Anime Image Labeller

Anime Image Labeller

A Gradio app that puts labels on anime pictures with trained model files, a label list and notebooks in its repository. It suits people who want to try image labelling in a browser.

Made by curt-tigges 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: Anime Image Labeller is a Gradio app on Hugging Face Spaces that puts labels on anime pictures. The repository holds two trained model files, a classes.txt list of labels, an inference script, two notebooks and a test folder. The README gives no description beyond its settings.

Who it suits: It suits people who want to try labelling anime images in a browser, and learners who want to see how a trained image model, a label list and notebooks fit in one project.

Industry | Who uses it | How they use it | Example
Art and illustration | Hobbyists | Try the labeller on a piece of anime art | Open the Space and give it a picture
Machine learning | Students | Read the inference script and notebooks | Download the Space and open the notebooks
Content tagging | Developers | Study a small image tagging project | Read classes.txt to see the label set

Anyone who needs documented accuracy or the training data can skip it, since the README gives neither.

Where to use it:

• A web browser, straight from the Space page on Hugging Face
• Windows, macOS or Linux with Python, as a local copy
• A Jupyter environment, to open the two notebooks

Use case | Who | What they get back
Label an anime picture | Anyone curious | Labels for the picture, shown in the browser
Study an image labelling project | Learners | Model files, a label list, a script and two notebooks

How to set it up: Using it online needs nothing. A local copy needs these first.

• Python and pip
• The huggingface_hub package, to fetch the files
• The packages in requirements.txt, plus Gradio

1. Install the Hugging Face Hub package.
2. Download the Space into a folder called anime-image-labeller.
3. Move into that folder.
4. Install Gradio, then the packages from requirements.txt.
5. Start the app.

pip install -U huggingface_hub
hf download curt-tigges/anime-image-labeller --repo-type space --local-dir anime-image-labeller
cd anime-image-labeller
pip install gradio
pip install -r requirements.txt
python app.py

How to use it: Open the Space page on Hugging Face and give it a picture. For a local copy, run python app.py in the folder and open the page it starts.

Example: You give the app an anime picture on the Space page. It runs its trained model on it and shows the labels it assigns to the picture.

For newcomers and experts: If you are new, open the Space page and try one picture. Labelling means the model picks words from a fixed list that describe what is in the image.

If you know the field, the repository holds a large model trained for 40 epochs and a basic one trained for 10, going by their file names. The source does not describe the architecture, the data or the label meanings.

You give the app an anime picture
The inference script runs a trained model
The assigned labels show on the page

Good to know:

• The README gives no description, so the label set and quality are clear only from the page and classes.txt
• The repository is large, with 37 files, including notebooks and tests
• License: apache-2
• Cost to run: none named
• Data it reaches: the pictures you give it and the model files in its own repository

On Hugging Face, 13 November 2026

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

Links

• Space: https://huggingface.co/spaces/curt-tigges/anime-image-labeller
• Files: https://huggingface.co/spaces/curt-tigges/anime-image-labeller/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.