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    \title{Part2-Training}
    
    
    

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    \begin{document}
    
    
    \maketitle
    
    

    
    \subsection{Models and Analysis}\label{models-and-analysis}

    The following model architectures are trained/used in this excericise:
1. SSD mobilenet v1 2. Faster R-CNN with Inception Resnet v2, Atrous
version 3. SSD inception v2 Model 1 trained from the scratch while
pre-trained models are used to generate and analyse the results from
Model 2 and 3. I wanted to try and compare YOLO\_v2 (using darknet) also
but due to time constraint I was not able to use it. Since we need to
detect red round signs, the model is trained for 3 classes, \textbf{red
rounds, non-red rounds and triangular} . * \textbf{Model Architectures}

It is very hard to compare various model architectures but for real life
applications like Traffic Sign Detection we use make choices by
comparing performance and accuracy of detector types. The mean average
precision (mAP) is used in measuring accuracy. For pre-trained models,
these values are taken from the original references.The below table
summarizs the comparison of following measures: number of parameters,
step time (cpu) and mAP.

\begin{longtable}[]{@{}lll@{}}
\toprule
Model Name & No. of Parameters & mAP\tabularnewline
\midrule
\endhead
SSD mobilenet v1 & 5572809 & 61.64\tabularnewline
Faster R-CNN with Inception Resnet v2, Atrous version & 59412281 &
95.77\tabularnewline
SSD inception v2 & 13474849 & 66.10\tabularnewline
\bottomrule
\end{longtable}

\begin{itemize}
\tightlist
\item
  \textbf{Training Strategy}
\end{itemize}

Tensorflow object detection code is used for training the model. The tf
record of the the input is created (as opposed to the data processing
done in Part-1). Object detection module provides the extensive code to
train the model. The setup of the code is explained
\href{https://github.com/rishabhbhardwaj/SignDetection/blob/master/README.md}{here}
The training is done on CPU (2.3 GHz Intel Core i5, 16GB RAM).

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}1}]:} \PY{o}{\PYZpc{}}\PY{k}{matplotlib} inline
        \PY{k+kn}{import} \PY{n+nn}{tensorflow} \PY{k+kn}{as} \PY{n+nn}{tf}
        \PY{k+kn}{from} \PY{n+nn}{matplotlib} \PY{k+kn}{import} \PY{n}{pyplot} \PY{k}{as} \PY{n}{plt}
        \PY{k+kn}{from} \PY{n+nn}{PIL} \PY{k+kn}{import} \PY{n}{Image}
        \PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k+kn}{as} \PY{n+nn}{np}
        \PY{k+kn}{from} \PY{n+nn}{tf\PYZus{}utils} \PY{k+kn}{import} \PY{o}{*}
\end{Verbatim}


    \subsection{Training}\label{training}

    ssd\_mobilenet\_v1 is trained with the tensorflow object detection API.

Run the below command to train the model,

\texttt{export\ PYTHONPATH=\$PYTHONPATH:\textasciigrave{}pwd\textasciigrave{}:\textasciigrave{}pwd\textasciigrave{}/slim\ python\ object\_detection/legacy/train.py\ -\/-logtostderr\ -\/-pipeline\_config\_path\ ssd\_mobilenet\_v1.config\ -\/-train\_dir\ gtsdb\_data/model/ssd\_mobilenet\_v1}

The \texttt{ssd\_mobilenet\_v1.config} has the all the hyperparaters
configured, along with other architecture variables.

    \subsection{Using Pre-Trained Models}\label{using-pre-trained-models}

    \subsubsection{Loading trained (or pre-trained) model and detecting
bounding
boxes}\label{loading-trained-or-pre-trained-model-and-detecting-bounding-boxes}

Change the model path in utils to get results with different model.

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                \PY{n}{serialized\PYZus{}graph} \PY{o}{=} \PY{n}{fid}\PY{o}{.}\PY{n}{read}\PY{p}{(}\PY{p}{)}
                \PY{n}{od\PYZus{}graph\PYZus{}def}\PY{o}{.}\PY{n}{ParseFromString}\PY{p}{(}\PY{n}{serialized\PYZus{}graph}\PY{p}{)}
                \PY{n}{tf}\PY{o}{.}\PY{n}{import\PYZus{}graph\PYZus{}def}\PY{p}{(}\PY{n}{od\PYZus{}graph\PYZus{}def}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}


    \subsubsection{Detection using trained (or pre-trained)
model}\label{detection-using-trained-or-pre-trained-model}

