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

Vision-and-Language Transformer (ViLT) model fine-tuned on [VQAv2](https://visualqa.org/). It 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). 

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:

```python
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}
}
```
{"license": "apache-2.0", "tags": ["visual-question-answering"], "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"}]}
transformers pytorch vilt visual-question-answering arxiv:2102.03334 license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #vilt #visual-question-answering #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #has_space #region-us 


# 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:



## 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 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: ## 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 #visual-question-answering #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 # 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: ## 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 #visual-question-answering #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
# 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:## Training data

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

(to do)### Pretraining

(to do)## Evaluation results

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