Can you please write the conclusion to this scientific report from the given introduction section and experiments section? Use an appropriate writing style that matches the one from the text.

\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. ResNet is designed to overcome the degradation problem in deeper networks, where even training error degrades with depth. The architecture is based on the idea of residual learning: the network learns how to balance an identity mapping (which cannot degrade performance) with a residual function learned in the convolutional layers. Figure \ref{fig:residual_block} illustrates how this is implemented in 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. 

All the model were run with the basic model with a momentum of 0.9 with options D = Dropout, DA = data augmentation, BN = batch normalization.

\subsubsection{Normalization with 0-mean and 1-std}
\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. 

All the model were run with the basic model with a momentum of 0.9 with options D = Dropout, DA = data augmentation, BN = batch normalization.

\paragraph{}
Comparing to the scaling pixel between 0 and 1, there is a slitly better accuracy on the test dataset, but not a real improvement. 

\subsubsection{Adam and AdamW}
\paragraph{}
Now let's look at different learning rate scheduler. We do not used SDG with momentum. Instead we will use Adam and AdamW optimizers. 

\paragraph{}
We can observe a sharp increase in the accuracy on test dataset compared to Table 1. The model is way better as it can reach now more than 90\% of accuracy. 


\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 was 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.