# :sauropod: Grounding DINO
---
Grounding DINO Methods | [](https://github.com/IDEA-Research/GroundingDINO)
[](https://arxiv.org/abs/2303.05499)
[](https://youtu.be/wxWDt5UiwY8)
Grounding DINO Demos |
[](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb)
[](https://youtu.be/cMa77r3YrDk)
[](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo)
[](https://youtu.be/oEQYStnF2l8)
[](https://youtu.be/C4NqaRBz_Kw)
Extensions | [Grounding DINO with Segment Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything); [Grounding DINO with Stable Diffusion](demo/image_editing_with_groundingdino_stablediffusion.ipynb); [Grounding DINO with GLIGEN](demo/image_editing_with_groundingdino_gligen.ipynb)
[](https://paperswithcode.com/sota/zero-shot-object-detection-on-mscoco?p=grounding-dino-marrying-dino-with-grounded) \
[](https://paperswithcode.com/sota/zero-shot-object-detection-on-odinw?p=grounding-dino-marrying-dino-with-grounded) \
[](https://paperswithcode.com/sota/object-detection-on-coco-minival?p=grounding-dino-marrying-dino-with-grounded) \
[](https://paperswithcode.com/sota/object-detection-on-coco?p=grounding-dino-marrying-dino-with-grounded)
Official PyTorch implementation of [Grounding DINO](https://arxiv.org/abs/2303.05499), a stronger open-set object detector. Code is available now!
## :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
- **`2023/04/15`**: Refer to [CV in the Wild Readings](https://github.com/Computer-Vision-in-the-Wild/CVinW_Readings) for those who are interested in open-set recognition!
- **`2023/04/08`**: We release [demos](demo/image_editing_with_groundingdino_gligen.ipynb) to combine [Grounding DINO](https://arxiv.org/abs/2303.05499) with [GLIGEN](https://github.com/gligen/GLIGEN) for more controllable image editings.
- **`2023/04/08`**: We release [demos](demo/image_editing_with_groundingdino_stablediffusion.ipynb) to combine [Grounding DINO](https://arxiv.org/abs/2303.05499) with [Stable Diffusion](https://github.com/Stability-AI/StableDiffusion) for image editings.
- **`2023/04/06`**: We build a new demo by marrying GroundingDINO with [Segment-Anything](https://github.com/facebookresearch/segment-anything) named **[Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything)** aims to support segmentation in GroundingDINO.
- **`2023/03/28`**: A YouTube [video](https://youtu.be/cMa77r3YrDk) about Grounding DINO and basic object detection prompt engineering. [[SkalskiP](https://github.com/SkalskiP)]
- **`2023/03/28`**: Add a [demo](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_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](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb) for Grounding DINO is available at Colab. [[SkalskiP](https://github.com/SkalskiP)]
- **`2023/03/22`**: Code is available Now!
<details open>
<summary><font size="4">
Description
</font></summary>
<a href="https://arxiv.org/abs/2303.05499">Paper</a> introduction.
<img src=".asset/hero_figure.png" alt="ODinW" width="100%">
Marrying <a href="https://github.com/IDEA-Research/GroundingDINO">Grounding DINO</a> and <a href="https://github.com/gligen/GLIGEN">GLIGEN</a>
<img src="https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/GD_GLIGEN.png" alt="gd_gligen" width="100%">
</details>
## :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 differetn 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.
## :label: TODO
- [x] Release inference code and demo.
- [x] Release checkpoints.
- [x] Grounding DINO with Stable Diffusion and GLIGEN demos.
- [ ] Release training codes.
## :hammer_and_wrench: Install
**Note:**
If you have a CUDA environment, please make sure the environment variable `CUDA_HOME` is set. It will be compiled under CPU-only mode if no CUDA available.
**Installation:**
Clone the GroundingDINO repository from GitHub.
```bash
git clone https://github.com/IDEA-Research/GroundingDINO.git
```
Change the current directory to the GroundingDINO folder.
```bash
cd GroundingDINO/
```
Install the required dependencies in the current directory.
```bash
pip3 install -q -e .
```
Create a new directory called "weights" to store the model weights.
```bash
mkdir weights
```
Change the current directory to the "weights" folder.
```bash
cd weights
```
Download the model weights file.
