vilt b32 finetuned vqa

Introduction

The Vision-and-Language Transformer (ViLT) is a model fine-tuned on the VQAv2 dataset for visual question answering. Introduced by Kim et al. in the paper "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision," it is available in the Hugging Face model repository.

Architecture

ViLT is designed to handle both vision and language tasks without the use of convolutional layers or region supervision. It leverages transformer architecture to integrate visual and textual data.

Training

Details about the training data, procedure, preprocessing, and pretraining are not provided in the documentation.

Guide: Running Locally

To use the ViLT model for visual question answering with PyTorch, follow these steps:

1. Install Dependencies:

   • Ensure you have Python and PyTorch installed.
   • Install the transformers library from Hugging Face.

2. Set Up the Code:

   from transformers import ViltProcessor, ViltForQuestionAnswering
   import requests
   from PIL import Image
   
   # Prepare image and question
   url = "http://images.cocodataset.org/val2017/000000039769.jpg"
   image = Image.open(requests.get(url, stream=True).raw)
   text = "How many cats are there?"
   
   processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
   model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
   
   # Prepare inputs
   encoding = processor(image, text, return_tensors="pt")
   
   # Forward pass
   outputs = model(**encoding)
   logits = outputs.logits
   idx = logits.argmax(-1).item()
   print("Predicted answer:", model.config.id2label[idx])
   

3. Hardware Requirements:

   • It is recommended to use a cloud GPU service like AWS, GCP, or Azure for better performance.

License

The ViLT model is released under the Apache 2.0 License, allowing for both personal and commercial use with appropriate attribution.