# VITIS AI Models for LHC Decision-Making

This repository serves as an archive of functional neural network models designed to run on the **VITIS AI VEK280** engine. The goal is to explore machine learning-driven decision-making for the **Large Hadron Collider (LHC)**.

I'm conducting this research on a remote server, and this repository provides a centralized location to pull the latest compiled models and run experiments.

## Included pretrained models

- **ResNet-50**  
- **ResNet-18**
- **EfficientNet BO** [pytorch]
- **My own network! (with training data)** [vanilla c++]

Resnet models are compiled into `.xmodel` format and are ready to run using the `resnet_pt` runtime executable.

> **Note:** To run any models using the VITIS AI runtime, you must be inside the correct **container** or working from a **VITIS-compatible environment**.

## Research Objective
 
The main objective of this project is to develop a **lightweight feedforward neural network** optimized for processing **particle collision data** from the LHC:
Currently working on deployment of my own model to the VEK 280 AI engine!


All models in this repository are **convolutional neural networks (CNNs) and Feedforward neural networks (FFNNs)** unless otherwise noted.