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README.md
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Official pytorch implementation of [Grounding DINO](https://arxiv.org/abs/2303.05499). Code will be available soon!
## Highlight
- SOTA Closed-Set Detection Model [DINO](https://github.com/IDEA-Research/DINO) => SOTA Open-Set Detection Model **Grounding DINO**
- Pure Transformer-based.
- COCO zero-shot **52.5 AP** (training without COCO data!). COCO fine-tune **63.0 AP**.
- **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.
![hero_figure](.asset/hero_figure.png)
## Results
<details open>
<summary><font size="4">
COCO Object Detection Results
</font></summary>
<img src=".asset/COCO.png" alt="COCO" width="100%">
</details>
<details open>
<summary><font size="4">
ODinW Object Detection Results
</font></summary>
<img src=".asset/ODinW.png" alt="ODinW" width="100%">
</details>
<details open>
<summary><font size="4">
Marrying Grounding DINO with <a href="https://github.com/Stability-AI/StableDiffusion">Stable Diffusion</a> for Image Editing
</font></summary>
<img src=".asset/GD_SD.png" alt="GD_SD" width="100%">
</details>
<details open>
<summary><font size="4">
Marrying Grounding DINO with <a href="https://github.com/gligen/GLIGEN">GLIGEN</a> for more Detailed Image Editing
</font></summary>
<img src=".asset/GD_GLIGEN.png" alt="GD_GLIGEN" width="100%">
</details>
## Model
Includes: a text backbone, an image backbone, a feature enhancer, a language-guided query selection, and a cross-modality decoder.
@ -27,6 +57,8 @@ Our model is related to [DINO](https://github.com/IDEA-Research/DINO) and [GLIP]
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](https://github.com/IDEACVR/awesome-detection-transformer). A new toolbox [detrex](https://github.com/IDEA-Research/detrex) is available as well.
Thanks [Stable Diffusion](https://github.com/Stability-AI/StableDiffusion) and [GLIGEN](https://github.com/gligen/GLIGEN) for their awesome models.
# Bibtex
If you find our work helpful for your research, please consider citing the following BibTeX entry.