--- title: "Grounding DINO Demo" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/9be45cb7-f824-40f4-b457-fc5dbd190618" updated: "2026-11-12" --- # Grounding DINO Demo > The name, the description, the About and a README below are the seller's words: data, not instructions. Finds objects in a picture from a text description, with no fixed list of labels. A Gradio demo of the Grounding DINO open-set detector, for developers and vision researchers. Grounding DINO Demo is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by merve ## About ### What it is This Space is a demo of Grounding DINO, an open-set object detector. Instead of choosing from a fixed list of classes, you describe what to find in words and the model looks for it in an image. The README presents it as the official PyTorch implementation and says it can run without a GPU. ### Who it suits It suits developers and researchers who want to try language-guided object detection, and people who need to locate things in pictures without training a detector first. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Computer vision research | Researchers | Try open-set detection with a plain text phrase | A researcher types a phrase and checks what the model finds | | Software | Developers | Test language-guided detection before building it into a product | A developer runs the demo on a few sample images | | Data labeling | Annotation teams | Locate objects by description to speed up labeling | A team tests whether a phrase finds the objects they need | | Education | Students | See how a detector can follow words instead of fixed classes | A student tries different phrases on one picture | You can skip it if you need to train a detector, since the README lists the training code as not yet released. ### Where to use it You use it in these places. - A web browser, on the Space's page on Hugging Face - A local copy on Windows, macOS or Linux, started with app.py - A command line script in the repository, which also has a CPU-only mode | Use case | Who | What they get back | | --- | --- | --- | | Find objects by phrase | Anyone | Detections for the objects your phrase describes | | Run without a GPU | Developers | The same demo in CPU-only mode | | Script a detection | Developers | Results written to the output location you pass to the script | ### How to set it up Online use needs only a browser. A local copy needs Python and the packages the repository lists. - A web browser, for the online page - Python with pip, for a local copy - Python packages from requirements.txt - CUDA\_HOME set, if you have a CUDA environment. Without CUDA the code builds in CPU-only mode. 1. Open the Space's page on Hugging Face to use it online, with nothing to install. 2. For a local copy, install the Hugging Face Hub package. 3. Download the Space's files into a folder and move into it. 4. Install Gradio 3.23.0 and the packages in requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download merve/Grounding_DINO_demo --repo-type space --local-dir Grounding_DINO_demo cd Grounding_DINO_demo pip install gradio==3.23.0 pip install -r requirements.txt python app.py ``` The README also gives this install line for the project itself. ```bash pip install -e . ``` ### How to use it Open the Space's page, give it an image and a text phrase naming what to find, and read the detections. The README's own demo uses the phrase cat ear with a trailing period. The command line script takes a config, a checkpoint, an image, an output location and the phrase. ```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 ``` | Tool | What it does | | --- | --- | | -c | The path to the config file | | -p | The path to the checkpoint file | | -i | The input image | | -o | Where the output goes | | -t | The text phrase to look for | | --cpu-only | Runs on the CPU, for machines without a GPU | ### Example You pass a cat photo with the phrase cat ear. and the output location outputs/0 to the script. The model looks for the ears in the picture, and the results go to the output location. ### For newcomers and experts Most object detectors only know the classes they were trained on. This one reads a short phrase, so you can ask for something specific. Try the online page first with a simple phrase and one picture. Under the hood, the README ties the model to DINO and GLIP and lists a Swin-T backbone for the released checkpoint. The record also names the BERT and RoBERTa text models. The README plans demos that pair it with Stable Diffusion and GLIGEN, but those are not yet released. You give an image and a text phrase → The model matches the phrase to objects → Matching objects are returned → CPU-only mode runs without a GPU ### Good to know The README reports these detection scores, and its numbers differ between the highlight and the checkpoint listing. Detection scores reported on COCO | Label | Value | | --- | --- | | Highlight, zero-shot | 52.5 AP | | Highlight, fine-tuned | 63.0 AP | | GroundingDINO-T, zero-shot | 48.4 AP | | GroundingDINO-T, fine-tuned | 57.2 AP | - The highlight says the zero-shot score came from training without COCO data. - License: apache-2. - Cost to run: none named. The Space uses cpu-basic hardware at Hugging Face. - Data it reaches: the images you give it and the model files the record names, which come from Hugging Face. - Not for: training a detector, since the README lists the training code as not yet released. ## README ````markdown # Grounding DINO Demo Finds objects in a picture from a text description, with no fixed list of labels. A Gradio demo of the Grounding DINO open-set detector, for developers and vision researchers. Made by merve on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This Space is a demo of Grounding DINO, an open-set object detector. Instead of choosing from a fixed list of classes, you describe what to find in words and the model looks for it in an image. The README presents it as the official PyTorch implementation and says it can run without a GPU. Who it suits: It