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  \textbf{Course:} \course
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% 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}
\\
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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