# :sauropod: Grounding DINO [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/zero-shot-object-detection-on-mscoco)](https://paperswithcode.com/sota/zero-shot-object-detection-on-mscoco?p=grounding-dino-marrying-dino-with-grounded) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/zero-shot-object-detection-on-odinw)](https://paperswithcode.com/sota/zero-shot-object-detection-on-odinw?p=grounding-dino-marrying-dino-with-grounded) \ [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/object-detection-on-coco-minival)](https://paperswithcode.com/sota/object-detection-on-coco-minival?p=grounding-dino-marrying-dino-with-grounded) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/object-detection-on-coco)](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)**. ## :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:  [[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 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](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. 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. ![model_explain1](.asset/model_explan1.PNG) ![model_explain2](.asset/model_explan2.PNG) ## :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 COCO
ODinW Object Detection Results ODinW
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. ![arch](.asset/arch.png) ## :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} } ```