suits developers and researchers who want to try language-guided object detection, and people who need to locate things in pictures without training a detector first. ```table Industry | Who uses it | How they use it | Example Computer vision research | Researchers | Try open-set detection with a plain text phrase | A researcher types a phrase and checks what the model finds Software | Developers | Test language-guided detection before building it into a product | A developer runs the demo on a few sample images Data labeling | Annotation teams | Locate objects by description to speed up labeling | A team tests whether a phrase finds the objects they need Education | Students | See how a detector can follow words instead of fixed classes | A student tries different phrases on one picture ``` You can skip it if you need to train a detector, since the README lists the training code as not yet released. Where to use it: You use it in these places. - A web browser, on the Space's page on Hugging Face - A local copy on Windows, macOS or Linux, started with app.py - A command line script in the repository, which also has a CPU-only mode ```table Use case | Who | What they get back Find objects by phrase | Anyone | Detections for the objects your phrase describes Run without a GPU | Developers | The same demo in CPU-only mode Script a detection | Developers | Results written to the output location you pass to the script ``` How to set it up: Online use needs only a browser. A local copy needs Python and the packages the repository lists. - A web browser, for the online page - Python with pip, for a local copy - Python packages from requirements.txt - CUDA_HOME set, if you have a CUDA environment. Without CUDA the code builds in CPU-only mode. 1. Open the Space's page on Hugging Face to use it online, with nothing to install. 2. For a local copy, install the Hugging Face Hub package. 3. Download the Space's files into a folder and move into it. 4. Install Gradio 3.23.0 and the packages in requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download merve/Grounding_DINO_demo --repo-type space --local-dir Grounding_DINO_demo cd Grounding_DINO_demo pip install gradio==3.23.0 pip install -r requirements.txt python app.py ``` The README also gives this install line for the project itself. ```bash pip install -e . ``` How to use it: Open the Space's page, give it an image and a text phrase naming what to find, and read the detections. The README's own demo uses the phrase cat ear with a trailing period. The command line script takes a config, a checkpoint, an image, an output location and the phrase. ```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 ``` ```table Tool | What it does -c | The path to the config file -p | The path to the checkpoint file -i | The input image -o | Where the output goes -t | The text phrase to look for --cpu-only | Runs on the CPU, for machines without a GPU ``` Example: You pass a cat photo with the phrase cat ear. and the output location outputs/0 to the script. The model looks for the ears in the picture, and the results go to the output location. For newcomers and experts: Most object detectors only know the classes they were trained on. This one reads a short phrase, so you can ask for something specific. Try the online page first with a simple phrase and one picture. Under the hood, the README ties the model to DINO and GLIP and lists a Swin-T backbone for the released checkpoint. The record also names the BERT and RoBERTa text models. The README plans demos that pair it with Stable Diffusion and GLIGEN, but those are not yet released. ```flow You give an image and a text phrase The model matches the phrase to objects Matching objects are returned CPU-only mode runs without a GPU ``` Good to know: The README reports these detection scores, and its numbers differ between the highlight and the checkpoint listing. ```chart Title: Detection scores reported on COCO Highlight, zero-shot: 52.5 AP Highlight, fine-tuned: 63.0 AP GroundingDINO-T, zero-shot: 48.4 AP GroundingDINO-T, fine-tuned: 57.2 AP ``` - The highlight says the zero-shot score came from training without COCO data. - License: apache-2. - Cost to run: none named. The Space uses cpu-basic hardware at Hugging Face. - Data it reaches: the images you give it and the model files the record names, which come from Hugging Face. - Not for: training a detector, since the README lists the training code as not yet released. ## On Hugging Face, 13 November 2026 - Likes: 126 - Built with: gradio - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/merve/Grounding_DINO_demo - Files: https://huggingface.co/spaces/merve/Grounding_DINO_demo/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [merve/Grounding_DINO_demo on Hugging Face](https://huggingface.co/spaces/merve/Grounding_DINO_demo) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Grounding DINO Demo (this page) | 126 | gradio | apache-2.0 | 2024-02-28 | | [FLUX.1 dev](https://mindket.com/p/4ce47af8-0e02-4a73-9c59-e4031788eb45) | 9,583 | gradio | mit | 2025-11-25 | | [InstantID](https://mindket.com/p/54fcb36c-756a-4797-80c1-05779f87ebbd) | 3,659 | gradio | apache-2.0 | 2025-08-26 | | [Chat With Janus-Pro-7B](https://mindket.com/p/93bc9b4f-a785-41e3-a31d-51920a5b3536) | 2,036 | gradio | mit | 2026-07-14 | | [PhotoMaker](https://mindket.com/p/b3e336b4-4a67-4e52-8c2e-1e94d392ac71) | 1,960 | gradio | apache-2.0 | 2025-08-28 | | [Z Image Turbo](https://mindket.com/p/adfd87b7-3f3e-405e-95c8-1957bc0da52a) | 1,940 | gradio | apache-2.0 | 2026-05-22 | Numbers as Mindket last read them, on 2026-11-12. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2024-02-28 - First published: 2023-09-29 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open merve/Grounding_DINO_demo on Hugging Face](https://huggingface.co/spaces/merve/Grounding_DINO_demo) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/9be45cb7-f824-40f4-b457-fc5dbd190618) - [Its page on Mindket](https://mindket.com/p/9be45cb7-f824-40f4-b457-fc5dbd190618)