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Anas El Korchi1, Youssef Ghanou1

  • 1University Moulay Ismail of Meknes, Morocco.

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This study introduces a synthetic dataset of 2D geometric shapes for machine learning. The dataset features 9 shape classes with 10,000 images each, ideal for classification and clustering tasks.

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Area of Science:

  • Computer Science
  • Machine Learning
  • Data Science

Background:

  • Machine learning models require diverse and clean datasets for effective training.
  • Synthetic datasets offer a controlled environment to study model behavior without real-world noise.

Purpose of the Study:

  • To introduce a novel, synthetic dataset of 2D geometric shapes for machine learning applications.
  • To provide a clean and well-defined dataset for classification and clustering tasks.
  • To enable the study of machine learning model performance independent of dataset-specific noise or biases.

Main Methods:

  • Generation of 9 classes of 2D geometric shapes (Triangle, Square, Pentagon, Hexagon, Heptagon, Octagon, Nonagon, Circle, Star).
  • Each shape is randomly placed, rotated (-180° to 180°), and colored on a 200x200 RGB image.
  • Each class contains 10,000 images, with random background and shape fill colors.
  • Source code for data generation is provided via a GitHub URL.

Main Results:

  • A comprehensive dataset of 90,000 synthetic 2D geometric shape images across 9 distinct classes.
  • The dataset is characterized by random variations in shape position, rotation, and color, along with random background colors.
  • The generator code is available, allowing for the creation of custom dataset sizes.

Conclusions:

  • The proposed synthetic dataset offers a valuable resource for developing and evaluating machine learning algorithms, particularly for classification and clustering.
  • Its synthetic nature allows for unbiased analysis of model performance and understanding of pattern recognition capabilities.
  • The availability of the generation code promotes reproducibility and further research in synthetic data generation for AI.