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https://github.com/idea-research/groundingdino
[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
https://github.com/idea-research/groundingdino
object-detection open-world open-world-detection vision-language vision-language-transformer
Last synced: 1 day ago
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[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
- Host: GitHub
- URL: https://github.com/idea-research/groundingdino
- Owner: IDEA-Research
- License: apache-2.0
- Created: 2023-03-09T06:14:41.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-12T08:52:02.000Z (4 months ago)
- Last Synced: 2024-12-20T09:03:21.435Z (2 days ago)
- Topics: object-detection, open-world, open-world-detection, vision-language, vision-language-transformer
- Language: Python
- Homepage: https://arxiv.org/abs/2303.05499
- Size: 12.5 MB
- Stars: 7,030
- Watchers: 43
- Forks: 710
- Open Issues: 269
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# :sauropod: Grounding DINO
[](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)
**[IDEA-CVR, IDEA-Research](https://github.com/IDEA-Research)**
[Shilong Liu](http://www.lsl.zone/), [Zhaoyang Zeng](https://scholar.google.com/citations?user=U_cvvUwAAAAJ&hl=zh-CN&oi=ao), [Tianhe Ren](https://rentainhe.github.io/), [Feng Li](https://scholar.google.com/citations?user=ybRe9GcAAAAJ&hl=zh-CN), [Hao Zhang](https://scholar.google.com/citations?user=B8hPxMQAAAAJ&hl=zh-CN), [Jie Yang](https://github.com/yangjie-cv), [Chunyuan Li](https://scholar.google.com/citations?user=Zd7WmXUAAAAJ&hl=zh-CN&oi=ao), [Jianwei Yang](https://jwyang.github.io/), [Hang Su](https://scholar.google.com/citations?hl=en&user=dxN1_X0AAAAJ&view_op=list_works&sortby=pubdate), [Jun Zhu](https://scholar.google.com/citations?hl=en&user=axsP38wAAAAJ), [Lei Zhang](https://www.leizhang.org/):email:.
[[`Paper`](https://arxiv.org/abs/2303.05499)] [[`Demo`](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo)] [[`BibTex`](#black_nib-citation)]
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](https://arxiv.org/abs/2303.05499)**.
- 🔥 **[Grounded SAM 2](https://github.com/IDEA-Research/Grounded-SAM-2)** is released now, which combines Grounding DINO with [SAM 2](https://github.com/facebookresearch/segment-anything-2) for any object tracking in open-world scenarios.
- 🔥 **[Grounding DINO 1.5](https://github.com/IDEA-Research/Grounding-DINO-1.5-API)** is released now, which is IDEA Research's **Most Capable** Open-World Object Detection Model!
- 🔥 **[Grounding DINO](https://arxiv.org/abs/2303.05499)** and **[Grounded SAM](https://arxiv.org/abs/2401.14159)** are now supported in Huggingface. For more convenient use, you can refer to [this documentation](https://huggingface.co/docs/transformers/model_doc/grounding-dino)
## :sun_with_face: Helpful Tutorial
- :grapes: [[Read our arXiv Paper](https://arxiv.org/abs/2303.05499)]
- :apple: [[Watch our simple introduction video on YouTube](https://youtu.be/wxWDt5UiwY8)]
- :blossom: Â [[Try the Colab Demo](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb)]
- :sunflower: [[Try our Official Huggingface Demo](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo)]
- :maple_leaf: [[Watch the Step by Step Tutorial about GroundingDINO by Roboflow AI](https://youtu.be/cMa77r3YrDk)]
- :mushroom: [[GroundingDINO: Automated Dataset Annotation and Evaluation by Roboflow AI](https://youtu.be/C4NqaRBz_Kw)]
- :hibiscus: [[Accelerate Image Annotation with SAM and GroundingDINO by Roboflow AI](https://youtu.be/oEQYStnF2l8)]
- :white_flower: [[Autodistill: Train YOLOv8 with ZERO Annotations based on Grounding-DINO and Grounded-SAM by Roboflow AI](https://github.com/autodistill/autodistill)]
## :sparkles: Highlight Projects
- [Semantic-SAM: a universal image segmentation model to enable segment and recognize anything at any desired granularity.](https://github.com/UX-Decoder/Semantic-SAM),
- [DetGPT: Detect What You Need via Reasoning](https://github.com/OptimalScale/DetGPT)
- [Grounded-SAM: Marrying 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 for Controllable Image Editing](demo/image_editing_with_groundingdino_gligen.ipynb)
- [OpenSeeD: A Simple and Strong Openset Segmentation Model](https://github.com/IDEA-Research/OpenSeeD)
- [SEEM: Segment Everything Everywhere All at Once](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once)
- [X-GPT: Conversational Visual Agent supported by X-Decoder](https://github.com/microsoft/X-Decoder/tree/xgpt)
- [GLIGEN: Open-Set Grounded Text-to-Image Generation](https://github.com/gligen/GLIGEN)
- [LLaVA: Large Language and Vision Assistant](https://github.com/haotian-liu/LLaVA)
## :bulb: Highlight
- **Open-Set Detection.** Detect **everything** with language!
