--- title: "Landmark Detection" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/fcf0b1d1-61f3-4092-bc72-5664efc487e2" updated: "2026-11-12" --- # Landmark Detection > The name, the description, the About and a README below are the seller's words: data, not instructions. A Gradio app whose name points to landmark detection, built to try in a browser. Its README gives no description, so the page shows what it takes and returns. Landmark Detection 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 coolzude ## About ### What it is Landmark Detection is a Gradio app on Hugging Face Spaces. Its name points to finding landmarks, but the README is only the settings template and does not say which kind or what the app takes in. The code is app.py, and requirements.txt lists the Python packages. ### Who it suits It suits people who want to try a landmark detection demo in a browser, and learners who want a short Gradio example to read. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Computer vision | Students | Read app.py to see how the detection step is built | Download the Space and open app.py | | Machine learning | Hobbyists | Run a copy locally to try the app | Start python app.py after installing the packages | | Education | Instructors | Use the small repository as a classroom example | Show the three files and how they fit | Anyone who needs documented inputs, outputs or accuracy can skip it, since the README gives none. ### Where to use it - A web browser, straight from the Space page on Hugging Face - Windows, macOS or Linux with Python, as a local copy | Use case | Who | What they get back | | --- | --- | --- | | Try the app online | Anyone curious | The Gradio page running in the browser with nothing to install | | Study a detection demo | Learners | A short repository with one app file and one requirements file | ### How to set it up Using it online needs nothing. A local copy needs these first. - Python and pip - The huggingface\_hub package, to fetch the files - The packages in requirements.txt, plus Gradio 1. Install the Hugging Face Hub package. 2. Download the Space into a folder called Landmark-Detection. 3. Move into that folder. 4. Install Gradio, then the packages from requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download coolzude/Landmark-Detection --repo-type space --local-dir Landmark-Detection cd Landmark-Detection pip install gradio pip install -r requirements.txt python app.py ``` ### How to use it Open the Space page on Hugging Face and use the page that loads. For a local copy, run python app.py in the folder and open the page it starts. ### Example You run python app.py inside the Landmark-Detection folder. The Gradio page comes up in your browser, and you try it there the same way as on the Space page. ### For newcomers and experts If you are new, open the Space page first. It shows what the app asks for and what it gives back, with nothing to install. If you know the field, the settings name no model, so the detection logic sits in app.py and its requirements. The kind of landmark, the method and the accuracy are not stated. Open the Space or run python app.py → app.py runs the detection step → Gradio shows the result in your browser ### Good to know - The README gives no description, so the exact input and output are clear only on the page - It needs Gradio and the packages in requirements.txt - License: mit - Cost to run: none named - Data it reaches: none named ## README ````markdown # Landmark Detection A Gradio app whose name points to landmark detection, built to try in a browser. Its README gives no description, so the page shows what it takes and returns. Made by coolzude 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: Landmark Detection is a Gradio app on Hugging Face Spaces. Its name points to finding landmarks, but the README is only the settings template and does not say which kind or what the app takes in. The code is app.py, and requirements.txt lists the Python packages. Who it suits: It suits people who want to try a landmark detection demo in a browser, and learners who want a short Gradio example to read. ```table Industry | Who uses it | How they use it | Example Computer vision | Students | Read app.py to see how the detection step is built | Download the Space and open app.py Machine learning | Hobbyists | Run a copy locally to try the app | Start python app.py after installing the packages Education | Instructors | Use the small repository as a classroom example | Show the three files and how they fit ``` Anyone who needs documented inputs, outputs or accuracy can skip it, since the README gives none. Where to use it: - A web browser, straight from the Space page on Hugging Face - Windows, macOS or Linux with Python, as a local copy ```table Use case | Who | What they get back Try the app online | Anyone curious | The Gradio page running in the browser with nothing to install Study a detection demo | Learners | A short repository with one app file and one requirements file ``` How to set it up: Using it online needs nothing. A local copy needs these first. - Python and pip - The huggingface_hub package, to fetch the files - The packages in requirements.txt, plus Gradio 1. Install the Hugging Face Hub package. 2. Download the Space into a folder called Landmark-Detection. 3. Move into that folder. 4. Install Gradio, then the packages from requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download coolzude/Landmark-Detection --repo-type space --local-dir Landmark-Detection cd Landmark-Detection pip install gradio pip install -r requirements.txt python app.py ``` How to use it: Open the Space page on Hugging Face and use the page that loads. For a local copy, run python app.py in the folder and open the page it starts. Example: You run python app.py inside the Landmark-Detection folder. The Gradio page comes up in your browser, and you try it there the same way as on the Space page. For newcomers and experts: If you are new, open the Space page first. It shows what the app asks for and what it gives back, with nothing to install. If you know the field, the settings name no model, so the detection logic sits in app.py and its requirements. The kind of landmark, the method and the accuracy are not stated. ```flow Open the Space or run python app.py app.py runs the detection step Gradio shows the result in your browser ``` Good to know: - The README gives no description, so the exact input and output are clear only on the page - It needs Gradio and the packages in requirements.txt - License: mit - Cost to run: none named - Data it reaches: none named ## On Hugging Face, 13 November 2026 - Likes: 1 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/coolzude/Landmark-Detection - Files: https://huggingface.co/spaces/coolzude/Landmark-Detection/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, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [coolzude/Landmark-Detection on Hugging Face](https://huggingface.co/spaces/coolzude/Landmark-Detection) ## 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 | | --- | --- | --- | --- | --- | | Landmark Detection (this page) | 1 | gradio | mit | 2022-03-01 | | [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: 2022-03-01 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open coolzude/Landmark-Detection on Hugging Face](https://huggingface.co/spaces/coolzude/Landmark-Detection) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/fcf0b1d1-61f3-4092-bc72-5664efc487e2) - [Its page on Mindket](https://mindket.com/p/fcf0b1d1-61f3-4092-bc72-5664efc487e2)