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PyTorch
vilt
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vilt-vsr-random / README.md
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---
license: apache-2.0
---
# Vision-and-Language Transformer (ViLT), fine-tuned on VSR random split
Vision-and-Language Transformer (ViLT) model fine-tuned on random split of [Visual Spatial Reasoning (VSR)](https://arxiv.org/abs/2205.00363). ViLT was introduced in the paper [ViLT: Vision-and-Language Transformer
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).
## Intended uses & limitations
You can use the model to determine whether a sentence is true or false given an image.
### How to use
Here is how to use the model in PyTorch:
```
from transformers import ViltProcessor, ViltForImagesAndTextClassification
import requests
from PIL import Image
image = Image.open(requests.get("https://camo.githubusercontent.com/ffcbeada14077b8e6d4b16817c91f78ba50aace210a1e4754418f1413d99797f/687474703a2f2f696d616765732e636f636f646174617365742e6f72672f747261696e323031372f3030303030303038303333362e6a7067", stream=True).raw)
text = "The person is ahead of the cow."
processor = ViltProcessor.from_pretrained("juletxara/vilt-vsr-random")
model = ViltForImagesAndTextClassification.from_pretrained("juletxara/vilt-vsr-random")
# prepare inputs
encoding = processor(image, text, return_tensors="pt")
# forward pass
outputs = model(input_ids=encoding.input_ids, pixel_values=encoding.pixel_values.unsqueeze(0))
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}
}
@article{liu2022visual,
title={Visual Spatial Reasoning},
author={Liu, Fangyu and Emerson, Guy and Collier, Nigel},
journal={arXiv preprint arXiv:2205.00363},
year={2022}
}
```