
# 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}
}
```
{"license": "apache-2.0"}
transformers pytorch vilt arxiv:2205.00363 arxiv:2102.03334 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #vilt #arxiv-2205.00363 #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #region-us 


# 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). ViLT 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.

## 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:



## Training data

(to do)

## Training procedure

### Preprocessing

(to do)

### Pretraining

(to do)

## Evaluation results

(to do)

### BibTeX entry and citation info


# 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). ViLT 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. ## 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: ## Training data

(to do) ## Training procedure ### Preprocessing

(to do) ### Pretraining

(to do) ## Evaluation results

(to do) ### BibTeX entry and citation info
TAGS
#transformers #pytorch #vilt #arxiv-2205.00363 #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #region-us 
 # 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). ViLT 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. ## 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: ## Training data

(to do) ## Training procedure ### Preprocessing

(to do) ### Pretraining

(to do) ## Evaluation results

(to do) ### BibTeX entry and citation info
passage: TAGS
#transformers #pytorch #vilt #arxiv-2205.00363 #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #region-us 
# 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). ViLT 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.## 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:## Training data

(to do)## Training procedure### Preprocessing

(to do)### Pretraining

(to do)## Evaluation results

(to do)### BibTeX entry and citation info