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--- |
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title: DINO |
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app_file: app.py |
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sdk: gradio |
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sdk_version: 4.40.0 |
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--- |
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<div align="center"> |
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<img src="./.asset/grounding_dino_logo.png" width="30%"> |
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</div> |
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# :sauropod: Grounding DINO |
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[](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) \ |
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[](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) |
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**[IDEA-CVR, IDEA-Research](https://github.com/IDEA-Research)** |
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[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/)<sup>:email:</sup>. |
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[[`Paper`](https://arxiv.org/abs/2303.05499)] [[`Demo`](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo)] [[`BibTex`](#black_nib-citation)] |
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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)**. |
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- 🔥 **[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! |
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- 🔥 **[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) |
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## :sun_with_face: Helpful Tutorial |
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- :grapes: [[Read our arXiv Paper](https://arxiv.org/abs/2303.05499)] |
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- :apple: [[Watch our simple introduction video on YouTube](https://youtu.be/wxWDt5UiwY8)] |
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- :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)] |
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- :sunflower: [[Try our Official Huggingface Demo](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo)] |
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- :maple_leaf: [[Watch the Step by Step Tutorial about GroundingDINO by Roboflow AI](https://youtu.be/cMa77r3YrDk)] |
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- :mushroom: [[GroundingDINO: Automated Dataset Annotation and Evaluation by Roboflow AI](https://youtu.be/C4NqaRBz_Kw)] |
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- :hibiscus: [[Accelerate Image Annotation with SAM and GroundingDINO by Roboflow AI](https://youtu.be/oEQYStnF2l8)] |
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- :white_flower: [[Autodistill: Train YOLOv8 with ZERO Annotations based on Grounding-DINO and Grounded-SAM by Roboflow AI](https://github.com/autodistill/autodistill)] |
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<!-- Grounding DINO Methods | |
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[](https://arxiv.org/abs/2303.05499) |
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[](https://youtu.be/wxWDt5UiwY8) --> |
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<!-- Grounding DINO Demos | |
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[](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb) --> |
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<!-- [](https://youtu.be/cMa77r3YrDk) |
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[](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo) |
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[](https://youtu.be/oEQYStnF2l8) |
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[](https://youtu.be/C4NqaRBz_Kw) --> |
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## :sparkles: Highlight Projects |
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- [Semantic-SAM: a universal image segmentation model to enable segment and recognize anything at any desired granularity.](https://github.com/UX-Decoder/Semantic-SAM), |
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- [DetGPT: Detect What You Need via Reasoning](https://github.com/OptimalScale/DetGPT) |
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- [Grounded-SAM: Marrying Grounding DINO with Segment Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything) |
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- [Grounding DINO with Stable Diffusion](demo/image_editing_with_groundingdino_stablediffusion.ipynb) |
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- [Grounding DINO with GLIGEN for Controllable Image Editing](demo/image_editing_with_groundingdino_gligen.ipynb) |
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- [OpenSeeD: A Simple and Strong Openset Segmentation Model](https://github.com/IDEA-Research/OpenSeeD) |
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- [SEEM: Segment Everything Everywhere All at Once](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once) |
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- [X-GPT: Conversational Visual Agent supported by X-Decoder](https://github.com/microsoft/X-Decoder/tree/xgpt) |
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- [GLIGEN: Open-Set Grounded Text-to-Image Generation](https://github.com/gligen/GLIGEN) |
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- [LLaVA: Large Language and Vision Assistant](https://github.com/haotian-liu/LLaVA) |
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<!-- 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) --> |
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<!-- Official PyTorch implementation of [Grounding DINO](https://arxiv.org/abs/2303.05499), a stronger open-set object detector. Code is available now! --> |
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## :bulb: Highlight |
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- **Open-Set Detection.** Detect **everything** with language! |
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- **High Performance.** COCO zero-shot **52.5 AP** (training without COCO data!). COCO fine-tune **63.0 AP**. |
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- **Flexible.** Collaboration with Stable Diffusion for Image Editting. |
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## :fire: News |
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- **`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! |
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- **`2023/06/17`**: We provide an example to evaluate Grounding DINO on COCO zero-shot performance. |
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- **`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! |
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- **`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. |
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- **`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. |
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- **`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. |
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- **`2023/03/28`**: A YouTube [video](https://youtu.be/cMa77r3YrDk) about Grounding DINO and basic object detection prompt engineering. [[SkalskiP](https://github.com/SkalskiP)] |
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- **`2023/03/28`**: Add a [demo](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo) on Hugging Face Space! |
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- **`2023/03/27`**: Support CPU-only mode. Now the model can run on machines without GPUs. |
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- **`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)] |
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- **`2023/03/22`**: Code is available Now! |
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<details open> |
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<summary><font size="4"> |
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Description |
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</font></summary> |
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<a href="https://arxiv.org/abs/2303.05499">Paper</a> introduction. |
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<img src=".asset/hero_figure.png" alt="ODinW" width="100%"> |
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Marrying <a href="https://github.com/IDEA-Research/GroundingDINO">Grounding DINO</a> and <a href="https://github.com/gligen/GLIGEN">GLIGEN</a> |
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<img src="https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/GD_GLIGEN.png" alt="gd_gligen" width="100%"> |
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</details> |
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## :star: Explanations/Tips for Grounding DINO Inputs and Outputs |
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- Grounding DINO accepts an `(image, text)` pair as inputs. |
