Implementing VGG-16 CNN on CIFAR-10 dataset
For this task, I will design and implement a CNN architecture for the CIFAR-10 dataset based on the VGG16 architecture. The VGG16 architecture is a widely used CNN architecture that was developed by the Visual Geometry Group at the University of Oxford. It is known for its good performance on image classification tasks and has been used as a baseline in many studies.
https://arxiv.org/pdf/1409.1556.pdf - VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
The architecture of the network is shown in the diagram below:
To prepare the datasets for training, I will first download the CIFAR-10 dataset from the website and split it into training, validation, and test sets. I will then apply standard data augmentation techniques, such as random cropping, horizontal flipping, and normalization, to the training set in order to increase the diversity of the data and improve the generalization ability of the model.
The main architectural elements of the VGG16 network are a series of convolutional layers followed by max pooling layers, and a fully connected layer at the end. The convolutional layers are used to extract features from the input images, and the max pooling layers are used to down-sample the feature maps and reduce the dimensionality of the data. The fully connected layer is used to make the final classification.
One of the main contributions of the VGG16 architecture is its use of small convolutional filters (3x3) and a large number of filters, which allows it to learn more detailed and fine-grained features from the images. It also uses max pooling layers to reduce the spatial resolution of the feature maps and reduce the number of parameters, which helps to prevent overfitting.
To obtain the hyperparameters for my model, I will conduct a grid search to find the combination of hyperparameters that gives the best performance on the validation set. Specifically, I will experiment with different learning rates, batch sizes, and dropout rates, and evaluate the performance of the model on the validation set for each combination. I will then choose the combination of hyperparameters that gives the highest validation accuracy as the final model.