Comparative Analysis of Different Methods for Classifying Polychromatic Sketches
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912320640253952 |
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| author | Baba, Fahd Mack, Devon |
| author_facet | Baba, Fahd Mack, Devon |
| contents | 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%. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_08186 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative Analysis of Different Methods for Classifying Polychromatic Sketches Baba, Fahd Mack, Devon Computer Vision and Pattern Recognition 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%. |
| title | Comparative Analysis of Different Methods for Classifying Polychromatic Sketches |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.08186 |