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor} }]:} \PY{k}{with} \PY{n}{detection\PYZus{}graph}\PY{o}{.}\PY{n}{as\PYZus{}default}\PY{p}{(}\PY{p}{)}\PY{p}{:}
            \PY{k}{with} \PY{n}{tf}\PY{o}{.}\PY{n}{Session}\PY{p}{(}\PY{n}{graph}\PY{o}{=}\PY{n}{detection\PYZus{}graph}\PY{p}{)} \PY{k}{as} \PY{n}{sess}\PY{p}{:}
                \PY{k}{for} \PY{n}{idx}\PY{p}{,} \PY{n}{image\PYZus{}path} \PY{o+ow}{in} \PY{n+nb}{enumerate}\PY{p}{(}\PY{n}{TEST\PYZus{}IMAGE\PYZus{}PATHS}\PY{p}{)}\PY{p}{:}
                    \PY{n}{image} \PY{o}{=} \PY{n}{Image}\PY{o}{.}\PY{n}{open}\PY{p}{(}\PY{n}{image\PYZus{}path}\PY{p}{)}
                    \PY{n}{image\PYZus{}np} \PY{o}{=} \PY{n}{load\PYZus{}image\PYZus{}into\PYZus{}numpy\PYZus{}array}\PY{p}{(}\PY{n}{image}\PY{p}{)}
                    \PY{n}{image\PYZus{}np\PYZus{}expanded} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{expand\PYZus{}dims}\PY{p}{(}\PY{n}{image\PYZus{}np}\PY{p}{,} \PY{n}{axis}\PY{o}{=}\PY{l+m+mi}{0}\PY{p}{)}
                    \PY{n}{image\PYZus{}tensor} \PY{o}{=} \PY{n}{detection\PYZus{}graph}\PY{o}{.}\PY{n}{get\PYZus{}tensor\PYZus{}by\PYZus{}name}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{image\PYZus{}tensor:0}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
                    \PY{n}{boxes} \PY{o}{=} \PY{n}{detection\PYZus{}graph}\PY{o}{.}\PY{n}{get\PYZus{}tensor\PYZus{}by\PYZus{}name}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{detection\PYZus{}boxes:0}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
                    \PY{n}{scores} \PY{o}{=} \PY{n}{detection\PYZus{}graph}\PY{o}{.}\PY{n}{get\PYZus{}tensor\PYZus{}by\PYZus{}name}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{detection\PYZus{}scores:0}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
                    \PY{n}{classes} \PY{o}{=} \PY{n}{detection\PYZus{}graph}\PY{o}{.}\PY{n}{get\PYZus{}tensor\PYZus{}by\PYZus{}name}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{detection\PYZus{}classes:0}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
                    \PY{n}{num\PYZus{}detections} \PY{o}{=} \PY{n}{detection\PYZus{}graph}\PY{o}{.}\PY{n}{get\PYZus{}tensor\PYZus{}by\PYZus{}name}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{num\PYZus{}detections:0}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
                    \PY{p}{(}\PY{n}{boxes}\PY{p}{,} \PY{n}{scores}\PY{p}{,} \PY{n}{classes}\PY{p}{,} \PY{n}{num\PYZus{}detections}\PY{p}{)} \PY{o}{=} \PY{n}{sess}\PY{o}{.}\PY{n}{run}\PY{p}{(}
                        \PY{p}{[}\PY{n}{boxes}\PY{p}{,} \PY{n}{scores}\PY{p}{,} \PY{n}{classes}\PY{p}{,} \PY{n}{num\PYZus{}detections}\PY{p}{]}\PY{p}{,}
                        \PY{n}{feed\PYZus{}dict}\PY{o}{=}\PY{p}{\PYZob{}}\PY{n}{image\PYZus{}tensor}\PY{p}{:} \PY{n}{image\PYZus{}np\PYZus{}expanded}\PY{p}{\PYZcb{}}\PY{p}{)}
                    \PY{c+c1}{\PYZsh{}print(\PYZsq{}viz and saving \PYZsq{}+image\PYZus{}path)}
                    \PY{n}{vis\PYZus{}util}\PY{o}{.}\PY{n}{visualize\PYZus{}boxes\PYZus{}and\PYZus{}labels\PYZus{}on\PYZus{}image\PYZus{}array}\PY{p}{(}
                        \PY{n}{image\PYZus{}np}\PY{p}{,}
                        \PY{n}{np}\PY{o}{.}\PY{n}{squeeze}\PY{p}{(}\PY{n}{boxes}\PY{p}{)}\PY{p}{,}
                        \PY{n}{np}\PY{o}{.}\PY{n}{squeeze}\PY{p}{(}\PY{n}{classes}\PY{p}{)}\PY{o}{.}\PY{n}{astype}\PY{p}{(}\PY{n}{np}\PY{o}{.}\PY{n}{int32}\PY{p}{)}\PY{p}{,}
                        \PY{n}{np}\PY{o}{.}\PY{n}{squeeze}\PY{p}{(}\PY{n}{scores}\PY{p}{)}\PY{p}{,}
                        \PY{n}{category\PYZus{}index}\PY{p}{,}
                        \PY{n}{use\PYZus{}normalized\PYZus{}coordinates}\PY{o}{=}\PY{n+nb+bp}{True}\PY{p}{,}
                        \PY{n}{line\PYZus{}thickness}\PY{o}{=}\PY{l+m+mi}{6}\PY{p}{)}
                    \PY{n}{out\PYZus{}img\PYZus{}path} \PY{o}{=} \PY{n}{PATH\PYZus{}TO\PYZus{}OUTPUT\PYZus{}DIR}\PY{o}{+}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{/}\PY{l+s+s1}{\PYZsq{}}\PY{o}{+}\PY{n}{image\PYZus{}path}\PY{o}{.}\PY{n}{split}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{/}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{[}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{]}
                    \PY{n}{plt}\PY{o}{.}\PY{n}{imsave}\PY{p}{(}\PY{n}{out\PYZus{}img\PYZus{}path}\PY{p}{,} \PY{n}{image\PYZus{}np}\PY{p}{)}
\end{Verbatim}


