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---
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tags:
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- visual-question-answering
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license: apache-2.0
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widget:
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- text: "What's the animal doing?"
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src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg"
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- text: "What is on top of the building?"
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src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg"
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---
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# Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2
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Vision-and-Language Transformer (ViLT) model fine-tuned on [VQAv2](https://visualqa.org/). It was introduced in the paper [ViLT: Vision-and-Language Transformer
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Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT).
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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.
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## Intended uses & limitations
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You can use the raw model for visual question answering.
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### How to use
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Here is how to use this model in PyTorch:
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```python
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from transformers import ViltProcessor, ViltForQuestionAnswering
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import requests
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from PIL import Image
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# prepare image + question
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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text = "How many cats are there?"
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processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
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model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
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# prepare inputs
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encoding = processor(image, text, return_tensors="pt")
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# forward pass
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outputs = model(**encoding)
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logits = outputs.logits
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idx = logits.argmax(-1).item()
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print("Predicted answer:", model.config.id2label[idx])
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```
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## Training data
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(to do)
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## Training procedure
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### Preprocessing
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(to do)
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### Pretraining
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(to do)
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## Evaluation results
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(to do)
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### BibTeX entry and citation info
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```bibtex
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@misc{kim2021vilt,
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title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision},
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author={Wonjae Kim and Bokyung Son and Ildoo Kim},
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year={2021},
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eprint={2102.03334},
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archivePrefix={arXiv},
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primaryClass={stat.ML}
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}
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``` |
