Icevision_fridge_tutorial

Icevision_fridge_tutorial

A small Gradio demo from dnth that, going by its name, accompanies an IceVision fridge tutorial, with a saved checkpoint and three sample pictures. For learners exploring a computer vision tutorial.

Made by dnth 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 is a small demo from dnth that, going by its name, goes with an IceVision fridge tutorial. It runs an image model inside a Gradio app. The repository holds a saved model checkpoint and three sample pictures. The README carries only the standard Space settings text, so what the model finds is not described.

Who it suits: It suits learners who are working through a computer vision tutorial and want to see a finished demo of one.

Industry | Who uses it | How they use it | Example
Education | Students | See what a tutorial project looks like when deployed | Open the demo and try a sample picture
Machine learning | Practitioners | Study how a checkpoint is wrapped in a Gradio app | Read app.py next to the checkpoint file
Research | Vision hobbyists | Try a small image model | Run one of the three sample pictures

You can skip it if you want a documented, general vision tool, since this is a tutorial demo.

Where to use it:

• A web browser, on the Space's page at Hugging Face
• Your own computer, on Windows, macOS or Linux

Use case | Who | What they get back
Try the demo | Learners | The model's output for a sample picture
Study the app | Students | A small Gradio wrapper around a saved model

How to set it up:

• Nothing to install if you use the Space online
• Python, if you run a copy on your own computer
• The Python packages in requirements.txt
• The system packages in packages.txt, which Hugging Face installs with apt-get

1. Open the Space's page on Hugging Face to use it online.
2. To run a copy, install huggingface_hub and download the Space into a folder.
3. Move into that folder and install Gradio, then the listed packages.
4. Start the app with Python.

pip install -U huggingface_hub
hf download dnth/icevision_fridge_tutorial --repo-type space --local-dir icevision_fridge_tutorial
cd icevision_fridge_tutorial
pip install gradio
pip install -r requirements.txt
python app.py

How to use it: Open the Space and give it a picture. The repository includes three sample pictures you can try. The README gives no steps, so follow the page's own controls. On your own copy, start the app with this command and open the local page Gradio prints.

python app.py

Example: Open the Space online and run one of the three sample pictures from the repository. You get back the model's result for that picture, though the README does not say what form it takes.

For newcomers and experts: If you are new, run a sample picture first and look at what the app returns. Then try your own photo to see where the model stops working.

If you know the field, the repository holds app.py, a saved checkpoint file and three images, so the app loads a trained model and runs it on a picture. The README gives no architecture, dataset or accuracy details.

You give the app a picture
The app loads the saved checkpoint
The model processes the image
The page shows the result

Good to know:

• License: afl-3
• Cost to run: none named. The online Space runs on Hugging Face.
• Data it reaches: the pictures you give it, and the checkpoint file in its repository.

On Hugging Face, 13 November 2026

• Likes: 1
• Built with: gradio
• License: afl-3.0

Links

• Space: https://huggingface.co/spaces/dnth/icevision_fridge_tutorial
• Files: https://huggingface.co/spaces/dnth/icevision_fridge_tutorial/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.