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\beamer@slide {fig:design}{15}
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\beamer@slide {tab:yolo-metrics}{34}
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\documentclass{beamer}
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\beamertemplatenavigationsymbolsempty
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\usetheme{default}
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\usecolortheme{dolphin}
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\usepackage{graphicx}
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\usepackage{caption}
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\usepackage{tikz}
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\usepackage{dsfont}
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\usepackage{siunitx}
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\usepackage{booktabs}
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\usepackage[labelformat=empty]{caption}
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\usetikzlibrary{shapes,arrows}
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% Define block styles
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\tikzstyle{decision} = [diamond, draw, fill=blue!20, text width=4.5em, text badly centered, node distance=3cm, inner sep=0pt]
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\tikzstyle{block} = [rectangle, draw, fill=blue!20, text width=5em, text centered, rounded corners, minimum height=4em]
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\setbeamerfont{caption}{size=\tiny}
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\begin{document}
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\title[Plant Detection and State Classification]{Plant Detection and
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State Classification with Machine Learning}
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\author{Tobias Eidelpes}
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\date{March 12, 2024}
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\begin{frame}
|
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\maketitle
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\end{frame}
|
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|
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\section{Introduction}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Problem Statement}
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\begin{itemize}
|
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\setlength{\itemsep}{1.1\baselineskip}
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\item Automated detection of water stress \pause
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\item Automated watering of household plants \pause
|
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\item Decision-making \emph{in the field} \pause
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\item No research so far in this context
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Research Questions}
|
||||
\begin{enumerate}
|
||||
\setlength{\itemsep}{1.1\baselineskip}
|
||||
\item How well does the model work in theory and how well in
|
||||
practice? \pause
|
||||
\item What are possible reasons for it to work/not work? \pause
|
||||
\item What are possible improvements to the system in the future?
|
||||
\end{enumerate}
|
||||
\end{frame}
|
||||
|
||||
\section{Methodological Approach}
|
||||
|
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\begin{frame}
|
||||
\frametitle{Methods}
|
||||
\begin{columns}[c]
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\column{.5\textwidth}
|
||||
\begin{enumerate}
|
||||
\setlength{\itemsep}{1.1\baselineskip}
|
||||
\item Literature Review
|
||||
\item Dataset Curation
|
||||
\item Model Training
|
||||
\item Optimization
|
||||
\item Deployment
|
||||
\item Evaluation
|
||||
\end{enumerate}
|
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\column{.5\textwidth}
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\begin{center}
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||||
\includegraphics[width=\textwidth]{graphics/wilted\_007.jpg}
|
||||
\end{center}
|
||||
\end{columns}
|
||||
\end{frame}
|
||||
|
||||
\section{Prototype Design}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Prototype Design: Requirements} \pause
|
||||
\begin{itemize}
|
||||
\setlength{\itemsep}{1.1\baselineskip}
|
||||
\item Detect and Classify \pause
|
||||
\item Publish Results via REST-API \pause
|
||||
\item Reasonable Inference Time \pause
|
||||
\item Reasonable Model Performance
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Prototype Design}
|
||||
