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-![](./assets/Grounded-SAM_logo.png)
-# Grounded-Segment-Anything
-[![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://youtu.be/oEQYStnF2l8) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/automated-dataset-annotation-and-evaluation-with-grounding-dino-and-sam.ipynb) [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/camenduru/grounded-segment-anything-colab) [![HuggingFace Space](https://img.shields.io/badge/🤗-HuggingFace%20Space-cyan.svg)](https://huggingface.co/spaces/IDEA-Research/Grounded-SAM) [![Replicate](https://replicate.com/cjwbw/grounded-recognize-anything/badge)](https://replicate.com/cjwbw/grounded-recognize-anything)  [![ModelScope Official Demo](https://img.shields.io/badge/ModelScope-Official%20Demo-important)](https://modelscope.cn/studios/tuofeilunhifi/Grounded-Segment-Anything/summary) [![Huggingface Demo by Community](https://img.shields.io/badge/Huggingface-Demo%20by%20Community-red)](https://huggingface.co/spaces/yizhangliu/Grounded-Segment-Anything) [![Stable-Diffusion WebUI](https://img.shields.io/badge/Stable--Diffusion-WebUI%20by%20Community-critical)](https://github.com/continue-revolution/sd-webui-segment-anything) [![Jupyter Notebook Demo](https://img.shields.io/badge/Demo-Jupyter%20Notebook-informational)](./grounded_sam.ipynb)
-We plan to create a very interesting demo by combining [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) and [Segment Anything](https://github.com/facebookresearch/segment-anything) which aims to detect and segment anything with text inputs! And we will continue to improve it and create more interesting demos based on this foundation.
-We are very willing to **help everyone share and promote new projects** based on Segment-Anything, Please check out here for more amazing demos and works in the community: [Highlight Extension Projects](#highlighted-projects). You can submit a new issue (with `project` tag) or a new pull request to add new project's links.
-![](./assets/grounded_sam_new_demo_image.png)
-![](./assets/ram_grounded_sam_new.png)
-**🍄 Why Building this Project?**
-The **core idea** behind this project is to **combine the strengths of different models in order to build a very powerful pipeline for solving complex problems**. And it's worth mentioning that this is a workflow for combining strong expert models, where **all parts can be used separately or in combination, and can be replaced with any similar but different models (like replacing Grounding DINO with GLIP or other detectors / replacing Stable-Diffusion with ControlNet or GLIGEN/ Combining with ChatGPT)**.
-**🍇 Updates**
-- **`2023/12/17`** Support [Grounded-RepViT-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-repvit-sam-demo) demo, thanks a lot for their great work!
-- **`2023/12/16`** Support [Grounded-Edge-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-edge-sam-demo) demo, thanks a lot for their great work!
-- **`2023/12/10`** Support [Grounded-Efficient-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-efficient-sam-demo) demo, thanks a lot for their great work!
-- **`2023/11/24`** Release [RAM++](https://arxiv.org/abs/2310.15200), which is the next generation of RAM. RAM++ can recognize any category with high accuracy, including both predefined common categories and diverse open-set categories.
-- **`2023/11/23`** Release our newly proposed visual prompt counting model [T-Rex](https://github.com/IDEA-Research/T-Rex). The introduction [Video](https://www.youtube.com/watch?v=engIEhZogAQ) and [Demo](https://deepdataspace.com/playground/ivp) is available in [DDS](https://github.com/IDEA-Research/deepdataspace) now.
-- **`2023/07/25`** Support [Light-HQ-SAM](https://github.com/SysCV/sam-hq) in [EfficientSAM](./EfficientSAM/), credits to [Mingqiao Ye](https://github.com/ymq2017) and [Lei Ke](https://github.com/lkeab), thanks a lot for their great work!
-- **`2023/07/14`** Combining **Grounding-DINO-B** with [SAM-HQ](https://github.com/SysCV/sam-hq) achieves **49.6 mean AP** in [Segmentation in the Wild](https://eval.ai/web/challenges/challenge-page/1931/overview) competition zero-shot track, surpassing Grounded-SAM by **3.6 mean AP**, thanks for their great work!
-- **`2023/06/28`** Combining Grounding-DINO with Efficient SAM variants including [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) and [MobileSAM](https://github.com/ChaoningZhang/MobileSAM) in [EfficientSAM](./EfficientSAM/) for faster annotating, thanks a lot for their great work!
-- **`2023/06/20`** By combining **Grounding-DINO-L** with **SAM-ViT-H**, Grounded-SAM achieves 46.0 mean AP in [Segmentation in the Wild](https://eval.ai/web/challenges/challenge-page/1931/overview) competition zero-shot track on [CVPR 2023 workshop](https://computer-vision-in-the-wild.github.io/cvpr-2023/), surpassing [UNINEXT (CVPR 2023)](https://github.com/MasterBin-IIAU/UNINEXT) by about **4 mean AP**.
-- **`2023/06/16`** Release [RAM-Grounded-SAM Replicate Online Demo](https://replicate.com/cjwbw/ram-grounded-sam). Thanks a lot to [Chenxi](https://chenxwh.github.io/) for providing this nice demo 🌹.
-- **`2023/06/14`** Support [RAM-Grounded-SAM & SAM-HQ](./automatic_label_ram_demo.py) and update [Simple Automatic Label Demo](./automatic_label_ram_demo.py) to support [RAM](https://github.com/OPPOMKLab/recognize-anything), setting up a strong automatic annotation pipeline.
-- **`2023/06/13`** Checkout the [Autodistill: Train YOLOv8 with ZERO Annotations](https://youtu.be/gKTYMfwPo4M) tutorial to learn how to use Grounded-SAM + [Autodistill](https://github.com/autodistill/autodistill) for automated data labeling and real-time model training.
