fix citation issues

paper/paper.bib

@@ -174,3 +174,13 @@ @misc{suman_kunwar_2023
 	author={Suman Kunwar},
 	year={2023}
 }
+
+@misc{tan2021efficientnetv2,
+      title={EfficientNetV2: Smaller Models and Faster Training}, 
+      author={Mingxing Tan and Quoc V. Le},
+      year={2021},
+      eprint={2104.00298},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+	  doi={10.48550/arXiv.2104.00298}
+}

paper/paper.md

@@ -37,9 +37,9 @@ 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.
 
 
-![Training and Validation loss at different epochs\label{fig:training_vs_val_loss}](training_vs_val_loss.png)
+![Training and Validation loss at different epochs\label{fig:training_vs_val_loss}](training_vs_val_loss.png){width="50%"}
 
-![Training and Validation accuracy at different epochs\label{fig:training_vs_val_accuracy}](training_vs_val_accuracy.png)
+![Training and Validation accuracy at different epochs\label{fig:training_vs_val_accuracy}](training_vs_val_accuracy.png){width="50%"}
 
  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](https://www.kaggle.com/code/sumn2u/garbage-classification-transfer-learning)
 The confusion matix of the modle is shown in