Update from X1

diff --git a/csv/test1.csv b/csv/test1.csv
new file mode 100644 (file)
index 0000000..15ab9f2
--- /dev/null
+++ b/csv/test1.csv
@@ -0,0 +1,1007 @@
+fname,label\r
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diff --git a/ref.bib b/ref.bib
index f5db0f941f4c2bcf3bbcb7ac99680059b199f214..4a6f5f589c1b674e70383f1b10c213baafbaff79 100644 (file)
--- a/ref.bib
+++ b/ref.bib
@@ -49,3 +49,10 @@ month = mar,
 year = {2022},
 howpublished={\url{www.reddit.com/r/thighdeology}}
 }
+@MISC{tkinter,
+author = {Python Documentation},
+title = {TKinter},
+month = mar,
+year = {2022},
+howpublished={\url{docs.python.org/3/library/tkinter.html}}
+}

diff --git a/wAiFu.aux b/wAiFu.aux
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-\bibcite{fastai}{2}
-\bibcite{deeplearning}{3}
-\bibcite{machinelearning}{4}
-\bibcite{thighdeology}{5}
+\bibcite{tkinter}{1}
+\bibcite{zoom-advanced}{2}
+\bibcite{fastai}{3}
+\bibcite{deeplearning}{4}
+\bibcite{machinelearning}{5}
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diff --git a/wAiFu.bbl b/wAiFu.bbl
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--- a/wAiFu.bbl
+++ b/wAiFu.bbl
@@ -1,5 +1,10 @@
 \begin{thebibliography}{1}
 
+\bibitem{tkinter}
+Python Documentation.
+\newblock Tkinter.
+\newblock \url{docs.python.org/3/library/tkinter.html}, March 2022.
+
 \bibitem{zoom-advanced}
 foobar167.
 \newblock Github: zoom\_advanced.py.