-- **`2023/06/13`** Support [SAM-HQ](https://github.com/SysCV/sam-hq) in [Grounded-SAM Demo](#running_man-grounded-sam-detect-and-segment-everything-with-text-prompt) for higher quality prediction.
-- **`2023/06/12`** Support [RAM-Grounded-SAM](#label-grounded-sam-with-ram-or-tag2text-for-automatic-labeling) for strong automatic labeling pipeline! Thanks for [Recognize-Anything](https://github.com/OPPOMKLab/recognize-anything).
-- **`2023/06/01`** Our Grounded-SAM has been accepted to present a **demo** at [ICCV 2023](https://iccv2023.thecvf.com/)! See you in Paris!
-- **`2023/05/23`**: Support `Image-Referring-Segment`, `Audio-Referring-Segment` and `Text-Referring-Segment` in [ImageBind-SAM](./playground/ImageBind_SAM/).
-- **`2023/05/03`**: Checkout the [Automated Dataset Annotation and Evaluation with GroundingDINO and SAM](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/automated-dataset-annotation-and-evaluation-with-grounding-dino-and-sam.ipynb) which is an amazing tutorial on automatic labeling! Thanks a lot for [Piotr Skalski](https://github.com/SkalskiP) and [Roboflow](https://github.com/roboflow/notebooks)!
-## Table of Contents
-- [Grounded-Segment-Anything](#grounded-segment-anything)
-  - [Preliminary Works](#preliminary-works)
-  - [Highlighted Projects](#highlighted-projects)
-- [Installation](#installation)
-  - [Install with Docker](#install-with-docker)
-  - [Install locally](#install-without-docker)
-- [Grounded-SAM Playground](#grounded-sam-playground)
-  - [Step-by-Step Notebook Demo](#open_book-step-by-step-notebook-demo)
-  - [GroundingDINO: Detect Everything with Text Prompt](#running_man-groundingdino-detect-everything-with-text-prompt)
-  - [Grounded-SAM: Detect and Segment Everything with Text Prompt](#running_man-grounded-sam-detect-and-segment-everything-with-text-prompt)
-  - [Grounded-SAM with Inpainting: Detect, Segment and Generate Everything with Text Prompt](#skier-grounded-sam-with-inpainting-detect-segment-and-generate-everything-with-text-prompt)
-  - [Grounded-SAM and Inpaint Gradio APP](#golfing-grounded-sam-and-inpaint-gradio-app)
-  - [Grounded-SAM with RAM or Tag2Text for Automatic Labeling](#label-grounded-sam-with-ram-or-tag2text-for-automatic-labeling)
-  - [Grounded-SAM with BLIP & ChatGPT for Automatic Labeling](#robot-grounded-sam-with-blip-for-automatic-labeling)
-  - [Grounded-SAM with Whisper: Detect and Segment Anything with Audio](#open_mouth-grounded-sam-with-whisper-detect-and-segment-anything-with-audio)
-  - [Grounded-SAM ChatBot with Visual ChatGPT](#speech_balloon-grounded-sam-chatbot-demo)
-  - [Grounded-SAM with OSX for 3D Whole-Body Mesh Recovery](#man_dancing-run-grounded-segment-anything--osx-demo)
-  - [Grounded-SAM with VISAM for Tracking and Segment Anything](#man_dancing-run-grounded-segment-anything--visam-demo)
-  - [Interactive Fashion-Edit Playground: Click for Segmentation And Editing](#dancers-interactive-editing)
-  - [Interactive Human-face Editing Playground: Click And Editing Human Face](#dancers-interactive-editing)
-  - [3D Box Via Segment Anything](#camera-3d-box-via-segment-anything)
-  - [Playground: More Interesting and Imaginative Demos with Grounded-SAM](./playground/)
-    - [DeepFloyd: Image Generation with Text Prompt](./playground/DeepFloyd/)
-    - [PaintByExample: Exemplar-based Image Editing with Diffusion Models](./playground/PaintByExample/)
-    - [LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions](./playground/LaMa/)
-    - [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](./playground/RePaint/)
-    - [ImageBind with SAM: Segment with Different Modalities](./playground/ImageBind_SAM/)
-  - [Efficient SAM Series for Faster Annotation](./EfficientSAM/)
-    - [Grounded-FastSAM Demo](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-fastsam-demo)
-    - [Grounded-MobileSAM Demo](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-mobilesam-demo)
-    - [Grounded-Light-HQSAM Demo](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-light-hqsam-demo)
-    - [Grounded-Efficient-SAM Demo](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-efficient-sam-demo)
-    - [Grounded-Edge-SAM Demo](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-edge-sam-demo)
-    - [Grounded-RepViT-SAM Demo](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-repvit-sam-demo)
-## Preliminary Works
-Here we provide some background knowledge that you may need to know before trying the demos.
