Swin Transformer for Masked Image Modeling

# Swin Transformer for Masked Image Modeling

This repository demonstrates how to use a pre-trained Swin Transformer for Masked Image Modeling. Masked Image Modeling is a task where random patches in an image are masked out, and the model is trained to predict the masked patches. The Swin Transformer is a powerful hierarchical vision transformer designed for computer vision tasks.

Swin Transformers, short for "Shifted Windows," were introduced in the paper titled "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" by Liu et a. (2021). Unlike traditional transformers, Swin Transformers divide the image into non-overlapping shifted windows, enabling efficient and scalable computation.

![Example](example.jpg)

## How It Works

1. We load an image from a URL using the PIL library.
2. The image is processed using the pre-trained Swin Transformer image processor (AutoImageProcessor).
3. A SwinForMaskedImageModeling model is loaded, pre-trained on the Swin Transformer architecture.
4. The image is divided into patches, and random patches are masked using a boolean mask.
5. The model is then trained to predict the masked patches given the rest of the image.
6. The model's reconstruction and loss are obtained for evaluation.

## Prerequisites

To run this code, you need the following libraries installed:

- `transformers`: The main library that provides the AutoImageProcessor and SwinForMaskedImageModeling classes.
- `torch`: The PyTorch library, is essential for running the deep learning model and performing tensor computations.
- `PIL`: The Python Imaging Library, required for image processing.
- `requests`: For fetching the image from a URL.

You can install these dependencies using the following command:

```bash
pip install transformers torch pillow requests

Usage

1. Clone the repository and navigate to the project directory.
2. Run the notebook to see how the Swin Transformer performs on the masked image modeling task.

python masked_image_modeling.py

3. The reconstructed pixel values and loss will be displayed, indicating the quality of the model's predictions.

Example Output

[1, 3, 192, 192]

Acknowledgments

• The Hugging Face team for providing the pre-trained Swin Transformer models and image processors.
• The COCO dataset for the example image used in this demonstration.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvement, feel free to create a pull request.

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License

This project is licensed under the MIT License.