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\newcommand { \assignmentnumber } { 03} % <-- CHANGE Assignment Number
\newcommand { \name } { Simon Franken} % <-- CHANGE YOUR NAME
\newcommand { \course } { Multimedia Project WiSe 2526} % <-- CHANGE COURSE NAME
\newcommand { \duedate } { 2025-11-05} % <-- CHANGE DUE DATE
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\textbf { Course:} \course
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%------------------ START OF ASSIGNMENT -----------------------
% Write your solutions below
\section * { Exercise 3.1 Dataset Parsing}
\begin { enumerate} [label=\alph * )]
\setcounter { enumi} { 2}
\item \begin { figure} [htp]
\centering
\includegraphics [width=4cm] { output.jpg}
\caption { 02254418.jpg with 18 annotations}
\end { figure}
\end { enumerate}
\section * { Exercise 3.3 Training}
\begin { tabular} { |c||c|}
\hline Batch size & 32 \\
\hline Training epoches & 10 \\
\hline Loss & 0.3719 \\
\hline Accuracy & 78.90 \% \\
\hline
\end { tabular}
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The accuracy metric isn’ t ideal because it’ s quite intuitive to classify whether an object appears once or more in an image. In this case, the model might not have guessed the correct number of objects (e.g., 3 instead of 2), but the result will still be interpreted as accurate. This also makes it more challenging to backpropagate. A better approach would be to output the actual number of objects (in this case, humans).
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%------------------ END OF ASSIGNMENT -----------------------
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