diff --git a/wAiFu.blg b/wAiFu.blg
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--- a/wAiFu.blg
+++ b/wAiFu.blg
@@ -4,45 +4,45 @@ The top-level auxiliary file: wAiFu.aux
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 ** Conference Paper **
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diff --git a/wAiFu.out b/wAiFu.out
index 0f822e28336d449a43e7f22d30617044ad23c9b7..69c40106fe5d2ee1d1e5bad62d55ec4e2f6e8919 100644 (file)
--- a/wAiFu.out
+++ b/wAiFu.out
@@ -3,22 +3,21 @@
 \BOOKMARK [2][-]{subsection.2.1}{Artificial Intelligence}{section.2}% 3
 \BOOKMARK [2][-]{subsection.2.2}{Hentai and Thighdeology}{section.2}% 4
 \BOOKMARK [1][-]{section.3}{Method}{}% 5
-\BOOKMARK [2][-]{subsection.3.1}{wAiFu Framework}{section.3}% 6
-\BOOKMARK [1][-]{section.4}{Design}{}% 7
-\BOOKMARK [2][-]{subsection.4.1}{Data Collection}{section.4}% 8
-\BOOKMARK [2][-]{subsection.4.2}{Data Preparation}{section.4}% 9
-\BOOKMARK [2][-]{subsection.4.3}{Data Labeling}{section.4}% 10
-\BOOKMARK [2][-]{subsection.4.4}{fastai}{section.4}% 11
-\BOOKMARK [1][-]{section.5}{Implementation}{}% 12
-\BOOKMARK [2][-]{subsection.5.1}{Data Preparation}{section.5}% 13
-\BOOKMARK [3][-]{subsubsection.5.1.1}{Convert and Rename}{subsection.5.1}% 14
-\BOOKMARK [3][-]{subsubsection.5.1.2}{Cropping Images}{subsection.5.1}% 15
-\BOOKMARK [2][-]{subsection.5.2}{Label App: Hentai Tinder}{section.5}% 16
-\BOOKMARK [2][-]{subsection.5.3}{Deep Learning with fastai}{section.5}% 17
-\BOOKMARK [1][-]{section.6}{Results}{}% 18
-\BOOKMARK [1][-]{section.7}{Discussion}{}% 19
-\BOOKMARK [2][-]{subsection.7.1}{Transforms}{section.7}% 20
-\BOOKMARK [2][-]{subsection.7.2}{Limitations}{section.7}% 21
-\BOOKMARK [2][-]{subsection.7.3}{Future Work}{section.7}% 22
-\BOOKMARK [1][-]{section.8}{Conclusion}{}% 23
-\BOOKMARK [1][-]{section*.2}{References}{}% 24
+\BOOKMARK [1][-]{section.4}{Design}{}% 6
+\BOOKMARK [2][-]{subsection.4.1}{Data Collection}{section.4}% 7
+\BOOKMARK [2][-]{subsection.4.2}{Data Preparation}{section.4}% 8
+\BOOKMARK [2][-]{subsection.4.3}{Data Labeling}{section.4}% 9
+\BOOKMARK [2][-]{subsection.4.4}{fastai}{section.4}% 10
+\BOOKMARK [1][-]{section.5}{Implementation}{}% 11
+\BOOKMARK [2][-]{subsection.5.1}{Data Preparation}{section.5}% 12
+\BOOKMARK [3][-]{subsubsection.5.1.1}{Convert and Rename}{subsection.5.1}% 13
+\BOOKMARK [3][-]{subsubsection.5.1.2}{Cropping Images}{subsection.5.1}% 14
+\BOOKMARK [2][-]{subsection.5.2}{Label App: Hentai Tinder}{section.5}% 15
+\BOOKMARK [2][-]{subsection.5.3}{Deep Learning with fastai}{section.5}% 16
+\BOOKMARK [1][-]{section.6}{Results}{}% 17
+\BOOKMARK [1][-]{section.7}{Discussion}{}% 18
+\BOOKMARK [2][-]{subsection.7.1}{Transforms}{section.7}% 19
+\BOOKMARK [2][-]{subsection.7.2}{Limitations}{section.7}% 20
+\BOOKMARK [2][-]{subsection.7.3}{Future Work}{section.7}% 21
+\BOOKMARK [1][-]{section.8}{Conclusion}{}% 22
+\BOOKMARK [1][-]{section*.2}{References}{}% 23

diff --git a/wAiFu.pdf b/wAiFu.pdf
index 228fb6733981269ecb4bffad3881a30240076f7c..da5a8c82b25efa0680ba2b7bd2306108710dce7e 100644 (file)
Binary files a/wAiFu.pdf and b/wAiFu.pdf differ

diff --git a/wAiFu.tex b/wAiFu.tex
index bcaf46ab4c5ed093839f16bf4a38884680524d7e..70fda4dc2a15a1ee17dd033bb7c08777a7923d7c 100644 (file)
--- a/wAiFu.tex
+++ b/wAiFu.tex
@@ -6,6 +6,7 @@
 \usepackage{textcomp}
 \usepackage{xcolor}
 \usepackage{url}
+\usepackage{listings}
 \usepackage[colorlinks=true, urlcolor=cyan]{hyperref}
 \def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em
     T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}}
@@ -31,8 +32,8 @@ deep learning, DL, machine learning, ML, artificial intelligence, AI, thighs, th
 \end{IEEEkeywords}
 
 \section{Introduction} \label{sec:intro}
-It all began when a friend started reviewing anime thighs sent their way. The reviews were simply approved or disapproved, but the surprisingly low amount of approved images sparked the idea of a machine learning model capable of learning an individual's taste in anime thighs.
-% Add more here
+It all began when a friend started reviewing anime thighs sent their way. The reviews were simply approved or disapproved, but the surprisingly low amount of approved images sparked the idea of a machine learning model capable of learning an individual's taste in anime thighs. By feeding a model images and their respective rating a model could be able to learn an individual's subjective taste.
+% More here
 The framework of wAiFu is not limited to lewd anime thighs, but can very easily be extended to other areas e.g., tits, ass, abs, middriffs and armpits.
 