-<div align="center">
-| Title | Intro | Description | Links |
-|:----:|:----:|:----:|:----:|
-| [Segment-Anything](https://arxiv.org/abs/2304.02643) | ![](https://github.com/facebookresearch/segment-anything/blob/main/assets/model_diagram.png?raw=true) | A strong foundation model aims to segment everything in an image, which needs prompts (as boxes/points/text) to generate masks | [[Github](https://github.com/facebookresearch/segment-anything)] <br> [[Page](https://segment-anything.com/)] <br> [[Demo](https://segment-anything.com/demo)] |
-| [Grounding DINO](https://arxiv.org/abs/2303.05499) | ![](https://github.com/IDEA-Research/GroundingDINO/blob/main/.asset/hero_figure.png?raw=True) | A strong zero-shot detector which is capable of to generate high quality boxes and labels with free-form text. | [[Github](https://github.com/IDEA-Research/GroundingDINO)] <br> [[Demo](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo)] |
-| [OSX](http://arxiv.org/abs/2303.16160) | ![](https://github.com/IDEA-Research/OSX/blob/main/assets/demo_video.gif?raw=True) | A strong and efficient one-stage motion capture method to generate high quality 3D human mesh from monucular image. OSX also releases a large-scale upper-body dataset UBody for a more accurate reconstrution in the upper-body scene. | [[Github](https://github.com/IDEA-Research/OSX)] <br> [[Page](https://osx-ubody.github.io/)] <br> [[Video](https://osx-ubody.github.io/)] <br> [[Data](https://docs.google.com/forms/d/e/1FAIpQLSehgBP7wdn_XznGAM2AiJPiPLTqXXHw5uX9l7qeQ1Dh9HoO_A/viewform)] |
-| [Stable-Diffusion](https://arxiv.org/abs/2112.10752) | ![](https://github.com/CompVis/stable-diffusion/blob/main/assets/stable-samples/txt2img/merged-0006.png?raw=True) | A super powerful open-source latent text-to-image diffusion model | [[Github](https://github.com/CompVis/stable-diffusion)] <br> [[Page](https://ommer-lab.com/research/latent-diffusion-models/)] |
-| [RAM++](https://arxiv.org/abs/2310.15200) | ![](https://github.com/xinyu1205/recognize-anything/blob/main/images/ram_plus_compare.jpg) | RAM++ is the next generation of RAM, which can recognize any category with high accuracy. | [[Github](https://github.com/OPPOMKLab/recognize-anything)] |
-| [RAM](https://recognize-anything.github.io/) | ![](https://github.com/xinyu1205/Tag2Text/raw/main/images/localization_and_recognition.jpg) | RAM is an image tagging model, which can recognize any common category with high accuracy. | [[Github](https://github.com/OPPOMKLab/recognize-anything)] <br> [[Demo](https://huggingface.co/spaces/xinyu1205/Recognize_Anything-Tag2Text)] |
-| [BLIP](https://arxiv.org/abs/2201.12086) | ![](https://github.com/salesforce/LAVIS/raw/main/docs/_static/logo_final.png) | A wonderful language-vision model for image understanding. | [[GitHub](https://github.com/salesforce/LAVIS)] |
-| [Visual ChatGPT](https://arxiv.org/abs/2303.04671) | ![](https://github.com/microsoft/TaskMatrix/raw/main/assets/figure.jpg) | A wonderful tool that connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting. | [[Github](https://github.com/microsoft/TaskMatrix)] <br> [[Demo](https://huggingface.co/spaces/microsoft/visual_chatgpt)] |
-| [Tag2Text](https://tag2text.github.io/) | ![](https://github.com/xinyu1205/Tag2Text/raw/main/images/tag2text_framework.png) | An efficient and controllable vision-language model which can simultaneously output superior image captioning and image tagging. | [[Github](https://github.com/OPPOMKLab/recognize-anything)] <br> [[Demo](https://huggingface.co/spaces/xinyu1205/Tag2Text)] |
-| [VoxelNeXt](https://arxiv.org/abs/2303.11301) | ![](https://github.com/dvlab-research/VoxelNeXt/raw/master/docs/sequence-v2.gif) | A clean, simple, and fully-sparse 3D object detector, which predicts objects directly upon sparse voxel features. | [[Github](https://github.com/dvlab-research/VoxelNeXt)]
-</div>
-## Highlighted Projects
-Here we provide some impressive works you may find interesting:
-<div align="center">
-| Title | Description | Links |
-|:---:|:---:|:---:|
-| [Semantic-SAM](https://github.com/UX-Decoder/Semantic-SAM) | A universal image segmentation model to enable segment and recognize anything at any desired granularity | [[Github](https://github.com/UX-Decoder/Semantic-SAM)] <br> [[Demo](https://github.com/UX-Decoder/Semantic-SAM)] |
-| [SEEM: Segment Everything Everywhere All at Once](https://arxiv.org/pdf/2304.06718.pdf) | A powerful promptable segmentation model supports segmenting with various types of prompts (text, point, scribble, referring image, etc.) and any combination of prompts. | [[Github](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once)] <br> [[Demo](https://huggingface.co/spaces/xdecoder/SEEM)] |
-| [OpenSeeD](https://arxiv.org/pdf/2303.08131.pdf) | A simple framework for open-vocabulary segmentation and detection which supports interactive segmentation with box input to generate mask | [[Github](https://github.com/IDEA-Research/OpenSeeD)] |
-| [LLaVA](https://arxiv.org/abs/2304.08485) | Visual instruction tuning with GPT-4 | [[Github](https://github.com/haotian-liu/LLaVA)] <br> [[Page](https://llava-vl.github.io/)] <br> [[Demo](https://llava.hliu.cc/)] <br> [[Data](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K)] <br> [[Model](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v0)] |
-| [GenSAM](https://arxiv.org/abs/2312.07374) | Relaxing the instance-specific manual prompt requirement in SAM through training-free test-time adaptation | [[Github](https://github.com/jyLin8100/GenSAM)] <br> [[Page](https://lwpyh.github.io/GenSAM/)] |
-</div>
-We also list some awesome segment-anything extension projects here you may find interesting:
-- [Computer Vision in the Wild (CVinW) Readings](https://github.com/Computer-Vision-in-the-Wild/CVinW_Readings) for those who are interested in open-set tasks in computer vision.
