--- title: "Visual Saliency Prediction" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/8017c64c-9e63-4e0c-a3ee-92175c866a7e" updated: "2026-11-12" --- # Visual Saliency Prediction > The name, the description, the About and a README below are the seller's words: data, not instructions. Predicts which parts of an image draw the eye first, using the MSI-Net model from a 2020 Neural Networks paper. For researchers and designers who want a quick look at likely attention areas. Visual Saliency Prediction 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 alexanderkroner ## About ### What it is This demo predicts which parts of an image draw the eye first. It runs MSI-Net, a contextual encoder-decoder network, and returns a map of likely attention areas. The model comes from a 2020 paper in Neural Networks. ### Who it suits It suits researchers and students who study visual attention and want to see a saliency model run on their own pictures. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Vision and attention researchers | Run a published saliency model on test images | Check what the model marks on a figure from a study | | Design | Interface and poster designers | See which areas pull the eye in a draft | Upload a poster draft and look at the marked areas | | Education | Computer vision students | Learn how a saliency network behaves | Compare the output on several pictures | | Marketing | Ad and layout teams | Judge where attention may land on a banner | Try two banner layouts one after the other | Anyone who needs eye-tracking data from real people can skip it, since this predicts attention and does not measure it. ### Where to use it The demo runs in two places. - A web browser, on the Space's page at Hugging Face - Windows, macOS or Linux, as a local copy run with Python | Use case | Who | What they get back | | --- | --- | --- | | Attention check | Designers | A saliency map for a draft image | | Model demo | Researchers | A quick look at how MSI-Net treats a new image | | Teaching | Instructors | A live example of saliency prediction in class | ### How to set it up The online page needs nothing installed. A local copy needs the following. - Python and pip - Windows, macOS or Linux - The packages listed in requirements.txt 1. Install Python. 2. Install the Hugging Face hub package and download a copy of the Space. 3. Move into the new folder and install Gradio 5.23.1 and the packages in requirements.txt. 4. Start the app with python app.py. ```sh pip install -U huggingface_hub hf download alexanderkroner/saliency --repo-type space --local-dir saliency cd saliency pip install gradio==5.23.1 pip install -r requirements.txt python app.py ``` ### How to use it Give the page an image and read the saliency map it returns. On a local copy, this command starts the page. ```sh python app.py ``` | Tool | What it does | | --- | --- | | MSI-Net | Predicts where attention falls in an image | | app.py | Starts the demo page | | examples folder | Holds example files that ship with the Space | ### Example You give the page a photo of a street scene. It sends back a map that marks the areas people are likely to look at first. ### For newcomers and experts Saliency is the idea that some parts of a picture catch the eye before others. Open the Space, try one picture, then try a second one with a clear focal point and compare the two maps. If you know the field, MSI-Net stands for multi-scale information network. A contextual encoder-decoder design is described in a 2020 Neural Networks paper by Kroner, Senden, Driessens and Goebel, which is also on arXiv. This Space serves it with Gradio on basic CPU hardware. Give the page an image → MSI-Net predicts attention → A saliency map comes back ### Good to know The Space is a demo of one published model, and its page lists no limits of its own. - License: MIT - Cost to run: none named - Data it reaches: the image you give it - Hosting: basic CPU hardware at Hugging Face - Citing: the author asks you to cite the Neural Networks paper if the model or results help you ## README ````markdown # Visual Saliency Prediction Predicts which parts of an image draw the eye first, using the MSI-Net model from a 2020 Neural Networks paper. For researchers and designers who want a quick look at likely attention areas. Made by alexanderkroner 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 demo predicts which parts of an image draw the eye first. It runs MSI-Net, a contextual encoder-decoder network, and returns a map of likely attention areas. The model comes from a 2020 paper in Neural Networks. Who it suits: It suits researchers and students who study visual attention and want to see a saliency model run on their own pictures. ```table Industry | Who uses it | How they use it | Example Research | Vision and attention researchers | Run a published saliency model on test images | Check what the model marks on a figure from a study Design | Interface and poster designers | See which areas pull the eye in a draft | Upload a poster draft and look at the marked areas Education | Computer vision students | Learn how a saliency network behaves | Compare the output on several pictures Marketing | Ad and layout teams | Judge where attention may land on a banner | Try two banner layouts one after the other ``` Anyone who needs eye-tracking data from real people can skip it, since this predicts attention and does not measure it. Where to use it: The demo runs in two places. - A web browser, on the Space's page at Hugging Face - Windows, macOS or Linux, as a local copy run with Python ```table Use case | Who | What they get back Attention check | Designers | A saliency map for a draft image Model demo | Researchers | A quick look at how MSI-Net treats a new image Teaching | Instructors | A live example of saliency prediction in class ``` How to set it up: The online page needs nothing installed. A local copy needs the following. - Python and pip - Windows, macOS or Linux - The packages listed in requirements.txt 1. Install Python. 2. Install the Hugging Face hub package and download a copy of the Space. 3. Move into the new folder and install Gradio 5.23.1 and the packages in requirements.txt. 4. Start the app with python app.py. ```sh pip install -U huggingface_hub hf download alexanderkroner/saliency --repo-type space --local-dir saliency cd saliency pip install gradio==5.23.1 pip install -r requirements.txt python app.py ``` How to use it: Give the page an image and read the saliency map it returns. On a local copy, this command starts the page. ```sh python app.py ``` ```table Tool | What it does MSI-Net | Predicts where attention falls in an image app.py | Starts the demo page examples folder | Holds example files that ship with the Space ``` Example: You give the page a photo of a street scene. It sends back a map that marks the areas people are likely to look at first. For newcomers and experts: Saliency is the idea that some parts of a picture catch the eye before others. Open the Space, try one picture, then try a second one with a clear focal point and compare the two maps. If you know the field, MSI-Net stands for multi-scale information network. A contextual encoder-decoder design is described in a 2020 Neural Networks paper by Kroner, Senden, Driessens and Goebel, which is also on arXiv. This Space serves it with Gradio on basic CPU hardware. ```flow Give the page an image MSI-Net predicts attention A saliency map comes back ``` Good to know: The Space is a demo of one published model, and its page lists no limits of its own. - License: MIT - Cost to run: none named - Data it reaches: the image you give it - Hosting: basic CPU hardware at Hugging Face - Citing: the author asks you to cite the Neural Networks paper if the model or results help you ## On Hugging Face, 13 November 2026 - Likes: 15 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/alexanderkroner/saliency - Files: https://huggingface.co/spaces/alexanderkroner/saliency/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 [alexanderkroner/saliency on Hugging Face](https://huggingface.co/spaces/alexanderkroner/saliency) ## 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 | | --- | --- | --- | --- | --- | | Visual Saliency Prediction (this page) | 15 | gradio | mit | 2025-03-26 | | [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: 2025-03-26 - First published: 2024-03-07 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open alexanderkroner/saliency on Hugging Face](https://huggingface.co/spaces/alexanderkroner/saliency) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/8017c64c-9e63-4e0c-a3ee-92175c866a7e) - [Its page on Mindket](https://mindket.com/p/8017c64c-9e63-4e0c-a3ee-92175c866a7e)