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z8G0kANv!8Zen{MEaIxF&H%OJ&Gnc3}*l3l1UM^S2_Pvnfb!Rbe%Dh>R`sa>}%ophd*JShr RAOR6RVLVn=c})ep{|8S^-8BFJ diff --git a/wAiFu.tex b/wAiFu.tex index 70fda4d..679da51 100644 --- a/wAiFu.tex +++ b/wAiFu.tex @@ -131,7 +131,6 @@ The data labeling implementation is detailed in Section~\ref{sec:impl_labelapp} \subsection{fastai} \label{sec:fastai} 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. @@ -169,16 +168,21 @@ The name of the label application is ``Hentai Tinder''. It is written in Python 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, using colab and google drive -% Add git link +The deep learning framework (fastai) was implemented using interactive python notebooks running on Google Colab\footnote{url{https://colab.research.google.com}} connected to Google Drive\footnote{\url{https://drive.google.com}} for storing csv-files, dataset and trained models. +The notebook is open source and can be found at: \url{https://git.hentai-ai.org/?p=waifu-notebook.git/.git} +% TODO add to git \section{Results} \label{sec:results} -% Two additional csv files, decriptive statistics -% Cool graphs of AI performance +\subsection{Justifying Additional Transforms} +One of the main observations when training on such a small dataset was the tendency to overfitting (see Figure~\ref{fig:overfitting}). There are two types of transformations applied to the dataset before training: \texttt{item\_tfms} and \texttt{batch\_tfms}. The item\_tfms for this implementation is using \textit{RandomResizedCrop} which will crop every image randomly to 224x244 with a minimum scaling of 0.75. The batch\_tfms is applying many more tranformations to images in batches between each epoch. These transformations include: zooming, flipping, rotating and changing the brightness. Figure~\ref{fig:wobt} shows how \emph{only} item\_tfms transform the dataset. Figure~\ref{fig:wbt} shows how batch\_tfms additionally transforms the dataset further. Figure~\ref{fig:btgraph} shows the batch\_tfms's effect on error\_rate, train\_loss and valid\_loss. -\section{Discussion} \label{sec:discussion} -\subsection{Transforms} -% Explain what the difference between item_transforma and batch_transform is. + + +\begin{figure} + \fbox{\includegraphics[width=.45\textwidth]{img/overfitting.png}} + \caption{Visualization of overfitting (Andrew Ng's Machine Learning Coursera class)} + \label{fig:overfitting} +\end{figure} \begin{figure} \fbox{\includegraphics[width=.45\textwidth]{img/no_batch_transform1.png}} @@ -192,19 +196,31 @@ The code is open source and can be found at: \url{https://git.hentai-ai.org/?p=h \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} \label{fig:btgraph} \end{figure} -\subsection{Limitations} \label{sec:limitations} -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. +\subsection{Error Rate of Thighs} +The dataset containing 1000 images was labled using Hentai Tinder (Section~\ref{sec:impl_labelapp}) by three individual persons: User A, User B and User C. A table of the result for each user can be seen in Table~\ref{tab:user-table}. The three different users had varying rates of approval on the dataset with user C liking almost half of the dataset. The lowest error\_rate observed came from the dataset labled by user B. With the error\_rate being close to the rate of approval, a sanity check with a confusion matrix showed that the model did not just predict false on the whole dataset. + +\begin{table} +\centering +\begin{tabular}{l|cccccc} + User & Approved & valid err & TP & FP & TN & FN & test err\\ \hline + A & 33.00\% & 26.87\% & 31 & 19 & 116 & 34 \\ + B & 13.22\% & 22\% & 1 & 6 & 76 & 16 \\ + C & 49.30\% & 28.36\% & 72 & 22 & 72 & 34 +\end{tabular} +\caption{User stats} +\label{tab:user-table} +\end{table} -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! +\section{Discussion} \label{sec:discussion} + +\subsection{Limitations} \label{sec:limitations} +The size of the lewd anime thighs dataset is only 1000 images. This leads to overfitting on the training or the validation set which can be mitigated slightly 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 spend some more focus on automating the collection, transformation and labeling of data. -- 2.25.1