Video Vision Transformer for CT

Video Vision Transformer for CT

Lets you try a video vision transformer on CT-style data in a Gradio page, with two example videos included. Aimed at students and developers curious about video models.

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: This Gradio demo from keras-io wraps one model, keras-io/video-vision-transformer. The CT in the name suggests CT scan data, and the repository holds two example videos. You use it in a browser, or you run a copy on your own computer.

Who it suits: It suits students and developers who want to see a video transformer at work before they read model code. Teachers can show it as a live demo.

Industry | Who uses it | How they use it | Example
Education | Deep learning students | Try a video model without writing code | Open the Space and play one of the example videos
Research | Machine learning researchers | See how a finished model is wired into a demo | Read app.py and the utils folder
Software | Python developers | Run a copy locally and adapt it | Download the Space and start app.py

Skip it if you need a training script or a clinical tool, because the source makes no medical claim.

Where to use it:

• A web browser: go to the Space on Hugging Face and try it with no install
• Windows, macOS or Linux: run a copy with Python

Use case | Who | What they get back
Try the model | Students | The model's answer on the page for the video they pick
Read the code | Developers | The app.py file and utils folder behind the demo
Teach with it | Instructors | A demo with two example videos for a class

How to set it up: You need a few things in place first.

• Python with pip, on Windows, macOS or Linux
• An internet connection to fetch the files from Hugging Face

1. Install the Hugging Face hub package.
2. Download the Space into a local folder.
3. Move into that folder.
4. Install Gradio 3.6 and the packages listed in requirements.txt.
5. Start the app.

pip install -U huggingface_hub
hf download keras-io/video-vision-transformer-CT --repo-type space --local-dir video-vision-transformer-CT
cd video-vision-transformer-CT
pip install gradio==3.6
pip install -r requirements.txt
python app.py

How to use it: Online, use the Gradio interface the Space page shows. On your own computer, run the main file from the project folder and open the page it serves in your browser.

python app.py

Example: You run python app.py inside the project folder. The Gradio page starts, you give it a video such as example_1.mp4 from the repository, and the page shows what the model returns for it.

For newcomers and experts: If this is new to you, start online. Go to the Space and try an example video before you install anything. It is the quickest way to see a video transformer working.

If you know the field, note that the Space names a single model and app.py loads it, with helper code in a utils folder. It pins Gradio 3.6, an older release, so a fresh environment may need that exact version. The source gives no accuracy figures.

Open the Space or run app.py
The Gradio page loads
You give it a video
The named model scores it
The result appears on the page

Good to know: The source gives no accuracy numbers or training details, and it makes no clinical claim, so do not use the demo for medical decisions.

• License: apache-2
• Cost to run: none named, no API key, account or paid service
• Data it reaches: the files in the repository and the model it names, with nothing else listed

On Hugging Face, 13 November 2026

• Likes: 1
• Built with: gradio
• License: apache-2.0

Links

• Space: https://huggingface.co/spaces/keras-io/video-vision-transformer-CT
• Files: https://huggingface.co/spaces/keras-io/video-vision-transformer-CT/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.