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Predicting physical properties from graphene defects images with YOLOv8, OpenCV and scikit-learn.

Python 65.78% Shell 0.26% Perl 0.06% Tcl 4.93% C 14.65% C++ 0.57% Objective-C 0.07% Makefile 0.39% Batchfile 0.01% HTML 0.23% CSS 0.01% PowerShell 0.03% Roff 1.11% Jupyter Notebook 11.93%

computer-vision machine-learning opencv pandas python scikit-learn

graphedefectdetector's Introduction

GrapheDefectDetector

R^2 score del predittore: 0.97

Trasformazione dataset .xyz in .png ✔️
Object Detection sui difetti ✔️
Analisi dei difetti con OpenCV per estrapolazione feature geometriche ✔️
Correlazione feature geometriche dei difetti con propietà fisiche del campione ✔️
Modello predittivo per total_energy a partire dalle feature geomtriche dei campioni ✔️

E' possibile lanciare una demo passo passo da main_playground.ipynb

Traformazione campioni in formato .xyz in immagini:

Il training di YOLO è stato fatto con 100 immagini su colab. Il test del preditorre è stato fatto con 1000 inferenze di YOLO.

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