Visual Saliency Prediction

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.

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.

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.

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

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.