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- It outputs `900` (by default) object boxes. Each box has similarity scores across all input words. (as shown in Figures below.) |
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- We defaultly choose the boxes whose highest similarities are higher than a `box_threshold`. |
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- We extract the words whose similarities are higher than the `text_threshold` as predicted labels. |
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- 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. |
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- 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. |
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- We suggest separating different category names with `.` for Grounding DINO. |
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## :label: TODO |
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- [x] Release inference code and demo. |
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- [x] Release checkpoints. |
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- [x] Grounding DINO with Stable Diffusion and GLIGEN demos. |
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- [ ] Release training codes. |
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## :hammer_and_wrench: Install |
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**Note:** |
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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. |
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Please make sure following the installation steps strictly, otherwise the program may produce: |
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```bash |
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NameError: name '_C' is not defined |
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``` |
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If this happened, please reinstalled the groundingDINO by reclone the git and do all the installation steps again. |
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#### how to check cuda: |
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```bash |
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echo $CUDA_HOME |
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``` |
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If it print nothing, then it means you haven't set up the path/ |
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Run this so the environment variable will be set under current shell. |
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```bash |
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export CUDA_HOME=/path/to/cuda-11.3 |
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``` |
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Notice the version of cuda should be aligned with your CUDA runtime, for there might exists multiple cuda at the same time. |
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If you want to set the CUDA_HOME permanently, store it using: |
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```bash |
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echo 'export CUDA_HOME=/path/to/cuda' >> ~/.bashrc |
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``` |
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after that, source the bashrc file and check CUDA_HOME: |
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```bash |
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source ~/.bashrc |
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echo $CUDA_HOME |
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``` |
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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: |
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For instance, |
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if the output is /usr/local/cuda/bin/nvcc, then: |
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```bash |
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export CUDA_HOME=/usr/local/cuda |
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``` |
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**Installation:** |
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1.Clone the GroundingDINO repository from GitHub. |
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```bash |
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git clone https://github.com/IDEA-Research/GroundingDINO.git |
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``` |
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2. Change the current directory to the GroundingDINO folder. |
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```bash |
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cd GroundingDINO/ |
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``` |
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3. Install the required dependencies in the current directory. |
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```bash |
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pip install -e . |
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``` |
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4. Download pre-trained model weights. |
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```bash |
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mkdir weights |
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cd weights |
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wget -q https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth |
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cd .. |
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``` |
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## :arrow_forward: Demo |
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Check your GPU ID (only if you're using a GPU) |
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```bash |
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nvidia-smi |
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``` |
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Replace `{GPU ID}`, `image_you_want_to_detect.jpg`, and `"dir you want to save the output"` with appropriate values in the following command |
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```bash |
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CUDA_VISIBLE_DEVICES={GPU ID} python demo/inference_on_a_image.py \ |
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-c groundingdino/config/GroundingDINO_SwinT_OGC.py \ |
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-p weights/groundingdino_swint_ogc.pth \ |
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-i image_you_want_to_detect.jpg \ |
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-o "dir you want to save the output" \ |
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-t "chair" |
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[--cpu-only] # open it for cpu mode |
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``` |
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If you would like to specify the phrases to detect, here is a demo: |
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```bash |
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CUDA_VISIBLE_DEVICES={GPU ID} python demo/inference_on_a_image.py \ |
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-c groundingdino/config/GroundingDINO_SwinT_OGC.py \ |
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-p ./groundingdino_swint_ogc.pth \ |
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-i .asset/cat_dog.jpeg \ |
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-o logs/1111 \ |
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-t "There is a cat and a dog in the image ." \ |
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--token_spans "[[[9, 10], [11, 14]], [[19, 20], [21, 24]]]" |
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[--cpu-only] # open it for cpu mode |
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``` |
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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`. |
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See the `demo/inference_on_a_image.py` for more details. |
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**Running with Python:** |
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```python |
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from groundingdino.util.inference import load_model, load_image, predict, annotate |
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import cv2 |
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model = load_model("groundingdino/config/GroundingDINO_SwinT_OGC.py", "weights/groundingdino_swint_ogc.pth") |
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IMAGE_PATH = "weights/dog-3.jpeg" |
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TEXT_PROMPT = "chair . person . dog ." |
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BOX_TRESHOLD = 0.35 |
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TEXT_TRESHOLD = 0.25 |
