Comparative Analysis of Different Methods for Classifying Polychromatic Sketches

Fahd Baba, Devon Mack
4/11/2025
cs.CV

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Abstract

Image classification is a significant challenge in computer vision, particularly in domains humans are not accustomed to. As machine learning and artificial intelligence become more prominent, it is crucial these algorithms develop a sense of sight that is on par with or exceeds human ability. For this reason, we have collected, cleaned, and parsed a large dataset of hand-drawn doodles and compared multiple machine learning solutions to classify these images into 170 distinct categories. The best model we found achieved a Top-1 accuracy of 47.5%, significantly surpassing human performance on the dataset, which stands at 41%.

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Cite this paper

@article{baba2025comparative,
  title  = {Comparative Analysis of Different Methods for Classifying Polychromatic Sketches},
  author = {Fahd Baba and Devon Mack},
  year   = {2025},
  eprint = {2504.08186},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.08186v1}
}

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