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+ ---
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+ license: apache-2.0
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+ language:
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+ - en
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+ tags:
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+ - visual_bert
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+ - vqa
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+ - easy_vqa
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+ ---
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+ # Visual BERT finetuned on easy_vqa
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+ This model is a finetuned version of the VisualBERT model on the easy_vqa dataset. The dataset is available at the following [github repo](https://github.com/vzhou842/easy-VQA/tree/master/easy_vqa)
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+
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+ ## VisualBERT
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+ VisualBERT is a multi-modal vision and language model. It can be used for tasks such as visual question answering, multiple choice and visual reasoning.
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+ For more info on VisualBERT, please refer to the [documentation](https://huggingface.co/docs/transformers/model_doc/visual_bert#overview)
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+
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+ ## Dataset
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+ The dataset easy_vqa, with which the model was fine-tuned, can be easily installed via the package easy_vqa:
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+ ```python
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+ pip install easy_vqa
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+ ```
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+
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+ An instance of the dataset is composed of a question, the answer of the question (a label) and the id of the image related to the question.
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+ Each image is 64x64 and contains a shape (rectangle, triangle or circle) filled with a single color (blue, red, green, yellow, black, gray, brown or teal)
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+ in a random position.
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+
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+ The questions of the dataset inquire about the shape (e.g. What is the blue shape?), the color of the shape (e.g. What color is the triangle?)
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+ and the presence of a particular shape/color in both affermative and negative form (e.g. Is there a red shape?).
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+ Therefore, the possible answers to a question are: the three possible shapes, the eight possible colors, yes and no.
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+
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+ More information about the package functions which allow to load the images and the questions can be found in the dataset's [repo](https://github.com/vzhou842/easy-VQA/tree/master/easy_vqa)
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+ as well an utility script to generate new instances of the dataset in case Data Augmentation is needed.
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+
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+ ## How to Use
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+ Load the image processor and the model with the following code:
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+ ```python
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+ processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
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+
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+ model = VisualBertForQuestionAnswering.from_pretrained("daki97/visualbert_finetuned_easy_vqa")
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+ ```
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+
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+ ## COLAB Demo
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+ An example of the usage of the model with the easy_vqa dataset is available [here](https://colab.research.google.com/drive/1yQfmz6wiSasRl6z-DmP-X403r3lZFqQS#scrollTo=HeVnH8BKkYCI)