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image_source, image = load_image(IMAGE_PATH) |
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boxes, logits, phrases = predict( |
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model=model, |
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image=image, |
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caption=TEXT_PROMPT, |
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box_threshold=BOX_TRESHOLD, |
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text_threshold=TEXT_TRESHOLD |
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) |
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annotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases) |
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cv2.imwrite("annotated_image.jpg", annotated_frame) |
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``` |
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**Web UI** |
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We also provide a demo code to integrate Grounding DINO with Gradio Web UI. See the file `demo/gradio_app.py` for more details. |
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**Notebooks** |
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- 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. |
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- 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. |
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## COCO Zero-shot Evaluations |
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We provide an example to evaluate Grounding DINO zero-shot performance on COCO. The results should be **48.5**. |
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```bash |
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CUDA_VISIBLE_DEVICES=0 \ |
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python demo/test_ap_on_coco.py \ |
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-c groundingdino/config/GroundingDINO_SwinT_OGC.py \ |
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-p weights/groundingdino_swint_ogc.pth \ |
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--anno_path /path/to/annoataions/ie/instances_val2017.json \ |
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--image_dir /path/to/imagedir/ie/val2017 |
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``` |
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## :luggage: Checkpoints |
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<!-- insert a table --> |
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<table> |
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<thead> |
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<tr style="text-align: right;"> |
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<th></th> |
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<th>name</th> |
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<th>backbone</th> |
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<th>Data</th> |
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<th>box AP on COCO</th> |
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<th>Checkpoint</th> |
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<th>Config</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<th>1</th> |
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<td>GroundingDINO-T</td> |
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<td>Swin-T</td> |
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<td>O365,GoldG,Cap4M</td> |
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<td>48.4 (zero-shot) / 57.2 (fine-tune)</td> |
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<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> |
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<td><a href="https://github.com/IDEA-Research/GroundingDINO/blob/main/groundingdino/config/GroundingDINO_SwinT_OGC.py">link</a></td> |
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</tr> |
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<tr> |
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<th>2</th> |
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<td>GroundingDINO-B</td> |
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<td>Swin-B</td> |
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<td>COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO</td> |
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<td>56.7 </td> |
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<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> |
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<td><a href="https://github.com/IDEA-Research/GroundingDINO/blob/main/groundingdino/config/GroundingDINO_SwinB_cfg.py">link</a></td> |
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</tr> |
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</tbody> |
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</table> |
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## :medal_military: Results |
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<details open> |
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<summary><font size="4"> |
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COCO Object Detection Results |
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</font></summary> |
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<img src=".asset/COCO.png" alt="COCO" width="100%"> |
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</details> |
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<details open> |
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<summary><font size="4"> |
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ODinW Object Detection Results |
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</font></summary> |
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<img src=".asset/ODinW.png" alt="ODinW" width="100%"> |
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</details> |
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<details open> |
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<summary><font size="4"> |
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Marrying Grounding DINO with <a href="https://github.com/Stability-AI/StableDiffusion">Stable Diffusion</a> for Image Editing |
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</font></summary> |
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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. |
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<img src=".asset/GD_SD.png" alt="GD_SD" width="100%"> |
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</details> |
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<details open> |
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<summary><font size="4"> |
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Marrying Grounding DINO with <a href="https://github.com/gligen/GLIGEN">GLIGEN</a> for more Detailed Image Editing. |
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</font></summary> |
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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. |
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<img src=".asset/GD_GLIGEN.png" alt="GD_GLIGEN" width="100%"> |
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</details> |
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## :sauropod: Model: Grounding DINO |
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Includes: a text backbone, an image backbone, a feature enhancer, a language-guided query selection, and a cross-modality decoder. |
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## :hearts: Acknowledgement |
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Our model is related to [DINO](https://github.com/IDEA-Research/DINO) and [GLIP](https://github.com/microsoft/GLIP). Thanks for their great work! |
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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. |
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Thanks [Stable Diffusion](https://github.com/Stability-AI/StableDiffusion) and [GLIGEN](https://github.com/gligen/GLIGEN) for their awesome models. |
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## :black_nib: Citation |
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If you find our work helpful for your research, please consider citing the following BibTeX entry. |
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```bibtex |
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@article{liu2023grounding, |
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title={Grounding dino: Marrying dino with grounded pre-training for open-set object detection}, |
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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}, |
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journal={arXiv preprint arXiv:2303.05499}, |
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year={2023} |
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} |
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``` |
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