 \section{Background} \label{sec:background}
@@ -62,7 +63,7 @@ For the purpose of this study and future studies in Project Hentai AI, the data
 In its simplest definition, Hentai is simply anime and manga pornography and can be seen as the most extreme out of the three. Ecchi on the other hand, when used as an adjective, translates to ``sexy'', ``dirty'' or ``naughty'', and has been used to describe anime and manga with \emph{sexual overtones} (playful sexuality or softcore). Lewd in these studies is defined as \emph{sexual undertones}. A detailed differentiation between these three categories is planned for a separate study. Project Hentai AI includes ecchi and lewd as well even though the name of the project uses the term hentai.
 
 Thighdeology is the worship of thick anime thighs which has its Mecca on the Thighdeology subreddit~\cite{thighdeology}. 
-The top two rules on the subreddit are: (1) All images must be thigh-focused and (2) No Pictures of Sex (Nudity is allowed). The second rule is a clear demonstration of the distinction between hentai and ecchi described above. The dataset used for wAiFu is images of lewd anime thighs in accordance with these two top rules.
+The top two rules on the subreddit are: (1) All images must be thigh-focused and (2) No Pictures of Sex (Nudity is allowed). The second rule is a clear demonstration of the distinction between hentai and ecchi described above. The dataset used for wAiFu is images of lewd anime thighs in accordance with these two rules.
 The epigraph which crowns the website says it all:
 \begin{quote}
     \emph{``Blessed is the man that walketh not in the counsel of the ungodly, nor standeth in the way of sinners, nor sitteth in the seat of the scornful. But his delight is in the law of the THICC anime thighs.''}
@@ -70,11 +71,11 @@ The epigraph which crowns the website says it all:
 
 \section{Method} \label{sec:method}
 
-\subsection{wAiFu Framework} \label{sec:waifu}
-Witty Artificial Intelligence Framework Utilization.
-% Talk about the overview of the framework, the main idea
+\emph{wAiFu} stands for Witty Artificial Intelligence Framework Utilization, and its goal is to standardize the process of creating a subjectively labeled dataset for machine learning. This means that a single set of images can be used as separate datasets depending on the subjective labeling. A system is set up for homogenizing the images (filename and file extensions), cropping the images to isolate the area of interest as much as possible and finally labeling the images using a separate file for mapping each filename to its subjective labeling.
+% Maybe more here
 
 \section{Design} \label{sec:design}
+The following section describes the design of wAiFu in its separate parts in detail. The data collection, the data preparation, the data labeling and finally the machine learning API.
 
 \subsection{Data Collection} \label{sec:datacollection}
 \noindent A dataset of lewd anime thighs was manually collected from six separate sources:
@@ -129,8 +130,8 @@ The \emph{Rate labeling} ranks the images on a scale (e.g., 0-10, 1-5, A-F). Thi
 The data labeling implementation is detailed in Section~\ref{sec:impl_labelapp}
 
