Grounding DINO Methods | 
Grounding DINO Demos |
Extensions | Grounding DINO with Segment Anything; Grounding DINO with Stable Diffusion; Grounding DINO with GLIGEN
Official PyTorch implementation of Grounding DINO, a stronger open-set object detector. Code is available now!
- 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.
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!
- 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_thresholdas predicted labels. - If you want to obtain objects of specific phrases, like the
dogsin the sentencetwo dogs with a stick., you can select the boxes with highest text similarities withdogsas 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.
- Release inference code and demo.
- Release checkpoints.
- Grounding DINO with Stable Diffusion and GLIGEN demos.
- Release training codes.
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.
git clone https://github.com/IDEA-Research/GroundingDINO.gitChange the current directory to the GroundingDINO folder.
cd GroundingDINO/Install the required dependencies in the current directory.
pip3 install -q -e .Create a new directory called "weights" to store the model weights.
mkdir weightsChange the current directory to the "weights" folder.
cd weightsDownload the model weights file.
wget -q https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pthCheck your GPU ID (only if you're using a GPU)
nvidia-smiReplace {GPU ID}, image_you_want_to_detect.jpg, and "dir you want to save the output" with appropriate values in the following command
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 modeSee the demo/inference_on_a_image.py for more details.
Running with 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 to combine Grounding DINO with GLIGEN for more controllable image editings.
- We release demos to combine Grounding DINO with Stable Diffusion for image editings.
| 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 |
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.
Includes: a text backbone, an image backbone, a feature enhancer, a language-guided query selection, and a cross-modality decoder.
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.
If you find our work helpful for your research, please consider citing the following BibTeX entry.
@inproceedings{ShilongLiu2023GroundingDM,
title={Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection},
author={Shilong Liu and Zhaoyang Zeng and Tianhe Ren and Feng Li and Hao Zhang and Jie Yang and Chunyuan Li and Jianwei Yang and Hang Su and Jun Zhu and Lei Zhang},
year={2023}
}