# `Vilt` Documentation

## Introduction

Welcome to the documentation for Vilt, a Vision-and-Language Transformer (ViLT) model fine-tuned on the VQAv2 dataset. Vilt is a powerful model capable of answering questions about images. This documentation will provide a comprehensive understanding of Vilt, its architecture, usage, and how it can be integrated into your projects.

## Overview

Vilt is based on the Vision-and-Language Transformer (ViLT) architecture, designed for tasks that involve understanding both text and images. It has been fine-tuned on the VQAv2 dataset, making it adept at answering questions about images. This model is particularly useful for tasks where textual and visual information needs to be combined to provide meaningful answers.

## Class Definition

```python
class Vilt:
    def __init__(self):
        """
        Initialize the Vilt model.
        """
```

## Usage

To use the Vilt model, follow these steps:

1. Initialize the Vilt model:

```python
from swarms.models import Vilt

model = Vilt()
```

2. Call the model with a text question and an image URL:

```python
output = model(
    "What is this image?", "http://images.cocodataset.org/val2017/000000039769.jpg"
)
```

### Example 1 - Image Questioning

```python
model = Vilt()
output = model(
    "What are the objects in this image?",
    "http://images.cocodataset.org/val2017/000000039769.jpg",
)
print(output)
```

### Example 2 - Image Analysis

```python
model = Vilt()
output = model(
    "Describe the scene in this image.",
    "http://images.cocodataset.org/val2017/000000039769.jpg",
)
print(output)
```

### Example 3 - Visual Knowledge Retrieval

```python
model = Vilt()
output = model(
    "Tell me more about the landmark in this image.",
    "http://images.cocodataset.org/val2017/000000039769.jpg",
)
print(output)
```

## How Vilt Works

Vilt operates by combining text and image information to generate meaningful answers to questions about the provided image. Here's how it works:

1. **Initialization**: When you create a Vilt instance, it initializes the processor and the model. The processor is responsible for handling the image and text input, while the model is the fine-tuned ViLT model.

2. **Processing Input**: When you call the Vilt model with a text question and an image URL, it downloads the image and processes it along with the text question. This processing step involves tokenization and encoding of the input.

3. **Forward Pass**: The encoded input is then passed through the ViLT model. It calculates the logits, and the answer with the highest probability is selected.

4. **Output**: The predicted answer is returned as the output of the model.

## Parameters

Vilt does not require any specific parameters during initialization. It is pre-configured to work with the "dandelin/vilt-b32-finetuned-vqa" model.

## Additional Information

- Vilt is fine-tuned on the VQAv2 dataset, making it proficient at answering questions about a wide range of images.
- You can use Vilt for various applications, including image question-answering, image analysis, and visual knowledge retrieval.

That concludes the documentation for Vilt. We hope you find this model useful for your vision-and-language tasks. If you have any questions or encounter any issues, please refer to the Hugging Face Transformers documentation for further assistance. Enjoy working with Vilt!