MC_VGPMIL

MC_VGPMIL stands for Multiclass Variational Gaussian Processes for Multiple Instance Learning. This repository implements a framework for multiclass classification using Variational Gaussian Processes within the context of Multiple Instance Learning (MIL). It is written entirely in Python and aims to provide a robust and flexible solution for research and practical applications in MIL.

Attention: this is a toy model implementation

Table of Contents

• Introduction
• Features
• Setup and Installation
• Usage
• Examples

Introduction

MC_VGPMIL leverages Variational Gaussian Processes (GPs) to handle the complexities of Multiple Instance Learning (MIL) tasks, where labels are assigned to sets (or "bags") of instances rather than individual data points. This framework extends Gaussian Processes to solve multiclass problems, making it highly versatile for a variety of real-world applications, such as:

• Medical Imaging
• Text Classification
• Object Detection in Images

The repository provides tools for training and evaluating models, as well as examples for real-world datasets.

Features

• Multiclass Variational Gaussian Processes:
  • Implemented for MIL tasks.
  • Flexible and scalable for large datasets.
• Multiple Instance Learning Framework:
  • Handles bags of instances effectively.
  • Supports both instance-level and bag-level inference.
• Python Implementation:
  • Fully written in Python for ease of use and integration.
• Customizable Models:
  • Easily adapt the framework to specific MIL tasks.

Setup and Installation

Prerequisites

• Python 3.8 or newer.
• Package manager (pip or conda).

Installation Steps

1. Clone the repository:

   git clone [https://github.com/AMorQ/MC_VGPMIL.git](https://github.com/AMorQ/MC_VGPMIL.git)
   cd MC_VGPMIL

2. Set up a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

Usage

Prepare Your Dataset:

Organize your data in a bag-label format suitable for MIL tasks. Follow the provided examples to preprocess your dataset.

Train the Model:

Use the training scripts to fit the MC_VGPMIL model to your data. Example command:

python train.py --data_path <path_to_data> --output_dir <output_directory>

Evaluate the Model:

Assess the model's performance on validation or test datasets. Example command:

python evaluate.py --model_path <path_to_model> --data_path <path_to_data>

Customize the model:

Edit the config.json file