- **High Performance.** 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](https://github.com/UX-Decoder/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](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!
Description
Paper introduction.
Marrying Grounding DINO and 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.
## :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:**
0. 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.
Please make sure following the installation steps strictly, otherwise the program may produce:
```bash
NameError: name '_C' is not defined
```
If this happened, please reinstalled the groundingDINO by reclone the git and do all the installation steps again.
#### how to check cuda:
```bash
echo $CUDA_HOME
```
If it print nothing, then it means you haven't set up the path/
Run this so the environment variable will be set under current shell.
```bash
export CUDA_HOME=/path/to/cuda-11.3
```
Notice the version of cuda should be aligned with your CUDA runtime, for there might exists multiple cuda at the same time.
If you want to set the CUDA_HOME permanently, store it using:
```bash
echo 'export CUDA_HOME=/path/to/cuda' >> ~/.bashrc
```
after that, source the bashrc file and check CUDA_HOME:
```bash
source ~/.bashrc
echo $CUDA_HOME
```
In this example, /path/to/cuda-11.3 should be replaced with the path where your CUDA toolkit is installed. You can find this by typing **which nvcc** in your terminal:
For instance,
if the output is /usr/local/cuda/bin/nvcc, then:
```bash
export CUDA_HOME=/usr/local/cuda
```
**Installation:**
1.Clone the GroundingDINO repository from GitHub.
```bash
git clone https://github.com/IDEA-Research/GroundingDINO.git
```
2. Change the current directory to the GroundingDINO folder.
```bash
cd GroundingDINO/
```
3. Install the required dependencies in the current directory.
```bash
pip install -e .
```
4. Download pre-trained model weights.
```bash
mkdir weights
cd weights
wget -q https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth
cd ..
```
## :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/config/GroundingDINO_SwinT_OGC.py \
-p 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
```
If you would like to specify the phrases to detect, here is a demo:
```bash
CUDA_VISIBLE_DEVICES={GPU ID} python demo/inference_on_a_image.py \
-c groundingdino/config/GroundingDINO_SwinT_OGC.py \
-p ./groundingdino_swint_ogc.pth \
-i .asset/cat_dog.jpeg \
-o logs/1111 \
-t "There is a cat and a dog in the image ." \
--token_spans "[[[9, 10], [11, 14]], [[19, 20], [21, 24]]]"
[--cpu-only] # open it for cpu mode
```
The token_spans specify the start and end positions of a phrases. For example, the first phrase is `[[9, 10], [11, 14]]`. `"There is a cat and a dog in the image ."[9:10] = 'a'`, `"There is a cat and a dog in the image ."[11:14] = 'cat'`. Hence it refers to the phrase `a cat` . Similarly, the `[[19, 20], [21, 24]]` refers to the phrase `a dog`.
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.
## COCO Zero-shot Evaluations
We provide an example to evaluate Grounding DINO zero-shot performance on COCO. The results should be **48.5**.
```bash
CUDA_VISIBLE_DEVICES=0 \
python demo/test_ap_on_coco.py \
-c groundingdino/config/GroundingDINO_SwinT_OGC.py \
-p weights/groundingdino_swint_ogc.pth \
--anno_path /path/to/annoataions/ie/instances_val2017.json \
--image_dir /path/to/imagedir/ie/val2017
```
## :luggage: Checkpoints
name
backbone
Data
box AP on COCO
Checkpoint
Config
1
GroundingDINO-T
Swin-T
O365,GoldG,Cap4M
48.4 (zero-shot) / 57.2 (fine-tune)
GitHub link | HF link
link
2
GroundingDINO-B
Swin-B
COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO
56.7
GitHub link | HF link
link
## :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.
Marrying Grounding DINO with GLIGEN for more Detailed Image Editing.
See our example notebook for more 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}
}
```