-- [Zero-Shot Anomaly Detection](https://github.com/caoyunkang/GroundedSAM-zero-shot-anomaly-detection) by Yunkang Cao
-- [EditAnything: ControlNet + StableDiffusion based on the SAM segmentation mask](https://github.com/sail-sg/EditAnything) by Shanghua Gao and Pan Zhou
-- [IEA: Image Editing Anything](https://github.com/feizc/IEA) by Zhengcong Fei
-- [SAM-MMRorate: Combining Rotated Object Detector and SAM](https://github.com/Li-Qingyun/sam-mmrotate) by Qingyun Li and Xue Yang
-- [Awesome-Anything](https://github.com/VainF/Awesome-Anything) by Gongfan Fang
-- [Prompt-Segment-Anything](https://github.com/RockeyCoss/Prompt-Segment-Anything) by Rockey
-- [WebUI for Segment-Anything and Grounded-SAM](https://github.com/continue-revolution/sd-webui-segment-anything) by Chengsong Zhang
-- [Inpainting Anything: Inpaint Anything with SAM + Inpainting models](https://github.com/geekyutao/Inpaint-Anything) by Tao Yu
-- [Grounded Segment Anything From Objects to Parts: Combining Segment-Anything with VLPart & GLIP & Visual ChatGPT](https://github.com/Cheems-Seminar/segment-anything-and-name-it) by Peize Sun and Shoufa Chen
-- [Narapi-SAM: Integration of Segment Anything into Narapi (A nice viewer for SAM)](https://github.com/MIC-DKFZ/napari-sam) by MIC-DKFZ
-- [Grounded Segment Anything Colab](https://github.com/camenduru/grounded-segment-anything-colab) by camenduru
-- [Optical Character Recognition with Segment Anything](https://github.com/yeungchenwa/OCR-SAM) by Zhenhua Yang
-- [Transform Image into Unique Paragraph with ChatGPT, BLIP2, OFA, GRIT, Segment Anything, ControlNet](https://github.com/showlab/Image2Paragraph) by showlab
-- [Lang-Segment-Anything: Another awesome demo for combining GroundingDINO with Segment-Anything](https://github.com/luca-medeiros/lang-segment-anything) by Luca Medeiros
-- [🥳 🚀 **Playground: Integrate SAM and OpenMMLab!**](https://github.com/open-mmlab/playground)
-- [3D-object via Segment Anything](https://github.com/dvlab-research/3D-Box-Segment-Anything) by Yukang Chen
-- [Image2Paragraph: Transform Image Into Unique Paragraph](https://github.com/showlab/Image2Paragraph) by Show Lab
-- [Zero-shot Scene Graph Generate with Grounded-SAM](https://github.com/showlab/Image2Paragraph) by JackWhite-rwx
-- [CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks](https://github.com/xmed-lab/CLIP_Surgery) by Eli-YiLi
-- [Panoptic-Segment-Anything: Zero-shot panoptic segmentation using SAM](https://github.com/segments-ai/panoptic-segment-anything) by segments-ai
-- [Caption-Anything: Generates Descriptive Captions for Any Object within an Image](https://github.com/ttengwang/Caption-Anything) by Teng Wang
-- [Segment-Anything-3D: Transferring Segmentation Information of 2D Images to 3D Space](https://github.com/Pointcept/SegmentAnything3D) by Yunhan Yang
-- [Expediting SAM without Fine-tuning](https://github.com/Expedit-LargeScale-Vision-Transformer/Expedit-SAM) by Weicong Liang and Yuhui Yuan
-- [Semantic Segment Anything: Providing Rich Semantic Category Annotations for SAM](https://github.com/fudan-zvg/Semantic-Segment-Anything) by Jiaqi Chen and Zeyu Yang and Li Zhang
-- [Enhance Everything: Combining SAM with Image Restoration and Enhancement Tasks](https://github.com/lixinustc/Enhance-Anything) by Xin Li
-- [DragGAN](https://github.com/Zeqiang-Lai/DragGAN) by Shanghai AI Lab.
-## Installation
-The code requires `python>=3.8`, as well as `pytorch>=1.7` and `torchvision>=0.8`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.
-### Install with Docker
-Open one terminal:
-```
-make build-image
-```
-```
-make run
-```
-That's it.
-If you would like to allow visualization across docker container, open another terminal and type:
-```
-xhost +
-```
-### Install without Docker
-You should set the environment variable manually as follows if you want to build a local GPU environment for Grounded-SAM:
-```bash
-export AM_I_DOCKER=False
-export BUILD_WITH_CUDA=True
-export CUDA_HOME=/path/to/cuda-11.3/
-```
-Install Segment Anything:
-```bash
-python -m pip install -e segment_anything
-```
-Install Grounding DINO:
-```bash
-python -m pip install -e GroundingDINO
-```
-Install diffusers:
-```bash
-pip install --upgrade diffusers[torch]
-```
-Install osx:
-```bash
-git submodule update --init --recursive
-cd grounded-sam-osx && bash install.sh
-```
-Install RAM & Tag2Text:
-```bash
-git clone https://github.com/xinyu1205/recognize-anything.git
-pip install -r ./recognize-anything/requirements.txt
-pip install -e ./recognize-anything/
-```
-The following optional dependencies are necessary for mask post-processing, saving masks in COCO format, the example notebooks, and exporting the model in ONNX format. `jupyter` is also required to run the example notebooks.
