--- title: "OFA-Visual_Question_Answering" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/2f79212e-9467-474f-a93e-731aad25b4d2" updated: "2026-11-13" --- # OFA-Visual\_Question\_Answering > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. OFA-Visual\_Question\_Answering is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by OFA-Sys ## 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. ```sh 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. ```sh 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh 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. ```sh 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. ```flow 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. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [OFA-Sys/OFA-vqa on Hugging Face](https://huggingface.co/spaces/OFA-Sys/OFA-vqa) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | OFA-Visual\_Question\_Answering (this page) | 40 | gradio | apache-2.0 | 2023-03-28 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2023-03-28 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open OFA-Sys/OFA-vqa on Hugging Face](https://huggingface.co/spaces/OFA-Sys/OFA-vqa) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/2f79212e-9467-474f-a93e-731aad25b4d2) - [Its page on Mindket](https://mindket.com/p/2f79212e-9467-474f-a93e-731aad25b4d2)