Mesh classification with Graph Neural Network

This repository hold my final project for the Neural Networks and Deep Learning (2023) course at UniPD.

The project presents an simple, original Message Passing Graph Neural Network (GNN) architecture that processes input graphs of arbitrary size representing a 3D mesh of an object. With the help of Torch Geometric's framework for developing GNN models, the goal was to see if a simple architecture that updates nodes embeddings without altering the graph topology was able to extract nodes features meaningful enough to perform object classification succesfully.

The pipeline is simple:

1. Load the dataset (ModelNet10/40 was used for benchmarking); each mesh is a graph, with nodes being the vertices of the mesh and the edges given by the triangular connectivity
2. Preprocess the dataset via a custom transform defined in transform.py
3. Train the model defined in model.py

Usage

Install working conda environment:

conda env create --name envname --file=environment.yml; conda activate envname

define training parameters in config.json, then launch training with

python train.py

TensorBoard support is included for metrics monitoring.