-```
-pip install opencv-python pycocotools matplotlib onnxruntime onnx ipykernel
-```
-More details can be found in [install segment anything](https://github.com/facebookresearch/segment-anything#installation) and [install GroundingDINO](https://github.com/IDEA-Research/GroundingDINO#install) and [install OSX](https://github.com/IDEA-Research/OSX)
-## Grounded-SAM Playground
-Let's start exploring our Grounding-SAM Playground and we will release more interesting demos in the future, stay tuned!
-## :open_book: Step-by-Step Notebook Demo
-Here we list some notebook demo provided in this project:
-- [grounded_sam.ipynb](grounded_sam.ipynb)
-- [grounded_sam_colab_demo.ipynb](grounded_sam_colab_demo.ipynb)
-- [grounded_sam_3d_box.ipynb](grounded_sam_3d_box)
-### :running_man: GroundingDINO: Detect Everything with Text Prompt
-:grapes: [[arXiv Paper](https://arxiv.org/abs/2303.05499)] &nbsp; :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)] &nbsp; :sunflower: [[Try Huggingface Demo](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo)] &nbsp; :mushroom: [[Automated Dataset Annotation and Evaluation](https://youtu.be/C4NqaRBz_Kw)]
-Here's the step-by-step tutorial on running `GroundingDINO` demo:
-**Step 1: Download the pretrained weights**
-```bash
-cd Grounded-Segment-Anything
-# download the pretrained groundingdino-swin-tiny model
-wget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth
-```
-**Step 2: Running the demo**
-```bash
-python grounding_dino_demo.py
-```
-<details>
-<summary> <b> Running with Python (same as demo but you can run it anywhere after installing GroundingDINO) </b> </summary>
-```python
-from groundingdino.util.inference import load_model, load_image, predict, annotate
-import cv2
-model = load_model("GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py", "./groundingdino_swint_ogc.pth")
-IMAGE_PATH = "assets/demo1.jpg"
-TEXT_PROMPT = "bear."
-BOX_THRESHOLD = 0.35
-TEXT_THRESHOLD = 0.25
-image_source, image = load_image(IMAGE_PATH)
-boxes, logits, phrases = predict(
-    model=model,
-    image=image,
-    caption=TEXT_PROMPT,
-    box_threshold=BOX_THRESHOLD,
-    text_threshold=TEXT_THRESHOLD
-)
-annotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases)
-cv2.imwrite("annotated_image.jpg", annotated_frame)
-```
-</details>
-<br>
-**Tips**
-- If you want to detect multiple objects in one sentence with [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO), we suggest separating each name with `.` . An example: `cat . dog . chair .`
-**Step 3: Check the annotated image**
-The annotated image will be saved as `./annotated_image.jpg`.
-<div align="center">
-| Text Prompt | Demo Image | Annotated Image |
-|:----:|:----:|:----:|
-| `Bear.` | ![](./assets/demo1.jpg)  | ![](./assets/annotated_image.jpg) |
-| `Horse. Clouds. Grasses. Sky. Hill` | ![](./assets/demo7.jpg)  | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/grounding_dino/groundingdino_demo7.jpg?raw=true)
-</div>
-### :running_man: Grounded-SAM: Detect and Segment Everything with Text Prompt
-Here's the step-by-step tutorial on running `Grounded-SAM` demo:
-**Step 1: Download the pretrained weights**
-```bash
-cd Grounded-Segment-Anything
-wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
-wget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth
-```
-We provide two versions of Grounded-SAM demo here:
-- [grounded_sam_demo.py](./grounded_sam_demo.py): our original implementation for Grounded-SAM.
-- [grounded_sam_simple_demo.py](./grounded_sam_simple_demo.py) our updated more elegant version for Grounded-SAM.
-**Step 2: Running original grounded-sam demo**
-```python
-export CUDA_VISIBLE_DEVICES=0
-python grounded_sam_demo.py \
-  --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \
-  --grounded_checkpoint groundingdino_swint_ogc.pth \
-  --sam_checkpoint sam_vit_h_4b8939.pth \
-  --input_image assets/demo1.jpg \
-  --output_dir "outputs" \
-  --box_threshold 0.3 \
-  --text_threshold 0.25 \
-  --text_prompt "bear" \
-  --device "cuda"
-```
-The annotated results will be saved in `./outputs` as follows
-<div align="center">
-| Input Image | Annotated Image | Generated Mask |
-|:----:|:----:|:----:|
-| ![](./assets/demo1.jpg) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/grounded_sam/original_grounded_sam_demo1.jpg?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/grounded_sam/mask.jpg?raw=true) |
-</div>
-**Step 3: Running grounded-sam demo with sam-hq**
-- Download the demo image
-```bash
-wget https://github.com/IDEA-Research/detrex-storage/releases/download/grounded-sam-storage/sam_hq_demo_image.png
-```
-- Download SAM-HQ checkpoint [here](https://github.com/SysCV/sam-hq#model-checkpoints)
-- Running grounded-sam-hq demo as follows:
-```python
-export CUDA_VISIBLE_DEVICES=0
-python grounded_sam_demo.py \
-  --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \
-  --grounded_checkpoint groundingdino_swint_ogc.pth \
-  --sam_hq_checkpoint ./sam_hq_vit_h.pth \  # path to sam-hq checkpoint
-  --use_sam_hq \  # set to use sam-hq model
-  --input_image sam_hq_demo_image.png \
-  --output_dir "outputs" \
-  --box_threshold 0.3 \
-  --text_threshold 0.25 \
-  --text_prompt "chair." \
-  --device "cuda"
-```
-The annotated results will be saved in `./outputs` as follows
-<div align="center">
-| Input Image | SAM Output | SAM-HQ Output |
-|:----:|:----:|:----:|
-| ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/sam_hq/sam_hq_demo.png?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/sam_hq/sam_output.jpg?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/sam_hq/sam_hq_output.jpg?raw=true) |
-</div>
-**Step 4: Running the updated grounded-sam demo (optional)**
-Note that this demo is almost same as the original demo, but **with more elegant code**.
