ViTGPT2

ViTGPT2

A Gradio app from gagan3012 that, going by its model names, describes a picture in words by pairing a ViT image encoder with a GPT-2 text decoder. For people who want to try image captioning in a browser.

Made by gagan3012 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: ViTGPT2 is a Gradio app from gagan3012. The Space names two ViTGPT2 models, plus Google's ViT base and OpenAI's GPT-2. Going by those names, it describes a picture in words by pairing a vision transformer with a GPT-2 text model. The repository includes four sample pictures. The README is empty.

Who it suits: It suits students and developers who want to see image captioning work on a few photos, and hobbyists who like small vision and language demos.

Industry | Who uses it | How they use it | Example
Research | ML researchers | Try a ViT and GPT-2 captioning pair | Run a sample picture and read the text
Accessibility | App builders | See what automatic picture descriptions look like | Check how a plain photo is described
Education | Students | Study a vision and language model in a web app | Compare descriptions of the four sample pictures

You can skip it if you need captions you can rely on, since the source gives no accuracy figures.

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
Caption a picture | Anyone | A short text description of the image, judging by the model names
Try the samples | Newcomers | Quick results on the avocado, banana, dice and duck pictures
Study the pair | Developers | A look at how an image model feeds a text 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

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 gagan3012/ViTGPT2 --repo-type space --local-dir ViTGPT2
cd ViTGPT2
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 four sample pictures you can try first. 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 the duck sample picture. You should get a short text describing the image, though the README does not show the exact output.

For newcomers and experts: If you are new, run a sample picture first and read the text it returns. Then try a photo of your own with one clear subject.

If you know the field, the Space lists google vit-base-patch16-224-in21k and openai gpt2 as the base models, with two fine-tuned ViTGPT2 checkpoints from the same author on top. That is the usual encoder-decoder captioning setup, but the README gives no training data or metrics.

You give the app a picture
The ViT model encodes the image
The GPT-2 model writes the text
The page shows the description

Good to know:

• License: mit
• Cost to run: none named. The online Space runs on Hugging Face.
• Data it reaches: the pictures you give it, and the models it names on Hugging Face.
• Limits: no accuracy figures or caption style details are given.

On Hugging Face, 13 November 2026

• Likes: 6
• Built with: gradio
• License: mit

Links

• Space: https://huggingface.co/spaces/gagan3012/ViTGPT2
• Files: https://huggingface.co/spaces/gagan3012/ViTGPT2/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.