README.md

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}
}