    The above code creates the output in the output dirtectory specified in
the tf\_utils.py file, in this case it is
\texttt{gtsdb\_data/faster\_rcnn\_inception\_resnet\_v2\_atrous/outputs/}.

Visualizing few of the images below,

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor} }]:} \PY{n}{out\PYZus{}glob} \PY{o}{=} \PY{n}{glob}\PY{o}{.}\PY{n}{glob}\PY{p}{(}\PY{p}{(}\PY{n}{os}\PY{o}{.}\PY{n}{path}\PY{o}{.}\PY{n}{join}\PY{p}{(}\PY{n}{PATH\PYZus{}TO\PYZus{}OUTPUT\PYZus{}DIR}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{*.png}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{)}\PY{p}{)}
        \PY{n}{idxs} \PY{o}{=} \PY{n+nb}{set}\PY{p}{(}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{4}\PY{p}{,}\PY{l+m+mi}{9}\PY{p}{,}\PY{l+m+mi}{12}\PY{p}{,}\PY{l+m+mi}{25}\PY{p}{,}\PY{l+m+mi}{34}\PY{p}{,}\PY{l+m+mi}{44}\PY{p}{,}\PY{l+m+mi}{51}\PY{p}{,}\PY{l+m+mi}{80}\PY{p}{]}\PY{p}{)}
        \PY{k}{for} \PY{n}{idx}\PY{p}{,} \PY{n}{image\PYZus{}path} \PY{o+ow}{in} \PY{n+nb}{enumerate}\PY{p}{(}\PY{n}{out\PYZus{}glob}\PY{p}{)}\PY{p}{:}
            \PY{n}{img\PYZus{}num} \PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{n}{image\PYZus{}path}\PY{o}{.}\PY{n}{split}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{/}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{[}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{]}\PY{o}{.}\PY{n}{split}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{.}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{)}
            \PY{k}{if} \PY{n}{img\PYZus{}num} \PY{o+ow}{in} \PY{n}{idxs}\PY{p}{:}
                \PY{n}{image} \PY{o}{=} \PY{n}{Image}\PY{o}{.}\PY{n}{open}\PY{p}{(}\PY{n}{image\PYZus{}path}\PY{p}{)}
                \PY{n}{plt}\PY{o}{.}\PY{n}{figure}\PY{p}{(}\PY{n}{idx}\PY{p}{,} \PY{n}{figsize}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{100}\PY{p}{,}\PY{l+m+mi}{100}\PY{p}{)}\PY{p}{)}
                \PY{n}{plt}\PY{o}{.}\PY{n}{axis}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{off}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
                \PY{n}{plt}\PY{o}{.}\PY{n}{imshow}\PY{p}{(}\PY{n}{image}\PY{p}{)}
\end{Verbatim}


    \begin{center}
    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_11_0.png}
    \end{center}
    { \hspace*{\fill} \\}
    
    \begin{center}
    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_11_1.png}
    \end{center}
    { \hspace*{\fill} \\}
    
    \begin{center}
    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_11_2.png}
    \end{center}
    { \hspace*{\fill} \\}
    

    % Add a bibliography block to the postdoc
    
    
    
    \end{document}