From f67a14e8e0060512b86b4cfca53ac20e8a18e478 Mon Sep 17 00:00:00 2001 From: sumn2u Date: Sun, 7 Jan 2024 21:30:19 -0600 Subject: [PATCH] fix image indentation --- paper/paper.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index e8db7e1..da5af52 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -36,13 +36,14 @@ Integration of machine learning models with mobile devices presents a promising The model was trained with Tesla T4 GPU and uses EfficientNetV2 [@tan2021efficientnetv2] model as a base model with addition of agumentation layer. Adam was used as an optmizer with intital learning rate of 0.01. Which was later optmised using [optuna](https://optuna.org/) to create more accurate optimization parameters. The training and validation loss is shown in \autoref{fig:training_vs_val_loss} whereas \autoref{fig:training_vs_val_accuracy} shows training and validation accuracy on the performed experiment[^2]. -![Training and Validation loss at different epochs\label{fig:training_vs_val_loss}](training_vs_val_loss.png){width="100%"} +![Training and Validation loss at different epochs\label{fig:training_vs_val_loss}](training_vs_val_loss.png){width="60%"} -![Training and Validation accuracy at different epochs\label{fig:training_vs_val_accuracy}](training_vs_val_accuracy.png){width="100%"} +![Training and Validation accuracy at different epochs\label{fig:training_vs_val_accuracy}](training_vs_val_accuracy.png){width="60%"} -The confusion matix of the modle is shown in \autoref{fig:confusion_matrix}. -![Confusion Matrix\label{fig:confusion_matrix}](confusion_matrix.png){width="100%"} +The confusion matix of the modle is shown in +\autoref{fig:confusion_matrix}. +![Confusion Matrix\label{fig:confusion_matrix}](confusion_matrix.png){width="60%"} [^2]: [https://www.kaggle.com/code/sumn2u/garbage-classification-transfer-learning](https://www.kaggle.com/code/sumn2u/garbage-classification-transfer-learning).