title={Grounding dino: Marrying dino with grounded pre-training for open-set object detection},
  author={Liu, Shilong and Zeng, Zhaoyang and Ren, Tianhe and Li, Feng and Zhang, Hao and Yang, Jie and Li, Chunyuan and Yang, Jianwei and Su, Hang and Zhu, Jun and others},
  journal={arXiv preprint arXiv:2303.05499},
  year={2023}
}