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Related Experiment Video

Updated: Aug 23, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Deep Learning for Clothing Style Recognition Using YOLOv5.

Yeong-Hwa Chang1,2, Ya-Ying Zhang1

  • 1Department of Electrical Engineering, Chang Gung University, Taoyuan City 333, Taiwan.

Micromachines
|October 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces YOLOv5s, a lightweight deep learning algorithm for efficient object detection. It demonstrates superior accuracy and speed in recognizing clothing styles compared to other models, making it ideal for resource-limited environments.

Keywords:
YOLOclothing style recognitiondeep learningone-stage detection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning advancements necessitate powerful hardware, posing challenges for resource-constrained users.
  • Lightweight algorithms and accessible development environments are crucial for broader deep learning adoption.
  • Cross-domain applications of deep learning are gaining significant traction in both academia and industry.

Purpose of the Study:

  • To evaluate the YOLOv5s algorithm, a lightweight deep learning model, for object detection tasks.
  • To investigate the performance of YOLOv5s in recognizing diverse clothing styles.
  • To demonstrate the utility of Google Colab as an open-source environment for training and testing deep learning models.

Main Methods:

  • Utilized the YOLOv5s algorithm, a one-stage object detection model.
  • Trained and tested the model using Google Colab, an accessible cloud-based platform.
  • Collected and categorized image data of fashion clothing from dedicated datasets and web crawling into five styles: plaid, plain, block, horizontal, and vertical.

Main Results:

  • YOLOv5s achieved high recognition accuracy and fast detection speeds for clothing styles.
  • Performance metrics included average precision, mean average precision, recall, F1-score, model size, and frames per second.
  • Experimental outcomes indicated YOLOv5s outperformed other learning algorithms in accuracy and speed.

Conclusions:

  • YOLOv5s offers an efficient and effective solution for object detection, particularly in scenarios with limited computational resources.
  • The study highlights the advantages of one-stage object detection algorithms for practical applications like fashion analysis.
  • Google Colab provides a viable and supportive open-source environment for developing and comparing deep learning models.