 \subsection{fastai} \label{sec:fastai}
-The AI implementation was using fastai, a deep learning library providing machine learning practitioners with high-level components creating state-of-the-art results in standard deep learning domains~\cite{fastai}. For the purpose of boolean labeling in this project, a single-label classification structure is implemented using various building blocks. The pictures and their labels are loaded into a \emph{DataLoaders} object. This object is responsible for maching labels with images, applying item transforms (transforms applied to each image individually) and batch transforms (transforms applied to each batch during training). It is also responsible of splitting the dataset into various sets: \emph{training, validation} and \emph{testing} (see Figure~\ref{fig:data_sets}). The training set is used to train a given model, which sees and learns from this data. The validation set is used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters. The evaluation becomes more biased as skill on the validation dataset is incorporated into the model configuration. Unlike the training set, the model only occasionally sees this data but never learns from it. The testing set is used to provide an unbiased evaluation of a \emph{final model fit} of the training dataset. 
-% Add something about how fastai splits the dataset using ImageDataLoader
+The AI implementation is using fastai, a deep learning library providing machine learning practitioners with high-level components creating state-of-the-art results in standard deep learning domains~\cite{fastai}. For the purpose of boolean labeling in this project, a single-label classification structure is implemented using various building blocks. The pictures and their labels are loaded into a \emph{DataLoaders} object. This object is responsible for maching labels with images, applying item transforms (transforms applied to each image individually) and batch transforms (transforms applied to each batch during training). It is also responsible of splitting the dataset into various sets: \emph{training, validation} and \emph{testing} (see Figure~\ref{fig:data_sets}). The training set is used to train a given model, which sees and learns from this data. The validation set is used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters. The evaluation becomes more biased as skill on the validation dataset is incorporated into the model configuration. Unlike the training set, the model only occasionally sees this data but never learns from it. The testing set is used to provide an unbiased evaluation of a \emph{final model fit} of the training dataset. 
+% Add something about how fastai loads and splits the dataset using ImageDataLoader
 
 The DataLoaders object is then combined with a model and a metric to create a \emph{Learner} object. The model can be pre-trained, which means that some object and shape recognition can be used as a foundation to train a model for a more specific computer vision problem. This method is called \emph{transfer learning}. The Learner object has a bunch of methods including: \texttt{fine\_tune}, \texttt{predict} and \texttt{export}. The \texttt{fine\_tune} method first freezes all layers except the last one for one cycle (a ``prequel'' epoch), and then unfreezes all layers before running the epochs. This process of freezing and unfreezing layers in the Convolutional Neural Network improves the performance of transfer learning. So using \texttt{fine\_tune(2)} would first run a cycle only adjusting the last layer, then run 2 epochs adjusting all layers. The \texttt{predict} method is simply loading a single image into the model which then predicts the label. This is usually done after the training to sample the accuracy of the model. The \texttt{export} method saves the trained model to a file.
 
@@ -151,48 +152,47 @@ The following section goes through the implementation of homogenizing the datase
 Talk about the script for making the dataset homogeneous. 
 
 \subsubsection{Cropping Images}
-The application for efficiently cropping the images manually was built ontop of a zooming-application~\cite{zoom-advanced} which utilizes tiles for increased performance. The frame border of the application window was set to a 1:1 aspect ratio with desired dimensions and could then easily be used to crop every image from a specified input directory, and put the cropped images in a separate (or in the same) destination directory.
-% Add git link
+The application for efficiently cropping the images manually was built ontop of a zooming-application~\cite{zoom-advanced} which utilizes tiling for increased performance. The frame border of the application window was set to a 1:1 aspect ratio with desired dimensions and could then easily be used to crop every image from a specified input directory, and put the cropped images in a separate (or in the same) destination directory.
 % Add screenshot
 The code is open source and can be found at: \url{https://git.hentai-ai.org/?p=hentai-cropper.git/.git}
 
