The Ekush dataset has been applied in our work which is publicly available at https://shahariarrabby.github.io/ekush/. There are two proposed methods- DCGAN(Deep Convolutional Generative Adversarial Network ) and Outlier. The outlier removed classes dataset has been given as named Dataset(outlier_removed).zip. For DCGAN, five classes have been used as there contains more imbalanced dataset. DCGAN_Generated_Images.zip contains the datasets of DCGAN generated images. Three datasets have been classified in this experiment using the ResNet-50 classifier. These three approaches datasets are split into three sets - train, validation, and test. First, the original dataset found in Dataset(original).zip has been used in the classifier. Then the outlier removed classes created in Dataset(outlier_removed).zip with the rest of the original dataset has used. Finally, together with DCGAN generate images and outlier removed classes created in Dataset(DCGAN_applied).zip with the other classes of the dataset used in the classifier.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Dataset and resulting model of Japanese Kamon trained using StarGAN.

The dataset used for training the generative adversarial network (GAN) model.

GAN SMT Res Augmented Dataset

## Overview

GAN SMT Res Augmented Dataset is a dataset for object detection tasks - it contains Defects annotations for 2,384 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).