Vision Transformer for Multiclass Classification 👁️

This repository contains an implementation of a Vision Transformer model for multiclass classification tasks. The implemented model includes the necessary components such as patch extraction and positional embedding, enabling the Vision Transformer to effectively process and classify images.

Overview

The Vision Transformer (ViT) is a deep learning architecture that applies the Transformer model, originally introduced for natural language processing tasks, to visual data. In this implementation, we have incorporated the crucial steps of patch extraction and positional embedding, which are essential for the proper functioning of the Vision Transformer.

The patch extraction step divides the input image into smaller patches, treating each patch as a token. This process enables the model to capture local information within the image. The positional embedding step assigns learnable embeddings to each patch, providing the model with spatial information about the patches' locations.

By combining the patch extraction, positional embedding, and self-attention mechanisms, the Vision Transformer can effectively model long-range interactions between image patches, leading to improved performance in image classification tasks.

Features

• Implementation of the Vision Transformer model for multiclass image classification, incorporating patch extraction and positional embedding.
• Preprocessing pipeline to prepare the input images for the Vision Transformer.
• Training and evaluation scripts to facilitate model training and performance assessment.
• Fine-tuning capabilities to adapt the model to specific datasets.
• Support for various datasets commonly used in image classification, such as CIFAR-10, ImageNet, etc.
• Integration with popular deep learning libraries.

Requirements

• Python 3.x
• Deep learning framework (PyTorch)
• Dataset(s) for training and evaluation

Usage

• Install the required dependencies specified in the requirements.txt file.
• Prepare the dataset(s) for training and evaluation.
• Adjust the hyperparameters and configurations in the provided scripts to fit your specific use case.
• Run the "main" script to train the Vision Transformer model on your dataset.
• Evaluate the trained model using the evaluation metrics.
• Fine-tune the model or experiment with different configurations to improve performance if desired.

References

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.

Khan, S., Anwar, S., Hayat, M., Gao, Y., & Barnes, N. (2021). TransGAN: Two Transformers Can Make One Strong GAN. arXiv preprint arXiv:2102.07074.

Please feel free to customize and expand upon this description to provide more specific details about your implementation, such as any additional modifications or optimizations you have made to the patch extraction or positional embedding process.