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

bibtex

@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}
}

Jeney/vilt-b32-finetuned-vqa

Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2

Intended uses & limitations

How to use

Training data

Training procedure

Preprocessing

Pretraining

Evaluation results

BibTeX entry and citation info

Jeney/
vilt-b32-finetuned-vqa