metadata
tags:
- visual-question-answering
license: apache-2.0
widget:
- text: What's the animal doing?
src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
- text: What is on top of the building?
src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
language:
- en
metrics:
- accuracy
library_name: transformers
Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2
Vision-and-Language Transformer (ViLT) model fine-tuned on VQAv2. It was introduced in the paper ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision by Kim et al. and first released in this repository.
Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team.
Intended uses & limitations
You can use the raw model for visual question answering.
How to use
Here is how to use this model in PyTorch:
from transformers import ViltProcessor, ViltForQuestionAnswering
import requests
from PIL import Image
# prepare image + 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])
Training data
(to do)
Training procedure
Preprocessing
(to do)
Pretraining
(to do)
Evaluation results
(to do)
BibTeX entry and citation info
@misc{kim2021vilt,
title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision},
author={Wonjae Kim and Bokyung Son and Ildoo Kim},
year={2021},
eprint={2102.03334},
archivePrefix={arXiv},
primaryClass={stat.ML}
}