--- title: "UNet For Crack Detection In Concrete" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/03019a4c-1e5b-4867-b968-c495b21a95d6" updated: "2026-11-12" --- # UNet For Crack Detection In Concrete > The name, the description, the About and a README below are the seller's words: data, not instructions. Segments cracks in a photo of concrete with a U-Net model: you upload an image of a cracked surface and the demo marks the cracks. For engineers, inspectors and students. UNet For Crack Detection In Concrete is a kit in the Research 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 Vizuara ## About ### What it is This Space runs a U-Net model that finds cracks in pictures of concrete. You upload an image of a cracked surface and the app segments it, which means it marks the pixels that belong to the cracks. The model weights ship inside the Space. ### Who it suits Site engineers, inspectors and students who want to see crack segmentation on a photo without setting up a model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Construction | Site engineers | Mark cracks in a photo of concrete | Upload a photo of a cracked wall | | Infrastructure | Inspectors | Look over inspection photos for cracks | Upload a picture of a damaged surface | | Education | Students | See how U-Net segmentation works on a real image | Upload an image and view the marked result | People who need a structural safety verdict can skip it, since it segments an image and does not judge a building. ### Where to use it - A browser: the Space runs on Hugging Face with nothing to install - Windows, macOS or Linux: run a copy of the Space with Python | Use case | Who | What they get back | | --- | --- | --- | | Mark cracks | Engineers | A segmentation of the cracks in the photo | | Check inspection photos | Inspectors | A marked image for each upload | | Learn segmentation | Students | A working U-Net example with its model code | ### How to set it up - Python with pip on Windows, macOS or Linux - The Python packages listed in requirements.txt - Nothing at all if you only use the Space in a browser 1. Install the Hugging Face hub package. 2. Download the Space's files into a folder. 3. Move into that folder. 4. Install Gradio 5.43.1 and the packages from requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download Vizuara/UNet-for-crack-detection-in-concrete --repo-type space --local-dir UNet-for-crack-detection-in-concrete cd UNet-for-crack-detection-in-concrete pip install gradio==5.43.1 pip install -r requirements.txt python app.py ``` ### How to use it Open the Space page in a browser, or start your own copy with the command below. Then upload an image of cracked concrete to get its segmentation. ```sh python app.py ``` ### Example You upload a photo of a cracked concrete wall. The app runs the U-Net model on it and returns a segmentation that shows where the cracks are. ### For newcomers and experts If you are new, open the Space in a browser and upload one clear photo of a cracked surface. Nothing needs installing, and the result shows what the model picked out. For experts, the repository holds the model definition in unet\_model.py and a weights file named unet\_weights\_v2.pth, with app.py as the Gradio front end. The Space also names the Vizuara/unet-crack-segmentation model. You upload a concrete photo → Gradio passes it to app.py → The U-Net model segments the cracks → You see the segmentation ### Good to know - License: mit - Cost to run: none named - Data it reaches: the image you upload, and the Vizuara/unet-crack-segmentation model it names - Not for: none stated in the README ## README ````markdown # UNet For Crack Detection In Concrete Segments cracks in a photo of concrete with a U-Net model: you upload an image of a cracked surface and the demo marks the cracks. For engineers, inspectors and students. Made by Vizuara 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 runs a U-Net model that finds cracks in pictures of concrete. You upload an image of a cracked surface and the app segments it, which means it marks the pixels that belong to the cracks. The model weights ship inside the Space. Who it suits: Site engineers, inspectors and students who want to see crack segmentation on a photo without setting up a model. ```table Industry | Who uses it | How they use it | Example Construction | Site engineers | Mark cracks in a photo of concrete | Upload a photo of a cracked wall Infrastructure | Inspectors | Look over inspection photos for cracks | Upload a picture of a damaged surface Education | Students | See how U-Net segmentation works on a real image | Upload an image and view the marked result ``` People who need a structural safety verdict can skip it, since it segments an image and does not judge a building. Where to use it: - A browser: the Space runs on Hugging Face with nothing to install - Windows, macOS or Linux: run a copy of the Space with Python ```table Use case | Who | What they get back Mark cracks | Engineers | A segmentation of the cracks in the photo Check inspection photos | Inspectors | A marked image for each upload Learn segmentation | Students | A working U-Net example with its model code ``` How to set it up: - Python with pip on Windows, macOS or Linux - The Python packages listed in requirements.txt - Nothing at all if you only use the Space in a browser 1. Install the Hugging Face hub package. 2. Download the Space's files into a folder. 3. Move into that folder. 4. Install Gradio 5.43.1 and the packages from requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download Vizuara/UNet-for-crack-detection-in-concrete --repo-type space --local-dir UNet-for-crack-detection-in-concrete cd UNet-for-crack-detection-in-concrete pip install gradio==5.43.1 pip install -r requirements.txt python app.py ``` How to use it: Open the Space page in a browser, or start your own copy with the command below. Then upload an image of cracked concrete to get its segmentation. ```sh python app.py ``` Example: You upload a photo of a cracked concrete wall. The app runs the U-Net model on it and returns a segmentation that shows where the cracks are. For newcomers and experts: If you are new, open the Space in a browser and upload one clear photo of a cracked surface. Nothing needs installing, and the result shows what the model picked out. For experts, the repository holds the model definition in unet_model.py and a weights file named unet_weights_v2.pth, with app.py as the Gradio front end. The Space also names the Vizuara/unet-crack-segmentation model. ```flow You upload a concrete photo Gradio passes it to app.py The U-Net model segments the cracks You see the segmentation ``` Good to know: - License: mit - Cost to run: none named - Data it reaches: the image you upload, and the Vizuara/unet-crack-segmentation model it names - Not for: none stated in the README ## On Hugging Face, 13 November 2026 - Likes: 1 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/Vizuara/UNet-for-crack-detection-in-concrete - Files: https://huggingface.co/spaces/Vizuara/UNet-for-crack-detection-in-concrete/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 [Vizuara/UNet-for-crack-detection-in-concrete on Hugging Face](https://huggingface.co/spaces/Vizuara/UNet-for-crack-detection-in-concrete) ## 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 | | --- | --- | --- | --- | --- | | UNet For Crack Detection In Concrete (this page) | 1 | gradio | mit | 2025-08-21 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2025-08-21 - First published: 2025-08-21 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Vizuara/UNet-for-crack-detection-in-concrete on Hugging Face](https://huggingface.co/spaces/Vizuara/UNet-for-crack-detection-in-concrete) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/03019a4c-1e5b-4867-b968-c495b21a95d6) - [Its page on Mindket](https://mindket.com/p/03019a4c-1e5b-4867-b968-c495b21a95d6)