-```python
-python grounded_sam_simple_demo.py
-```
-The annotated results will be saved as `./groundingdino_annotated_image.jpg` and `./grounded_sam_annotated_image.jpg`
-<div align="center">
-| Text Prompt | Input Image | GroundingDINO Annotated Image | Grounded-SAM Annotated Image |
-|:----:|:----:|:----:|:----:|
-| `The running dog` | ![](./assets/demo2.jpg) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/grounded_sam/groundingdino_annotated_image_demo2.jpg?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/grounded_sam/grounded_sam_annotated_image_demo2.jpg?raw=true) |
-| `Horse. Clouds. Grasses. Sky. Hill` | ![](./assets/demo7.jpg) | ![](assets/groundingdino_annotated_image.jpg) | ![](assets/grounded_sam_annotated_image.jpg) |
-</div>
-### :skier: Grounded-SAM with Inpainting: Detect, Segment and Generate Everything with Text Prompt
-**Step 1: Download the pretrained weights**
-```bash
-cd Grounded-Segment-Anything
-wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
-wget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth
-```
-**Step 2: Running grounded-sam inpainting demo**
-```bash
-CUDA_VISIBLE_DEVICES=0
-python grounded_sam_inpainting_demo.py \
-  --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \
-  --grounded_checkpoint groundingdino_swint_ogc.pth \
-  --sam_checkpoint sam_vit_h_4b8939.pth \
-  --input_image assets/inpaint_demo.jpg \
-  --output_dir "outputs" \
-  --box_threshold 0.3 \
-  --text_threshold 0.25 \
-  --det_prompt "bench" \
-  --inpaint_prompt "A sofa, high quality, detailed" \
-  --device "cuda"
-```
-The annotated and inpaint image will be saved in `./outputs`
-**Step 3: Check the results**
-<div align="center">
-| Input Image | Det Prompt | Annotated Image | Inpaint Prompt | Inpaint Image |
-|:---:|:---:|:---:|:---:|:---:|
-|![](./assets/inpaint_demo.jpg) | `Bench` | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/grounded_sam_inpaint/grounded_sam_output.jpg?raw=true) | `A sofa, high quality, detailed` | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/grounded_sam_inpaint/grounded_sam_inpainting_output.jpg?raw=true) |
-</div>
-### :golfing: Grounded-SAM and Inpaint Gradio APP
-We support 6 tasks in the local Gradio APP：
-1. **scribble**: Segmentation is achieved through Segment Anything and mouse click interaction (you need to click on the object with the mouse, no need to specify the prompt).
-2. **automask**: Segment the entire image at once through Segment Anything (no need to specify a prompt).
-3. **det**: Realize detection through Grounding DINO and text interaction (text prompt needs to be specified).
-4. **seg**: Realize text interaction by combining Grounding DINO and Segment Anything to realize detection + segmentation (need to specify text prompt).
-5. **inpainting**: By combining Grounding DINO + Segment Anything + Stable Diffusion to achieve text exchange and replace the target object (need to specify text prompt and inpaint prompt) .
-6. **automatic**: By combining BLIP + Grounding DINO + Segment Anything to achieve non-interactive detection + segmentation (no need to specify prompt).
-```bash
-python gradio_app.py
-```
-- The gradio_app visualization as follows:
-![](./assets/gradio_demo.png)
-### :label: Grounded-SAM with RAM or Tag2Text for Automatic Labeling
-[**The Recognize Anything Models**](https://github.com/OPPOMKLab/recognize-anything) are a series of open-source and strong fundamental image recognition models, including [RAM++](https://arxiv.org/abs/2310.15200), [RAM](https://arxiv.org/abs/2306.03514) and [Tag2text](https://arxiv.org/abs/2303.05657).
-It is seamlessly linked to generate pseudo labels automatically as follows:
-1. Use RAM/Tag2Text to generate tags.
-2. Use Grounded-Segment-Anything to generate the boxes and masks.
-**Step 1: Init submodule and download the pretrained checkpoint**
-- Init submodule:
-```bash
-cd Grounded-Segment-Anything
-git submodule init
-git submodule update
-```
-- Download pretrained weights for `GroundingDINO`, `SAM` and `RAM/Tag2Text`:
-```bash
-wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
-wget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth
-wget https://huggingface.co/spaces/xinyu1205/Tag2Text/resolve/main/ram_swin_large_14m.pth
-wget https://huggingface.co/spaces/xinyu1205/Tag2Text/resolve/main/tag2text_swin_14m.pth
-```
-**Step 2: Running the demo with RAM**
-```bash
-export CUDA_VISIBLE_DEVICES=0
-python automatic_label_ram_demo.py \
-  --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \
-  --ram_checkpoint ram_swin_large_14m.pth \
-  --grounded_checkpoint groundingdino_swint_ogc.pth \
-  --sam_checkpoint sam_vit_h_4b8939.pth \
-  --input_image assets/demo9.jpg \
-  --output_dir "outputs" \
-  --box_threshold 0.25 \
-  --text_threshold 0.2 \
-  --iou_threshold 0.5 \
-  --device "cuda"
-```
-**Step 2: Or Running the demo with Tag2Text**
-```bash
-export CUDA_VISIBLE_DEVICES=0
-python automatic_label_tag2text_demo.py \
-  --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \
-  --tag2text_checkpoint tag2text_swin_14m.pth \
-  --grounded_checkpoint groundingdino_swint_ogc.pth \
-  --sam_checkpoint sam_vit_h_4b8939.pth \
-  --input_image assets/demo9.jpg \
-  --output_dir "outputs" \
-  --box_threshold 0.25 \
-  --text_threshold 0.2 \
-  --iou_threshold 0.5 \
-  --device "cuda"
-```
-- RAM++ significantly improves the open-set capability of RAM, for [RAM++ inference on unseen categoreis](https://github.com/xinyu1205/recognize-anything#ram-inference-on-unseen-categories-open-set).
