Visual QA: Chat with Image using Open Source AI Model - No OpenAI ❌

Описание: Welcome to my video on building a Visual Question Answering (VQA) system using state-of-the-art deep learning models! In this tutorial, I'll explore how to leverage the power of the Hugging Face's ViLT (Vision-and-Language Transformer) model to answer questions about images.

I'll start by introducing the ViLT model, which combines text embeddings with a Vision Transformer (ViT) architecture, enabling us to perform joint vision-and-language tasks. We'll dive into the research behind ViLT and understand how it achieves efficient and expressive pre-training for VQA.

Next, I'll demonstrate how to implement the ViLT model in two different ways: as an API using FastAPI and as an interactive app using Streamlit. FastAPI allows us to build a robust API that can receive image and text inputs and return the predicted answer. Streamlit, on the other hand, provides a user-friendly interface with an image uploader and text input field, giving users an interactive experience to ask questions about images.

During the implementation, I'll walk you through the code step by step, explaining key concepts and showcasing best practices for handling image processing, model inference, and error handling.

By the end of the video, you'll have a deep understanding of how to utilize the ViLT model for visual question answering and how to create both an API and an interactive app to leverage this powerful model. You'll be equipped with the knowledge and skills to apply similar techniques to various other vision-and-language tasks.

Whether you're an AI enthusiast, a developer, or simply curious about cutting-edge models, this video is for you! Don't forget to like, subscribe, and leave a comment with your thoughts and questions.

GitHub Link: https://github.com/AIAnytime/Visual-Q...
ViLT Model HF: https://huggingface.co/docs/transform...
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