\begin{figure}[htbp]
|
||||
\centerline{\includegraphics[width=0.9\textwidth]{graphics/setup.pdf}}
|
||||
\label{fig:design}
|
||||
\end{figure}
|
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\end{frame}
|
||||
|
||||
\section{Prototype Implementation}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Prototype Implementation: YOLOv7n}
|
||||
\begin{minipage}[bt]{.49\textwidth}
|
||||
\begin{itemize}
|
||||
\setlength{\itemsep}{1.1\baselineskip}
|
||||
\item Pretrained on COCO
|
||||
\item OID classes \emph{Houseplant} and \emph{Plant}
|
||||
\item Training Set
|
||||
\begin{itemize}
|
||||
\item \num{79204} images
|
||||
\item \num{284130} bounding boxes
|
||||
\end{itemize}
|
||||
\item Validation Set
|
||||
\begin{itemize}
|
||||
\item \num{3091} images
|
||||
\item \num{4092} bounding boxes
|
||||
\end{itemize}
|
||||
\end{itemize}
|
||||
\end{minipage}
|
||||
\begin{minipage}[bt]{.49\textwidth}
|
||||
\vspace{.5cm}
|
||||
\begin{figure}
|
||||
\begin{center}
|
||||
\includegraphics[width=.85\textwidth]{graphics/houseplant.jpg}
|
||||
\caption{Earthy Tones For Fallsurlevif by Flickr User decor8
|
||||
under CC BY 2.0}
|
||||
\end{center}
|
||||
\end{figure}
|
||||
\end{minipage}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Prototype Implementation: YOLOv7n}
|
||||
\begin{figure}[htbp]
|
||||
\begin{center}
|
||||
\includegraphics[width=\textwidth]{graphics/model_fitness.pdf}
|
||||
\end{center}
|
||||
\end{figure}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Prototype Implementation: YOLOv7n}
|
||||
\begin{figure}[htbp]
|
||||
\begin{center}
|
||||
\includegraphics[width=\textwidth]{graphics/val\_box\_obj\_loss.pdf}
|
||||
\end{center}
|
||||
\end{figure}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{YOLOv7n Hyperparameter Optimization} \pause
|
||||
\begin{itemize}
|
||||
\setlength{\itemsep}{1.1\baselineskip}
|
||||
\item Mutate 26 out of 30 hyperparameters \pause
|
||||
\item Parent chosen randomly from top five previous generations \pause
|
||||
\item 3 epochs per iteration \pause
|
||||
\item 87 iterations \pause
|
||||
\item Best with 0.6076 fitness
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{YOLOv7n Hyperparameter Optimization}
|
||||
\begin{figure}[htbp]
|
||||
\begin{center}
|
||||
\includegraphics[width=\textwidth]{graphics/model_fitness\_final.pdf}
|
||||
\end{center}
|
||||
\end{figure}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Prototype Implementation: ResNet-50}
|
||||
\begin{minipage}[bt]{.49\textwidth}
|
||||
\begin{itemize}
|
||||
\setlength{\itemsep}{1.1\baselineskip}
|
||||
\item Pretrained on ImageNet
|
||||
\item Training Set
|
||||
\begin{itemize}
|
||||
\item \num{384} healthy
|
||||
\item \num{384} stressed
|
||||
\end{itemize}
|
||||
\item Validation Set
|
||||
\begin{itemize}
|
||||
\item \num{68} healthy
|
||||
\item \num{68} stressed
|
||||
\end{itemize}
|
||||
\end{itemize}
|
||||
\end{minipage}
|
||||
\begin{minipage}[bt]{.49\textwidth}
|
||||
\begin{center}
|
||||
\includegraphics[width=\textwidth]{graphics/classifier-cam-cropped.pdf}
|
||||
\end{center}
|
||||
\end{minipage}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Prototype Implementation: ResNet-50 Accuracy}
|
||||
\begin{figure}[htbp]
|
||||
\begin{center}
|
||||
\includegraphics[width=\textwidth]{graphics/classifier-metrics-acc.pdf}
|
||||
\caption{\normalsize Maximum validation accuracy of 0.9118 at epoch 27}
|
||||
\end{center}
|
||||
\end{figure}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Prototype Implementation: ResNet-50 Loss}
|
||||
\begin{figure}[htbp]
|
||||
\begin{center}
|
||||
\includegraphics[width=\textwidth]{graphics/classifier-metrics-loss.pdf}
|
||||
\end{center}
|
||||
\end{figure}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{ResNet-50 Hyperparameter Optimization}
|
||||
\begin{itemize}
|
||||
\setlength{\itemsep}{1.1\baselineskip}
|
||||
\item Random search \pause
|
||||
\item 10 epochs per iteration \pause
|
||||
\item 138 iterations \pause
|
||||
\item Best with 0.9783 $\mathrm{F}_{1}$-score
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{ResNet-50 Hyperparameter Optimization}
|
||||
\begin{figure}[htbp]
|
||||
\centerline{\includegraphics[width=\textwidth]{graphics/classifier-hyp-metrics.pdf}}
|
||||
\caption{\normalsize Learning rate and batch size effect on
|
||||
$\mathrm{F}_{1}$-score}
|
||||
\end{figure}
|
||||
\end{frame}
|
||||
|
||||
\section{Evaluation}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{YOLOv7n Evaluation}
|
||||
\begin{itemize}
|
||||
\setlength{\itemsep}{1.1\baselineskip}
|
||||
\item Test Set
|
||||
\begin{itemize}
|
||||
\item \num{9000} images
|
||||
\item \num{12238} bounding boxes \pause
|
||||
\end{itemize}
|
||||
\end{itemize}
|
||||
\begin{table}[h]