-- Tag2Text also provides powerful captioning capabilities, and the process with captions can refer to [BLIP](#robot-run-grounded-segment-anything--blip-demo).
-- The pseudo labels and model prediction visualization will be saved in `output_dir` as follows (right figure):
-![](./assets/automatic_label_output/demo9_tag2text_ram.jpg)
-### :robot: Grounded-SAM with BLIP for Automatic Labeling
-It is easy to generate pseudo labels automatically as follows:
-1. Use BLIP (or other caption models) to generate a caption.
-2. Extract tags from the caption. We use ChatGPT to handle the potential complicated sentences.
-3. Use Grounded-Segment-Anything to generate the boxes and masks.
-- Run Demo
-```bash
-export OPENAI_API_KEY=your_openai_key
-export OPENAI_API_BASE=https://closeai.deno.dev/v1
-export CUDA_VISIBLE_DEVICES=0
-python automatic_label_demo.py \
-  --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \
-  --grounded_checkpoint groundingdino_swint_ogc.pth \
-  --sam_checkpoint sam_vit_h_4b8939.pth \
-  --input_image assets/demo3.jpg \
-  --output_dir "outputs" \
-  --openai_key $OPENAI_API_KEY \
-  --box_threshold 0.25 \
-  --text_threshold 0.2 \
-  --iou_threshold 0.5 \
-  --device "cuda"
-```
-- When you don't have a paid Account for ChatGPT is also possible to use NLTK instead. Just don't include the ```openai_key``` Parameter when starting the Demo.
-  - The Script will automatically download the necessary NLTK Data.
-- The pseudo labels and model prediction visualization will be saved in `output_dir` as follows:
-![](./assets/automatic_label_output_demo3.jpg)
-### :open_mouth: Grounded-SAM with Whisper: Detect and Segment Anything with Audio
-Detect and segment anything with speech!
-![](assets/acoustics/gsam_whisper_inpainting_demo.png)
-**Install Whisper**
-```bash
-pip install -U openai-whisper
-```
-See the [whisper official page](https://github.com/openai/whisper#setup) if you have other questions for the installation.
-**Run Voice-to-Label Demo**
-Optional: Download the demo audio file
-```bash
-wget https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/demo_audio.mp3
-```
-```bash
-export CUDA_VISIBLE_DEVICES=0
-python grounded_sam_whisper_demo.py \
-  --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \
-  --grounded_checkpoint groundingdino_swint_ogc.pth \
-  --sam_checkpoint sam_vit_h_4b8939.pth \
-  --input_image assets/demo4.jpg \
-  --output_dir "outputs" \
-  --box_threshold 0.3 \
-  --text_threshold 0.25 \
-  --speech_file "demo_audio.mp3" \
-  --device "cuda"
-```
-![](./assets/grounded_sam_whisper_output.jpg)
-**Run Voice-to-inpaint Demo**
-You can enable chatgpt to help you automatically detect the object and inpainting order with `--enable_chatgpt`.
-Or you can specify the object you want to inpaint [stored in `args.det_speech_file`] and the text you want to inpaint with [stored in `args.inpaint_speech_file`].
-```bash
-export OPENAI_API_KEY=your_openai_key
-export OPENAI_API_BASE=https://closeai.deno.dev/v1
-# Example: enable chatgpt
-export CUDA_VISIBLE_DEVICES=0
-python grounded_sam_whisper_inpainting_demo.py \
-  --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \
-  --grounded_checkpoint groundingdino_swint_ogc.pth \
-  --sam_checkpoint sam_vit_h_4b8939.pth \
-  --input_image assets/inpaint_demo.jpg \
-  --output_dir "outputs" \
-  --box_threshold 0.3 \
-  --text_threshold 0.25 \
-  --prompt_speech_file assets/acoustics/prompt_speech_file.mp3 \
-  --enable_chatgpt \
-  --openai_key $OPENAI_API_KEY\
-  --device "cuda"
-```
-```bash
-# Example: without chatgpt
-export CUDA_VISIBLE_DEVICES=0
-python grounded_sam_whisper_inpainting_demo.py \
-  --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \
-  --grounded_checkpoint groundingdino_swint_ogc.pth \
-  --sam_checkpoint sam_vit_h_4b8939.pth \
-  --input_image assets/inpaint_demo.jpg \
-  --output_dir "outputs" \
-  --box_threshold 0.3 \
-  --text_threshold 0.25 \
-  --det_speech_file "assets/acoustics/det_voice.mp3" \
-  --inpaint_speech_file "assets/acoustics/inpaint_voice.mp3" \
-  --device "cuda"
-```
-![](./assets/acoustics/gsam_whisper_inpainting_pipeline.png)
-### :speech_balloon: Grounded-SAM ChatBot Demo
-https://user-images.githubusercontent.com/24236723/231955561-2ae4ec1a-c75f-4cc5-9b7b-517aa1432123.mp4
-Following [Visual ChatGPT](https://github.com/microsoft/visual-chatgpt), we add a ChatBot for our project. Currently, it supports:
-1. "Describe the image."
