OFA-Visual_Question_Answering

OFA-Visual_Question_Answering

Answers questions about a picture with OFA in a Gradio page, with example pictures included and the full OFA code in the repository. For researchers and developers who want to try it or read the training code.

Made by OFA-Sys 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 from OFA-Sys answers questions about pictures, which is what visual question answering means. The repository carries the wider OFA code too, with folders such as fairseq, models, tasks and ofa_module, plus training and evaluation scripts. It names one model, openai-community/gpt2. You use it in a browser, or you run a copy on your own computer.

Who it suits: It suits researchers and developers who want to ask a model about a picture, and to read or reuse the OFA code behind it.

Industry | Who uses it | How they use it | Example
Research | Multimodal researchers | Try a vision and language model on their own pictures | Open the Space and use an example picture
Education | Machine learning students | See what visual question answering looks like | Ask a question about the cat picture
Software | Python developers | Read how app.py and the OFA modules fit together | Download the Space and open app.py

Skip it if you need a light install, because the repository holds 1087 files and a local copy is large.

Where to use it:

• A web browser: go to the Space on Hugging Face and try it with no install
• Windows, macOS or Linux: run a copy with Python

Use case | Who | What they get back
Ask about a picture | Students | An answer to a question about the image
Try your own images | Researchers | A quick look at how the model handles them
Read the OFA code | Developers | Training, evaluation and model code in one repository

How to set it up: You need a few things in place first.

• Python with pip, on Windows, macOS or Linux
• An internet connection to fetch the files from Hugging Face
• Disk space for a large repository, and likely for model files on the first run

1. Install the Hugging Face hub package.
2. Download the Space into a local folder.
3. Move into that folder.
4. Install Gradio and the packages listed in requirements.txt.
5. Start the app.

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

How to use it: Online, use the Gradio interface the Space page shows. On your own computer, run the main file from the project folder and open the page it serves in your browser.

python app.py

Example: You run python app.py inside the project folder, open the page and give it a picture such as the labrador one from the repository, together with a question about it. The page returns the model's answer.

For newcomers and experts: If this is new to you, start online. Go to the Space, pick an example picture and ask a short question about it before you install anything.

If you know the field, note that the repository is the OFA code base with a Gradio front end. It includes fairseq, criterions, tasks, run_scripts, train.py and evaluate.py, plus notes on checkpoints and datasets. The source gives no scores, so see its own notes for them.

Open the Space or run app.py
The Gradio page loads
You give a picture and a question
The OFA model reads both
The answer appears on the page

Good to know: The source gives no accuracy figures, so test the answers on your own pictures before you rely on them.

• License: apache-2
• Cost to run: none named, no API key, account or paid service
• Data it reaches: the pictures and questions you give it, the files in the repository and the model it names

On Hugging Face, 13 November 2026

• Likes: 40
• Built with: gradio
• License: apache-2.0

Links

• Space: https://huggingface.co/spaces/OFA-Sys/OFA-vqa
• Files: https://huggingface.co/spaces/OFA-Sys/OFA-vqa/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.