Flowers Classification MobileViT

Flowers Classification MobileViT

Identifies flower types in a photo with a small MobileViT model from Keras in a Gradio page, with example images included. For machine learning learners and plant hobbyists.

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 app classifies flower pictures with a small MobileViT model. The record names one model, mobile-vit-xxs, from the Keras team's keras-io group. An examples folder holds sample pictures to try.

Who it suits: It suits machine learning learners who want to see a compact vision model at work. Plant hobbyists can get a quick label for a flower photo, and developers get a small Gradio example. The record gives no list of flower classes or accuracy figures.

Industry | Who uses it | How they use it | Example
Education | Machine learning students | See a compact vision transformer classify images | Run one of the example pictures
Gardening | Plant hobbyists | Get a quick guess at a flower's type | Try a photo of a flower you own
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 dependable plant identification service.

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
Classify a flower photo | Hobbyists | The model's label for the picture
Try the bundled examples | Learners | A quick sense of how the model behaves
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.

pip install -U huggingface_hub
hf download keras-io/Flowers-Classification-MobileViT --repo-type space --local-dir Flowers-Classification-MobileViT
cd Flowers-Classification-MobileViT
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 pictures are the quickest way to try it.

python app.py

Example: You open the Space and pick one of the example flower pictures. The app returns the model's label for that flower. The record shows no sample output, so the exact display depends on the page.

For newcomers and experts: An image classifier looks at a photo and picks the label that fits best. Start by opening the Space and running an example picture, then try one of your own.

If you know the field, MobileViT mixes convolution with transformer blocks to stay small, and the xxs in the model name points to its smallest size. The record gives no accuracy figures or class list, so check results on your own photos.

You pick or upload a flower photo
The Gradio page sends it to app.py
The MobileViT model scores the labels
The page shows the best match

Good to know:

• License: cc0-1, as listed on the Space
• Cost to run: none named
• Data it reaches: the pictures you give it and the model it names
• Not for: the record names no exclusions, and it gives no accuracy figures

On Hugging Face, 13 November 2026

• Likes: 4
• Built with: gradio
• License: cc0-1.0

Links

• Space: https://huggingface.co/spaces/keras-io/Flowers-Classification-MobileViT
• Files: https://huggingface.co/spaces/keras-io/Flowers-Classification-MobileViT/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.