 \subsection{Label App: Hentai Tinder} \label{sec:impl_labelapp}
-The name of the label application is ``Hentai Tinder''\\(cred. Hood Classic\#8866).
+The name of the label application is ``Hentai Tinder''. It is written in Python using the Tkinter library, a Python binding to the Tk GUI toolkit~\cite{tkinter}. The application loads the images in batches and presents each image to the user one-by-one. The GUI consists of four buttons, inspired by the original Tinder application: \emph{Smash, Pass, Go Back} and \emph{Save}. \emph{Smash} will label the image as ``True'' and \emph{Pass} will label it as ``False''. These are internal boolean values which represents if the user liked the image or not. \emph{Go Back} was added in order for users to change their mind about previous images. The \emph{Save} button simply writes the current results to the output file. The output of the Hentai Tinder application is a csv file which can be used in fastai to create a dataloader with all the images including their labels, see Figure~\ref{fig:csv}.
 % Add screenshot
-% Add git link
-\begin{itemize}
-    \item Tkinter is a Python binding to the Tk GUI toolkit\footnote{\url{https://docs.python.org/3/library/tkinter.html}}
-    \item Load in batches of 10\%
-    \item Smash, Pass, Go Back, Save
-    \item Output file structure
-\end{itemize}
-The output of the Hentai Tinder application is a csv file which can be easily used in fastai to create a dataloader with all the images including their labels.
-% Include the head of a sample csv file
+
+\begin{figure}
+\centering
+\fbox{\lstinputlisting[lastline=6, basicstyle=\ttfamily]{csv/test1.csv}}
+    \caption{Structure of output file using comma separated values}
+    \label{fig:csv}
+\end{figure}
 The code is open source and can be found at: \url{https://git.hentai-ai.org/?p=hentai-tinder.git/.git}
 
 \subsection{Deep Learning with fastai} \label{sec:impl_deeplearning}
-% How was fastai implemented
+% How was fastai implemented, using colab and google drive
 % Add git link
 
 \section{Results} \label{sec:results}
-% Two more csv files
+% Two additional csv files, decriptive statistics
 % Cool graphs of AI performance
-% Compare with/-out transforms, graph of train_loss, valid_loss and error_rate for 15 epochs
 
 \section{Discussion} \label{sec:discussion}
 \subsection{Transforms}
+% Explain what the difference between item_transforma and batch_transform is.
+
 \begin{figure}
-    \fbox{\includegraphics[width=.5\textwidth]{img/no_batch_transform1.png}}
+    \fbox{\includegraphics[width=.45\textwidth]{img/no_batch_transform1.png}}
     \caption{Training image without batch transforms}
     \label{fig:wobt}
 \end{figure}
 
 \begin{figure}
-    \fbox{\includegraphics[width=.5\textwidth]{img/with_batch_transform2.png}}
+    \fbox{\includegraphics[width=.45\textwidth]{img/with_batch_transform2.png}}
     \caption{Training image with batch transforms}
     \label{fig:wbt}
 \end{figure}
 
+% Compare with/-out transforms, graph of train_loss, valid_loss and error_rate, 15 epochs
 \begin{figure}
     \includegraphics[width=.5\textwidth]{img/with_vs_without_batch_transforms.png}
     \caption{Comparing with and without batch transforms on error\_rate, train\_loss and valid\_loss} 
@@ -204,8 +204,10 @@ The size of the lewd anime thighs dataset is only 1000 images.
 This leads to overfitting which can be mitigated by applying transformations
 The small dataset is due to the time-consuming task of manually cropping and labeling the dataset. Since the model is trying to learn an individual's taste, that individual must label the full dataset.
 
+If only 15\% of the dataset is labeled as True, and the model is incapable of getting past 15\% error rate, this could be a sign of the model just trying to say False on every single image... This can be verified using a confusion matrix!
+
 \subsection{Future Work} \label{sec:futurework}
-In order to increase the size of the dataset and thereby obtaining a more robust accuracy from the machine learning model, future research in Project Hentai AI will spend some more focus on automating the collection, transformation and labeling of data.
+In order to increase the size of the dataset and thereby obtaining a more robust accuracy from the machine learning model, future research in Project Hentai AI will spend some more focus on automating the collection, transformation and labeling of data. 
 
 In this study, only boolean labeling was considered when reviewing lewd anime thighs. But even in the world of Hentai thighs are more often than not in a gray-zone as opposed to black or white. A future work in wAiFu would be to extend the labeling application (\emph{Hentai Tinder}) to have a mode or a version capable of using rate labeling on a spectrum. This could be as easy as presenting the user with a 5-star system, similar to reviewing restaurants or hotels, where each image gets rated from 1-5.