Deep Learning for Clothing Style Recognition Using YOLOv5

Abstract

With the rapid development of artificial intelligence, much more attention has been paid to deep learning. However, as the complexity of learning algorithms increases, the needs of computation power of hardware facilities become more crucial. Instead of the focus being on computing devices like GPU computers, a lightweight learning algorithm could be the answer for this problem. Cross-domain applications of deep learning have attracted great interest amongst researchers in academia and industries. For beginners who do not have enough support with software and hardware, an open-source development environment is very helpful. In this paper, a relatively lightweight algorithm YOLOv5s is addressed, and the Google Colab is used for model training and testing. Based on the developed environment, many state-of-art learning algorithms can be studied for performance comparisons. To highlight the benefits of one-stage object detection algorithms, the recognition of clothing styles is investigated. The image samples are selected from datasets of fashion clothes and the web crawling of online stores. The image data are categorized into five groups: plaid; plain; block; horizontal; and vertical. Average precison, mean average precison, recall, F1-score, model size, and frame per second are the metrics used for performance validations. From the experimental outcomes, it shows that YOLOv5s is better than other learning algorithms in the recognition accuracy and detection speed.

Video Classification of Cloth Simulations: Deep Learning and Position-Based Dynamics for Stiffness Prediction

“…This method achieved an admirable accuracy rate of 90%. On the other hand, Chang et al [ 30 ] harnessed region-based convolutional neural networks (R-CNN) in conjunction with YOLOv5s for feature extraction and fabric classification, culminating in a remarkable accuracy rate of 97%. It is worth noting, however, that both approaches confront inherent limitations stemming from the relatively modest size of their training datasets.…”

Two-Stage Method for Clothing Feature Detection

“…These methods combine region proposal and classification into a single step, significantly increasing processing speed while maintaining reasonable accuracy. Recent advancements in object detection have seen the YOLOv5 algorithm being extensively applied across various domains, particularly in clothing recognition [26,27] and personal protective equipment (PPE) detection, showcasing its versatility and efficiency. The YOLOv5 algorithm, known for its speed and accuracy, has been adapted and improved to meet the specific needs of different applications.…”

Future Cities Carbon Emission Models: Hybrid Vehicle Emission Modelling for Low-Emission Zones

“…Google Colaboratory, often referred to as Colab, is a free cloud-based platform for working with Jupyter notebooks offered by Google [20]. Colab allows access to popular libraries and tools for machine learning and data analysis, such as TensorFlow, PyTorch, or Pandas, without the need to install and configure a local programming environment [21,22]…”