Description:

The Synthetic Rock Paper Scissors Dataset offers a rich and diverse collection of images tailored for training and testing machine learning models on the classic game of Rock Paper Scissors. This dataset includes synthetic images generated through advanced data augmentation techniques, ensuring a wide variety of samples to enhance model training. Each of the three classes—Rock, Paper, and Scissors—is well-represented, providing balanced and comprehensive examples. The augmentation processes involve transformations such as rotation, scaling, and color adjustments, simulating real-world conditions to improve model robustness and generalization.

Download Dataset Designed for researchers and developers, this dataset serves as an excellent resource for developing and evaluating convolutional neural networks (CNNs) and other machine learning models in image classification tasks. The diverse and augmented nature of the images ensures that models trained on this dataset can effectively recognize and classify Rock, Paper, and Scissors in various scenarios. By leveraging this dataset, developers can advance AI capabilities in game-based recognition and contribute to broader applications in image classification and pattern recognition.

This dataset is sourced from Kaggle.