Comparative Analysis of Different Methods for Classifying Polychromatic Sketches
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Abstract (arXiv)
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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References · 7
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
- 2014How transferable are features in deep neural networks?cited by 1,225
- 2017A Neural Representation of Sketch Drawingscited by 313
- 2024Do Generalised Classifiers really work on Human Drawn Sketches?cited by 2
- 2025An Analytical Model for Overparameterized Learning Under Class Imbalancecited by 2
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