Spaces:
Running
Running
| title: Grounding DINO Demo | |
| emoji: 💻 | |
| colorFrom: purple | |
| colorTo: yellow | |
| sdk: gradio | |
| sdk_version: 5.9.1 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| # Grounding DINO | |
| [📃Paper](https://arxiv.org/abs/2303.05499) | | |
| [📽️Video](https://www.youtube.com/watch?v=wxWDt5UiwY8) | | |
| [🗯️ Github](https://github.com/IDEA-Research/GroundingDINO) | | |
| [📯Demo on Colab](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb) | | |
| [🤗Demo on HF (Coming soon)]() | |
| [](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb) \ | |
| [](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) | |
| Official pytorch implementation of [Grounding DINO](https://arxiv.org/abs/2303.05499), a stronger open-set object detector. Code is available now! | |
| ## 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. | |
| ## News | |
| [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. Thanks to @Piotr! \ | |
| [2023/03/22] Code is available Now! | |
| ## TODO | |
| - [x] Release inference code and demo. | |
| - [x] Release checkpoints. | |
| - [ ] Grounding DINO with Stable Diffusion and GLIGEN demos. | |
| - [ ] Release training codes. | |
| ## Install | |
| 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. | |
| ```bash | |
| pip install -e . | |
| ``` | |
| ## Demo | |
| ```bash | |
| CUDA_VISIBLE_DEVICES=6 python demo/inference_on_a_image.py \ | |
| -c /path/to/config \ | |
| -p /path/to/checkpoint \ | |
| -i .asset/cats.png \ | |
| -o "outputs/0" \ | |
| -t "cat ear." \ | |
| [--cpu-only] # open it for cpu mode | |
| ``` | |
| See the `demo/inference_on_a_image.py` for more details. | |
| ## Checkpoints | |
| <!-- insert a table --> | |
| <table> | |
| <thead> | |
| <tr style="text-align: right;"> | |
| <th></th> | |
| <th>name</th> | |
| <th>backbone</th> | |
| <th>Data</th> | |
| <th>box AP on COCO</th> | |
| <th>Checkpoint</th> | |
| <th>Config</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr> | |
| <th>1</th> | |
| <td>GroundingDINO-T</td> | |
| <td>Swin-T</td> | |
| <td>O365,GoldG,Cap4M</td> | |
| <td>48.4 (zero-shot) / 57.2 (fine-tune)</td> | |
| <td><a href="https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth">link</a></td> | |
| <td><a href="https://github.com/IDEA-Research/GroundingDINO/blob/main/groundingdino/config/GroundingDINO_SwinT_OGC.py">link</a></td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| ## 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. | |
| ## Citation | |
| If you find our work helpful for your research, please consider citing the following BibTeX entry. | |
| ```bibtex | |
| @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} | |
| } | |
| ``` |