Can you please write the conclusion for this research article given the introduction and the experiments?
Please use an appropriate writing style that matches the one from the given text.
Here are the introduction and experiments.

\section{Introduction} 

The field of computer vision relies heavily on benchmarking neural networks on standard, well-studied datasets, such as ImageNet, CIFAR-10 and CIFAR-100. Our goal was to use basic convolutional nets as baselines on the CIFAR-10 dataset, and then to try to improve on these baselines. Two models were used in this study.

The first model, VGG \cite{vgg}, is a very simple architecture. We have used a VGG-9 architecture, with 6 convectional layers and 3 fully connected layers. Different option were computed. The basic model is the simple VGG-9 with momentum. Then several option where combined between dropout, weight decay, data augmentation and batch normalization. 

Second, the original ResNet \cite{resnet} provides our baseline on CIFAR-10. Our goal was first to match its performance, then add improvements to the network.

\section{Experiments} 

\subsection{VGG}
\paragraph{}
Different models have been trained. We first tried to add dropout layers with prevent from over-fitting. Then we used data augmentation for the model to be more robust, weight decay, and batch normalization. The following table presents the accuracy on the test set. 

\begin{table}[h]
\centering
    \begin{tabular}{|c|c|c|c|c|c|c|}
        \hline
        Model & Basic & D & L2 & DA &  D + DA & D + DA + BN \\
        \hline
        Number of epochs & 100 & 100 & 100 & 200 & 200 & 200 \\
        \hline
        Test accuracy & 74.47\% & 82.98\% &72.04\% & 84.26\% & 85.59\% & 86.82\% \\
        \hline
    \end{tabular}
    \caption{VGG network accuracy on the test set for different model with a fixed learning rate of 0.001}
    \label{tab:VGG1}
\end{table}
All the model were run with the basic model with a momentum of 0.9. D = Dropout, DA = data augmentation, BN = batch normalization.

\paragraph{}
We also look if the preparation of the pixel from normalization between 0 and 1 to removing the mean and divide by standard deviation can improve the accuracy. 

\begin{table}[h]
\centering
    \begin{tabular}{|c|c|c|c|c|c|c|}
        \hline
        Model & Basic & D & L2  & D + DA + BN \\
        \hline
        Number of epochs & 100 & 100 & 100 & 200 \\
        \hline
        Test accuracy & 73.74\% & 83.35\% & 75.59\% & 84.26\% 88.44\% & 86.82\% \\
        \hline
    \end{tabular}
    \caption{VGG network accuracy on the test set for different model with a fixed learning rate and normally distributed input data}
    \label{tab:VGG1}
\end{table}

\subsection{ResNet}

For ResNet-20, our hyperparameter tuning involved looking for a good training time, weight decay strength (the original paper used 1e-4) and learning rate schedule. In the original paper, the learning rate divided by 10 at 32k iterations, then by 10 again at 48k, and ran for 64k iterations, which, with a batch size of 128, is approximately equivalent to 180 epochs. Based on the validation set, our best weight decay strength was 5e-4 (SUBJECT TO REVISION) and the learning rate schedule was a modified exponential decay: $\eta_{\text{ep}} = 0.87^{\lfloor \text{ep} /5\rfloor}\eta_{\text{init}}$ (SUBJECT TO REVISION) where $\text{ep}$ is the epoch. We kept the 180-epoch training time (SUBJECT TO REVISION).

The only data augmentation used in the original paper was reproduced: adding 4 pixels of zero padding on every side of the image, then randomly cropping a $32\times 32$ crop from the enlarged image, with a random chance of horizontally flipping the image. We got rid of the horizontal flip, as it doubled the training time without improving the validation accuracy.