PaliGemma2 LoRA finetuned on VQAv2
Display a loading spinner while preparing
Create a dynamic 3D scene with random torus knots and lights
Explore news topics through interactive visuals
Rank images based on text similarity
Answer questions about images
Answer questions about documents or images
Ivy-VL is a lightweight multimodal model with only 3B.
Ask questions about an image and get answers
Rerun viewer with Gradio
Create visual diagrams and flowcharts easily
Chat with documents like PDFs, web pages, and CSVs
Explore data leakage in machine learning models
Paligemma2 Vqav2 is an AI tool that enables visual question answering (VQA). It is a version of the PaliGemma2 model that has been fine-tuned using LoRA (Low-Rank Adaptation) on the VQAv2 dataset, making it highly effective for tasks that involve answering questions about images. This tool is designed to understand visual content and provide accurate, context-relevant answers to user queries.
• Fine-tuned specifically for visual question answering tasks using the VQAv2 dataset.
• Leverages the LoRA technique to adapt the base PaliGemma2 model efficiently.
• Supports multi-language capabilities, enabling diverse applications.
• Capable of processing and interpreting complex visual inputs.
• Provides detailed and accurate responses to user questions about images.
What is the primary purpose of Paligemma2 Vqav2?
Paligemma2 Vqav2 is designed primarily for visual question answering, allowing users to ask questions about images and receive accurate responses.
What languages does Paligemma2 Vqav2 support?
Paligemma2 Vqav2 supports multiple languages, though it is optimized for English-based visual question answering tasks.
How accurate is Paligemma2 Vqav2?
The accuracy of Paligemma2 Vqav2 depends on the quality of the input images and the clarity of the questions. It performs best with clear, high-resolution images and specific, well-defined questions.