|
||||
\centering
|
||||
\begin{tabular}{lrrrr}
|
||||
\toprule
|
||||
{} & Precision & Recall & $\mathrm{F}_1$-score & Support \\
|
||||
\midrule
|
||||
Plant & \num{0.5476} & \num{0.7379} & \num{0.6286} & \num{12238} \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
\caption{\scriptsize Results for the non-optimized object detection model}
|
||||
\label{tab:yolo-metrics}
|
||||
\end{table}
|
||||
\begin{table}[h]
|
||||
\centering
|
||||
\begin{tabular}{lrrrr}
|
||||
\toprule
|
||||
{} & Precision & Recall & $\mathrm{F}_1$-score & Support \\
|
||||
\midrule
|
||||
Plant & \num{0.6334} & \num{0.7028} & \num{0.6663} & \num{12238} \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
\caption{\scriptsize Results for the optimized object detection model}
|
||||
\label{tab:yolo-metrics-hyp}
|
||||
\end{table}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{ResNet-50 Evaluation}
|
||||
\begin{center}
|
||||
\begin{figure}[htbp]
|
||||
\includegraphics[width=0.65\textwidth]{graphics/classifier-hyp-folds.pdf}
|
||||
\caption{\scriptsize ROC curves and AUC for classifier 10-fold
|
||||
cross-validation}
|
||||
\end{figure}
|
||||
\end{center}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Aggregate Model Evaluation}
|
||||
\begin{itemize}
|
||||
\setlength{\itemsep}{1.1\baselineskip}
|
||||
\item Pre-annotated Test Set
|
||||
\begin{itemize}
|
||||
\item \num{640} images
|
||||
\item \num{766} bounding boxes healthy
|
||||
\item \num{494} bounding boxes stressed \pause
|
||||
\end{itemize}
|
||||
\item Non-optimized model $\mathrm{mAP} = 0.3581$ \pause
|
||||
\item Optimized model $\mathrm{mAP} = 0.3838$
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
\section{Conclusion}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Limitations and Conclusions}
|
||||
\begin{itemize}
|
||||
\setlength{\itemsep}{0.75\baselineskip}
|
||||
\item I am \emph{not} an expert labeler! \pause
|
||||
\item Object detection performs well (mAP 0.5727) \pause
|
||||
\item Optimized detector worse than non-optimized \pause
|
||||
\item Inconsistent ground truth \pause
|
||||
\item Robust classification
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Research Questions Revisited}
|
||||
\begin{enumerate}
|
||||
\setlength{\itemsep}{1.1\baselineskip}
|
||||
\item How well does the model work in theory and how well in practice? \pause
|
||||
\begin{itemize}
|
||||
\item Plant detection comparable to benchmarks \pause
|
||||
\item Impressive stress classification \pause
|
||||
\end{itemize}
|
||||
\item What are possible reasons for it to work/not work? \pause
|
||||
\begin{itemize}
|
||||
\item Dataset curation \pause
|
||||
\end{itemize}
|
||||
\item What are possible improvements to the system in the future? \pause
|
||||
\begin{itemize}
|
||||
\item Use more computational resources \pause
|
||||
\item Expert labeling
|
||||
\end{itemize}
|
||||
\end{enumerate}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\centering
|
||||
\Large
|
||||
Thank you for your attention!
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{ResNet-50 CAM}
|
||||
\begin{figure}[htbp]
|
||||
\centerline{\includegraphics[width=0.9\textwidth]{graphics/classifier-cam.pdf}}
|
||||
\caption[]{\label{fig:classifier-cam} Top-right: CAM for
|
||||
\emph{healthy}. Bot-right: CAM for \emph{stressed}}
|
||||
\end{figure}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Aggregate Model Evaluation}
|
||||
\begin{table}
|
||||
\centering
|
||||
\begin{tabular}{lrrrr}
|
||||
\toprule
|
||||
{} & Precision & Recall & $\mathrm{F}_{1}$-score & Support \\
|
||||
\midrule
|
||||
Healthy & \num{0.665} & \num{0.554} & \num{0.604} & \num{766} \\
|
||||
Stressed & \num{0.639} & \num{0.502} & \num{0.562} & \num{494} \\
|
||||
Weighted Avg & \num{0.655} & \num{0.533} & \num{0.588} & \num{1260} \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
\caption{Metrics for the non-optimized aggregate model}
|
||||
\label{tab:model-metrics}
|
||||
\end{table}
|
||||
\begin{table}
|
||||
\centering
|
||||
\begin{tabular}{lrrrr}
|
||||
\toprule
|
||||
{} & Precision & Recall & $\mathrm{F}_{1}$-score & Support \\
|
||||
\midrule
|
||||
Healthy & 0.711 & 0.555 & 0.623 & 766 \\
|
||||
Stressed & 0.570 & 0.623 & 0.596 & 494 \\
|
||||
Weighted Avg & 0.656 & 0.582 & 0.612 & 1260 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
\caption{Metrics for the optimized aggregate model}
|
||||
\label{tab:model-metrics-hyp}
|
||||
\end{table}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\end{document}
|
||||
%%% Local Variables:
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%%% mode: LaTeX
|
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%%% TeX-master: t
|
||||
%%% End:
|
||||
Reference in New Issue
Block a user