From 70e0239d6c9a1648ae2bc720f3ca5f1de3e37ecc Mon Sep 17 00:00:00 2001 From: Arcnilya Date: Thu, 17 Mar 2022 19:00:15 +0100 Subject: [PATCH] paper update with images --- img/loaded_images1.png | Bin 0 -> 446359 bytes img/no_batch_transform1.png | Bin 0 -> 423957 bytes img/overfitting2.png | Bin 0 -> 72165 bytes img/with_batch_transform1.png | Bin 0 -> 410771 bytes img/with_batch_transform2.png | Bin 0 -> 395663 bytes img/with_vs_without_batch_transforms.png | Bin 0 -> 48210 bytes ref.bib | 9 ++- wAiFu.aux | 35 ++++++---- wAiFu.bbl | 7 ++ wAiFu.blg | 60 ++++++++--------- wAiFu.log | 80 +++++++++++++++++------ wAiFu.out | 9 +-- wAiFu.pdf | Bin 527373 -> 1281908 bytes wAiFu.tex | 30 +++++++-- 14 files changed, 158 insertions(+), 72 deletions(-) create mode 100644 img/loaded_images1.png create mode 100644 img/no_batch_transform1.png create mode 100644 img/overfitting2.png create mode 100644 img/with_batch_transform1.png create mode 100644 img/with_batch_transform2.png create mode 100644 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\begin{thebibliography}{1} +\bibitem{zoom-advanced} +foobar167. +\newblock Github: zoom\_advanced.py. +\newblock + \url{https://github.com/foobar167/junkyard/blob/master/zoom\_advanced.py}, + March 2022. + \bibitem{fastai} Jeremy Howard and Sylvain Gugger. \newblock Fastai: {A} layered {API} for deep learning. diff --git a/wAiFu.blg b/wAiFu.blg index 2186c5e..2b2810e 100644 --- a/wAiFu.blg +++ b/wAiFu.blg @@ -1,48 +1,48 @@ This is BibTeX, Version 0.99d (TeX Live 2019/Debian) Capacity: max_strings=200000, hash_size=200000, hash_prime=170003 -The top-level auxiliary file: main.aux +The top-level auxiliary file: wAiFu.aux The style file: plain.bst Database file #1: ref.bib Warning--empty journal in machinelearning -You've used 3 entries, +You've used 4 entries, 2118 wiz_defined-function locations, - 517 strings with 4258 characters, -and the built_in function-call counts, 904 in all, are: -= -- 86 -> -- 44 + 522 strings with 4384 characters, +and the built_in function-call counts, 1114 in 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fastai, a layered API for deep learning~\cite{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 images individually) and batch transforms (transforms applied to each batch during training). It is also responsible of splitting the dataset into various parts: \emph{train, validation} and \emph{test}. \section{Implementation} \label{sec:implementation} The code of all tools in Project Hentai AI is open source and can be found at \url{https://git.hentai-ai.org}. @@ -144,9 +144,10 @@ 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 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. +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 % 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). @@ -160,6 +161,7 @@ The name of the label application is ``Hentai Tinder''\\(cred. Hood Classic\#886 \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 +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 @@ -168,15 +170,35 @@ The output of the Hentai Tinder application is a csv file which can be easily us \section{Results} \label{sec:results} % Two more csv files % 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} +\begin{figure} + \fbox{\includegraphics[width=.5\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}} + \caption{Training image with batch transforms} + \label{fig:wbt} +\end{figure} + +\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} + \label{fig:btgraph} +\end{figure} \subsection{Limitations} \label{sec:limitations} The size of the lewd anime thighs dataset is only 1000 images. -The small dataset is due to the time-consuming task of manually crop and label the dataset. +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. \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 give more thought to 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. \section{Conclusion} -- 2.25.1

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