Efficient-CapsNet: Capsule Network with Self-Attention Routing Implementation

Introduction

This repository contains my implementation of the Efficient-CapsNet model, a Capsule Network enhanced with a self-attention routing mechanism, originally proposed by Vittorio Mazzia, Francesco Salvetti, and Marcello Chiaberge. This innovative approach improves the model's efficiency and performance by focusing on the most relevant parts of the data, making it suitable for a wide range of applications including image recognition, segmentation, and more.

Features

• Self-Attention Routing: Implementation of the novel self-attention routing mechanism that enhances the model's capability to focus on relevant features.
• Customizable Architecture: Easy to modify for various datasets and applications.
• Pre-Trained Models: Includes pre-trained models on [SPECIFY DATASETS], ready to use out of the box.
• Detailed Documentation: Comprehensive documentation on how to get started, use the model, and customize it.

Requirements

List the libraries and tools required to run your implementation, including version numbers if applicable. For example:

• Python >= 3.6
• PyTorch >= 1.7
• NumPy >= 1.19