# Comparative Analysis of Different Methods for Classifying Polychromatic Sketches

Source: [Comparative Analysis of Different Methods for Classifying Polychromatic Sketches](<https://www.sota2.com/research/paper/68fa77f61e209131cea763c4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:29:27.078Z

Authors: Fahd Baba, Devon Mack

Published: 2025-04-11T01:06:36.000Z

## 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%.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.08186>)

[PDF](<https://arxiv.org/pdf/2504.08186.pdf>)