Vision Transformer-Based Image Classification

This project implements a custom neural network inspired by the Vision Transformer (ViT) architecture for image classification tasks. By leveraging the power of transformers and the patch-based approach, this model offers a novel way to process and analyze images. The core idea is to split images into smaller patches, extract meaningful features using a pre-trained model like ResNet, and apply attention mechanisms to capture complex relationships within the image.

Key Features

Patch-Based Image Processing:

The model divides images into non-overlapping patches, allowing for localized feature extraction and processing.

Pre-Trained Embedding Model:

Utilizes a pre-trained ResNet (or similar) to generate rich embeddings for each image patch, ensuring robust feature representation.

Multi-Head Self-Attention:

Implements the attention mechanism across multiple heads to focus on different parts of the image simultaneously, enhancing the model's ability to understand context.

Positional Encoding:

Integrates positional encodings to maintain spatial information within patches, crucial for capturing the structural layout of the image.

Flexible Design:

The network is highly modular, enabling easy adjustments to the number of patches, embedding dimensions, and the attention mechanism.

Visualization Tools:

Includes methods to visualize the attention maps, providing insights into what the model focuses on during classification.

Applications

This model is designed for a variety of image classification tasks, particularly where understanding the spatial relationships and detailed features within an image is critical. It is well-suited for tasks in fields like medical imaging, satellite image analysis, and any domain requiring fine-grained visual understanding.

Getting Started

To get started, clone this repository and install the necessary dependencies. Ensure you have access to a GPU for optimal performance, as the model leverages CUDA for faster computations.

git clone https://github.com/GboyeStack-Robotics-ML-Engineer/VIT---Transformers
cd vision-transformer-classification
pip install -r requirements.txt

Explore the code and experiment with different configurations to see the power of transformer-based image classification in action!