```bash
wget -q https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth
```
## :arrow_forward: Demo
Check your GPU ID (only if you're using a GPU)
```bash
nvidia-smi
```
Replace `{GPU ID}`, `image_you_want_to_detect.jpg`, and `"dir you want to save the output"` with appropriate values in the following command
```bash
CUDA_VISIBLE_DEVICES={GPU ID} python demo/inference_on_a_image.py \
-c /GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \
-p /GroundingDINO/weights/groundingdino_swint_ogc.pth \
-i image_you_want_to_detect.jpg \
-o "dir you want to save the output" \
-t "chair"
[--cpu-only] # open it for cpu mode
```
See the `demo/inference_on_a_image.py` for more details.
**Running with Python:**
```python
from groundingdino.util.inference import load_model, load_image, predict, annotate
import cv2
model = load_model("groundingdino/config/GroundingDINO_SwinT_OGC.py", "weights/groundingdino_swint_ogc.pth")
IMAGE_PATH = "weights/dog-3.jpeg"
TEXT_PROMPT = "chair . person . dog ."
BOX_TRESHOLD = 0.35
TEXT_TRESHOLD = 0.25
image_source, image = load_image(IMAGE_PATH)
boxes, logits, phrases = predict(
model=model,
image=image,
caption=TEXT_PROMPT,
box_threshold=BOX_TRESHOLD,
text_threshold=TEXT_TRESHOLD
)
annotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases)
cv2.imwrite("annotated_image.jpg", annotated_frame)
```
**Web UI**
We also provide a demo code to integrate Grounding DINO with Gradio Web UI. See the file `demo/gradio_app.py` for more details.
**Notebooks**
- We release [demos](demo/image_editing_with_groundingdino_gligen.ipynb) to combine [Grounding DINO](https://arxiv.org/abs/2303.05499) with [GLIGEN](https://github.com/gligen/GLIGEN) for more controllable image editings.
- We release [demos](demo/image_editing_with_groundingdino_stablediffusion.ipynb) to combine [Grounding DINO](https://arxiv.org/abs/2303.05499) with [Stable Diffusion](https://github.com/Stability-AI/StableDiffusion) for image editings.
## :luggage: Checkpoints
<!-- insert a table -->
<table>
<thead>
<tr style="text-align: right;">
<th></th>
<th>name</th>
<th>backbone</th>
<th>Data</th>
<th>box AP on COCO</th>
<th>Checkpoint</th>
<th>Config</th>
</tr>
</thead>
<tbody>
<tr>
<th>1</th>
<td>GroundingDINO-T</td>
<td>Swin-T</td>
<td>O365,GoldG,Cap4M</td>
<td>48.4 (zero-shot) / 57.2 (fine-tune)</td>
<td><a href="https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth">GitHub link</a> | <a href="https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth">HF link</a></td>
<td><a href="https://github.com/IDEA-Research/GroundingDINO/blob/main/groundingdino/config/GroundingDINO_SwinT_OGC.py">link</a></td>
</tr>
<tr>
<th>2</th>
<td>GroundingDINO-B</td>
<td>Swin-B</td>
<td>COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO</td>
<td>56.7 </td>
<td><a href="https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha2/groundingdino_swinb_cogcoor.pth">GitHub link</a> | <a href="https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swinb_cogcoor.pth">HF link</a>
<td><a href="https://github.com/IDEA-Research/GroundingDINO/blob/main/groundingdino/config/GroundingDINO_SwinB.cfg.py">link</a></td>
</tr>
</tbody>
</table>
## :medal_military: 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>
See our example <a href="https://github.com/IDEA-Research/GroundingDINO/blob/main/demo/image_editing_with_groundingdino_stablediffusion.ipynb">notebook</a> for more details.
<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>
See our example <a href="https://github.com/IDEA-Research/GroundingDINO/blob/main/demo/image_editing_with_groundingdino_gligen.ipynb">notebook</a> for more details.
<img src=".asset/GD_GLIGEN.png" alt="GD_GLIGEN" width="100%">
</details>
## :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](https://github.com/IDEA-Research/DINO) and [GLIP](https://github.com/microsoft/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](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.
## :black_nib: Citation
If you find our work helpful for your research, please consider citing the following BibTeX entry.
```bibtex
@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}
}
```