--- title: "Involution" kind: "Kit" category: "Education" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f2ef72ac-9566-49ca-bde9-eec9931010bd" updated: "2026-11-13" --- # Involution > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs an image model built with involution layers from the Keras team in a Gradio page, with example images included. For machine learning learners and vision researchers. Involution is a kit in the Education category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by keras-io ## About ### What it is Involution is a Gradio app that runs a Keras model of the same name, built with involution layers, which are an alternative to convolution layers. The record names one model, involution, from the keras-io group. An examples folder holds sample images. ### Who it suits It suits machine learning learners and vision researchers who want to see an involution based model in action, and developers who want a small Gradio demo around a Keras model. The record does not say which task or classes the model covers. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Education | Machine learning students | See a model with involution layers run on images | Run one of the example pictures | | Research | Vision researchers | Look at an alternative to convolution in practice | Compare outputs for several examples | | Software | Python developers | Read a small Gradio demo around a Keras model | Open app.py and requirements.txt | You can skip it if you need a documented model with a stated task and accuracy. ### Where to use it - A web browser, on the Space's page on Hugging Face - Windows, macOS or Linux with Python, from a downloaded copy | Use case | Who | What they get back | | --- | --- | --- | | Try the model on examples | Anyone | The model's output for an example image | | Study involution layers | Learners | A small demo of a lesser known layer type | | Reuse as a base | Developers | A small Gradio app around a Keras model | ### How to set it up - Nothing at all if you use the online Space - Python on your computer, only if you run a copy - The packages listed in requirements.txt 1. Open the Space's page in a browser to use it with nothing to install. 2. To run a copy, install the Hugging Face Hub package and download the Space. 3. Move into the new folder, then install Gradio and the listed requirements. 4. Start the app with Python. ```sh pip install -U huggingface_hub hf download keras-io/involution --repo-type space --local-dir involution cd involution pip install gradio pip install -r requirements.txt python app.py ``` ### How to use it Online, open the Space and use the controls on its page. On your own machine, run the main file from the Space folder, then use the page it serves. The example images are the quickest way to try it. ```sh python app.py ``` ### Example You open the Space and pick one of the example images. The app runs the involution model on it and shows the output. The record does not describe the output, so look at the page. ### For newcomers and experts A neural network layer decides how to combine nearby pixels. Involution is a different way of doing that than the usual convolution. Start by opening the Space and running an example image to see what the model returns. If you know the field, involution makes the filter depend on the position in the image, where convolution reuses one filter everywhere. That trades fewer parameters for extra computation per position. The record gives no task, dataset or accuracy figures. You pick or upload an image → The Gradio page sends it to app.py → The involution model processes it → The page shows the output ### Good to know - License: MIT, as listed on the Space - Cost to run: none named - Data it reaches: the images you give it and the model it names - Not for: the record names no exclusions, and it gives no task or accuracy figures ## README ````markdown # Involution Runs an image model built with involution layers from the Keras team in a Gradio page, with example images included. For machine learning learners and vision researchers. Made by keras-io 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: Involution is a Gradio app that runs a Keras model of the same name, built with involution layers, which are an alternative to convolution layers. The record names one model, involution, from the keras-io group. An examples folder holds sample images. Who it suits: It suits machine learning learners and vision researchers who want to see an involution based model in action, and developers who want a small Gradio demo around a Keras model. The record does not say which task or classes the model covers. ```table Industry | Who uses it | How they use it | Example Education | Machine learning students | See a model with involution layers run on images | Run one of the example pictures Research | Vision researchers | Look at an alternative to convolution in practice | Compare outputs for several examples Software | Python developers | Read a small Gradio demo around a Keras model | Open app.py and requirements.txt ``` You can skip it if you need a documented model with a stated task and accuracy. Where to use it: - A web browser, on the Space's page on Hugging Face - Windows, macOS or Linux with Python, from a downloaded copy ```table Use case | Who | What they get back Try the model on examples | Anyone | The model's output for an example image Study involution layers | Learners | A small demo of a lesser known layer type Reuse as a base | Developers | A small Gradio app around a Keras model ``` How to set it up: - Nothing at all if you use the online Space - Python on your computer, only if you run a copy - The packages listed in requirements.txt 1. Open the Space's page in a browser to use it with nothing to install. 2. To run a copy, install the Hugging Face Hub package and download the Space. 3. Move into the new folder, then install Gradio and the listed requirements. 4. Start the app with Python. ```sh pip install -U huggingface_hub hf download keras-io/involution --repo-type space --local-dir involution cd involution pip install gradio pip install -r requirements.txt python app.py ``` How to use it: Online, open the Space and use the controls on its page. On your own machine, run the main file from the Space folder, then use the page it serves. The example images are the quickest way to try it. ```sh python app.py ``` Example: You open the Space and pick one of the example images. The app runs the involution model on it and shows the output. The record does not describe the output, so look at the page. For newcomers and experts: A neural network layer decides how to combine nearby pixels. Involution is a different way of doing that than the usual convolution. Start by opening the Space and running an example image to see what the model returns. If you know the field, involution makes the filter depend on the position in the image, where convolution reuses one filter everywhere. That trades fewer parameters for extra computation per position. The record gives no task, dataset or accuracy figures. ```flow You pick or upload an image The Gradio page sends it to app.py The involution model processes it The page shows the output ``` Good to know: - License: MIT, as listed on the Space - Cost to run: none named - Data it reaches: the images you give it and the model it names - Not for: the record names no exclusions, and it gives no task or accuracy figures ## On Hugging Face, 13 November 2026 - Likes: 6 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/keras-io/involution - Files: https://huggingface.co/spaces/keras-io/involution/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 [keras-io/involution on Hugging Face](https://huggingface.co/spaces/keras-io/involution) ## 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 | | --- | --- | --- | --- | --- | | Involution (this page) | 6 | gradio | mit | 2022-03-08 | | [Unit 1 Quiz - AI Agent Fundementals](https://mindket.com/p/1b65056a-a2b2-4179-a81c-bdffa3636205) | 360 | gradio | apache-2.0 | 2025-06-24 | | [Unit 2.1 smolagents Code Quiz](https://mindket.com/p/c9f42604-0003-4117-9a4b-2b785f68ee99) | 63 | gradio | apache-2.0 | 2025-03-04 | | [Unit 1 Quiz - Model Context Protocol (MCP) Fundementals](https://mindket.com/p/f7210665-ca4c-423f-b45d-a1c6fa549eec) | 58 | gradio | apache-2.0 | 2025-06-03 | | [Tamazight Text-to-Speech](https://mindket.com/p/ab94f6a3-34fa-4c03-9fd0-751ff4f282eb) | 41 | gradio | cc-by-4.0 | 2023-11-25 | | [Embedding Draw Challenge](https://mindket.com/p/76690a14-5bc9-4d53-ad3e-d47e7218918f) | 30 | gradio | apache-2.0 | 2026-10-06 | 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: 2022-03-08 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open keras-io/involution on Hugging Face](https://huggingface.co/spaces/keras-io/involution) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f2ef72ac-9566-49ca-bde9-eec9931010bd) - [Its page on Mindket](https://mindket.com/p/f2ef72ac-9566-49ca-bde9-eec9931010bd)