-2. "Detect the dog (and the cat) in the image."
-3. "Segment anything in the image."
-4. "Segment the dog (and the cat) in the image."
-5. "Help me label the image."
-6. "Replace the dog with a cat in the image."
-To use the ChatBot:
-- Install whisper if you want to use audio as input.
-- Set the default model setting in the tool `Grounded_dino_sam_inpainting`.
-- Run Demo
-```bash
-export OPENAI_API_KEY=your_openai_key
-export OPENAI_API_BASE=https://closeai.deno.dev/v1
-export CUDA_VISIBLE_DEVICES=0
-python chatbot.py
-```
-### :man_dancing: Run Grounded-Segment-Anything + OSX Demo
-<p align="middle">
-<img src="assets/osx/grouned_sam_osx_demo.gif">
-<br>
-</p>
-- Download the checkpoint `osx_l_wo_decoder.pth.tar` from [here](https://drive.google.com/drive/folders/1x7MZbB6eAlrq5PKC9MaeIm4GqkBpokow?usp=share_link) for OSX:
-- Download the human model files and place it into `grounded-sam-osx/utils/human_model_files` following the instruction of [OSX](https://github.com/IDEA-Research/OSX).
-- Run Demo
-```shell
-export CUDA_VISIBLE_DEVICES=0
-python grounded_sam_osx_demo.py \
-  --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \
-  --grounded_checkpoint groundingdino_swint_ogc.pth \
-  --sam_checkpoint sam_vit_h_4b8939.pth \
-  --osx_checkpoint osx_l_wo_decoder.pth.tar \
-  --input_image assets/osx/grounded_sam_osx_demo.png \
-  --output_dir "outputs" \
-  --box_threshold 0.3 \
-  --text_threshold 0.25 \
-  --text_prompt "humans, chairs" \
-  --device "cuda"
-```
-- The model prediction visualization will be saved in `output_dir` as follows:
-<img src="assets/osx/grounded_sam_osx_output.jpg" style="zoom: 49%;" />
-- We also support promptable 3D whole-body mesh recovery. For example, you can track someone with a text prompt  and estimate his 3D pose and shape :
-| ![space-1.jpg](assets/osx/grounded_sam_osx_output1.jpg) |
-| :---------------------------------------------------: |
-|             *A person with pink clothes*              |
-| ![space-1.jpg](assets/osx/grounded_sam_osx_output2.jpg) |
-| :---------------------------------------------------: |
-|               *A man with a sunglasses*               |
-## :man_dancing: Run Grounded-Segment-Anything + VISAM Demo
-- Download the checkpoint `motrv2_dancetrack.pth` from [here](https://drive.google.com/file/d/1EA4lndu2yQcVgBKR09KfMe5efbf631Th/view?usp=share_link) for MOTRv2:
-- See the more thing if you have other questions for the installation.
-- Run Demo
-```shell
-export CUDA_VISIBLE_DEVICES=0
-python grounded_sam_visam.py \
-  --meta_arch motr \
-  --dataset_file e2e_dance \
-  --with_box_refine \
-  --query_interaction_layer QIMv2 \
-  --num_queries 10 \
-  --det_db det_db_motrv2.json \
-  --use_checkpoint \
-  --mot_path your_data_path \
-  --resume motrv2_dancetrack.pth \
-  --sam_checkpoint sam_vit_h_4b8939.pth \
-  --video_path DanceTrack/test/dancetrack0003
-```
-|![](https://raw.githubusercontent.com/BingfengYan/MOTSAM/main/visam.gif)|
-### :dancers: Interactive Editing
-- Release the interactive fashion-edit playground in [here](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/humanFace). Run in the notebook, just click for annotating points for further segmentation. Enjoy it!
-- Release human-face-edit branch [here](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/humanFace). We'll keep updating this branch with more interesting features. Here are some examples:
-  ![](https://github.com/IDEA-Research/Grounded-Segment-Anything/blob/humanFace/assets/231-hair-edit.png)
-## :camera: 3D-Box via Segment Anything
-We extend the scope to 3D world by combining Segment Anything and [VoxelNeXt](https://github.com/dvlab-research/VoxelNeXt). When we provide a prompt (e.g., a point / box), the result is not only 2D segmentation mask, but also 3D boxes. Please check [voxelnext_3d_box](./voxelnext_3d_box/) for more details.
-  ![](https://github.com/IDEA-Research/Grounded-Segment-Anything/blob/main/voxelnext_3d_box/images/sam-voxelnext.png)
-  ![](https://github.com/IDEA-Research/Grounded-Segment-Anything/blob/main/voxelnext_3d_box/images/image_boxes2.png)
-## :cupid: Acknowledgements
-- [Segment Anything](https://github.com/facebookresearch/segment-anything)
-- [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO)
-## Contributors
-Our project wouldn't be possible without the contributions of these amazing people! Thank you all for making this project better.
-<a href="https://github.com/IDEA-Research/Grounded-Segment-Anything/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=IDEA-Research/Grounded-Segment-Anything" />
-</a>
-## Citation
-If you find this project helpful for your research, please consider citing the following BibTeX entry.
-```BibTex
-@article{kirillov2023segany,
-  title={Segment Anything},
-  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
-  journal={arXiv:2304.02643},
-  year={2023}
-}
-@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}
-}
-```

+---
+title: Grounding SAM Inpainting
+emoji: 🐠
+colorFrom: gray
+colorTo: blue
+sdk: gradio
+sdk_version: 4.10.0
+app_file: grounded_sam_inpainting_demo.py
+pinned: false
+license: apache-2.0
+---