307 lines
16 KiB
Markdown
307 lines
16 KiB
Markdown
<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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## :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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- :rose: [[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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<!-- 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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- [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 Performancce.** 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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- **`2023/04/15`**: 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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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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**Installation:**
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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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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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Install the required dependencies in the current directory.
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```bash
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pip3 install -q -e .
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```
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Create a new directory called "weights" to store the model weights.
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```bash
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mkdir weights
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```
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Change the current directory to the "weights" folder.
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```bash
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cd weights
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```
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Download the model weights file.
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```bash
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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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```
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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/groundingdino/config/GroundingDINO_SwinT_OGC.py \
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-p /GroundingDINO/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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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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## :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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