The project includes: Single Image Augmentation: Demonstrates various image augmentation techniques such as flipping, grayscale conversion, saturation adjustment, brightness adjustment, rotation, and ...

Data augmentation enhances deep learning models by artificially increasing the training dataset through transformations like rotation, scaling, flipping, or adding noise. This helps models ...

Performance of the data augmentation techniques was studied using state-of-the-art transfer learning techniques, for instance, VGG16, ResNet, and InceptionV3. An extensive simulation shows that the ...

In this approach, we represent the dataset as a tf Dataset object, then apply various transformations including data augmentation. For performance benefit, data augmentation should be done using ...

1. MNIST – One of the popular deep learning datasets of handwritten digits which consists of sixty thousand training set examples, and ten thousand test set examples. The time spent in data pre ...

One of the difficulties in deep learning is making a model with small dataset. Overfitting, outliers, low accuracy, noise, and general poor performance often caused by lack of information given to the ...

We propose strongly unrealistic data augmentation to improve the robustness of convolutional neural networks (CNNs) for automatic classification of dopamine transporter SPECT against the variability ...