Image Caption With Vit Gpt2

Image Caption With Vit Gpt2

A Gradio app that writes a short caption for a picture, pairing a vision transformer with a GPT-2 style text model. It suits people who want to try image captioning in a browser.

Made by Amrrs 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: Image Caption With Vit Gpt2 is a Gradio app on Hugging Face Spaces that writes a short caption for a picture. It pairs a vision transformer with a GPT-2 style language model, and it names three models, including google/vit-base-patch16-224-in21k and sachin/vit2distilgpt2. The repository also holds one sample photo.

Who it suits: It suits people who want to try automatic image captioning in a browser, and learners who want a short example of an image model feeding a text model.

Industry | Who uses it | How they use it | Example
Accessibility | Content editors | Try a first draft of picture descriptions | Open the Space and give it a photo
Machine learning | Students | Read app.py to see how an image encoder and a text decoder fit | Download the Space and open app.py
Education | Instructors | Show image captioning in a web page | Click the sample photo in class

Anyone who needs production-grade captions or measured quality can skip it, since the source gives none.

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

Use case | Who | What they get back
Caption a photo | Anyone curious | A short text description of the picture
Study an image-to-text demo | Learners | An app file, a requirements file and one sample photo

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 image-caption-with-vit-gpt2.
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 Amrrs/image-caption-with-vit-gpt2 --repo-type space --local-dir image-caption-with-vit-gpt2
cd image-caption-with-vit-gpt2
pip install gradio
pip install -r requirements.txt
python app.py

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

Example: You upload a photo of a dog on a beach. The app passes it through the vision model and the text model and shows a short sentence describing the picture.

For newcomers and experts: If you are new, click the sample photo on the Space page first. Image captioning means a program looks at a picture and writes a sentence about it.

If you know the field, the named checkpoints are a ViT encoder, a vit2distilgpt2 model and a DistilGPT-2 decoder. The source does not give training data or quality figures, so judge the captions on your own images.

You give the app a picture
The ViT encoder reads the image
The GPT-2 style decoder writes a caption

Good to know:

• Captions are generated by the named models, so check them before you rely on them
• It needs Gradio and the packages in requirements.txt
• License: mit
• Cost to run: none named
• Data it reaches: the pictures you give it and the three models it names

On Hugging Face, 13 November 2026

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

Links

• Space: https://huggingface.co/spaces/Amrrs/image-caption-with-vit-gpt2
• Files: https://huggingface.co/spaces/Amrrs/image-caption-with-vit-gpt2/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.