VisualSense QA is a visual question answering (VQA) application built using Streamlit and ViLT (Vision-and-Language Transformer). It allows users to ask questions about images, and the model provides answers based on the content of the images.
- Visual Question Answering (VQA): Users can upload images and ask questions about them. The model processes the image and generates answers to the questions asked.
- Interactive Interface: The Streamlit interface provides an intuitive and user-friendly experience for interacting with the VQA model.
- ViLT Integration: ViLT (Vision-and-Language Transformer) powers the VQA model, allowing it to understand both visual and textual inputs.
To run VisualSense QA locally, follow these steps:
-
Clone the repository:
git clone https://github.com/vethan123/visualsense-qa.git -
Navigate to the project directory:
cd visualsense-qa -
Install the required dependencies:
pip install -r requirements.txt -
Run the Streamlit app:
streamlit run app.py -
Access the app in your web browser at
http://localhost:8501.
- Upload an image using the file uploader.
- Type a question related to the content of the image in the text box provided.
- Click the "Ask" button to submit the question.
- The model will process the image and generate an answer to the question asked.
Contributions are welcome! If you'd like to contribute to VisualSense QA, please follow these guidelines:
- Fork the repository.
- Create a new branch (
git checkout -b feature/your-feature-name). - Make your changes and commit them (
git commit -am 'Add new feature'). - Push to the branch (
git push origin feature/your-feature-name). - Create a new pull request.
- ViLT: The Vision-and-Language Transformer model is developed by the Hugging Face team.
- Streamlit: VisualSense QA is built using Streamlit, an open-source app framework for Machine Learning and Data Science projects.
For questions or feedback, please contact [yadamvethan1@gmail.com].
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