A deep transfer learning based convolution neural network framework for air temperature classification using human clothing images.

ABSTRACT: Weather recognition is crucial due to its significant impact on various aspects of daily life, such as weather prediction, environmental monitoring, tourism, and energy production. Several studies have already conducted research on image-based weather recognition. However, previous studies have addressed few types of weather phenomena recognition from images with insufficient accuracy. In this paper, we propose a transfer learning CNN framework for classifying air temperature levels from human clothing images. The framework incorporates various deep transfer learning approaches, including DeepLabV3 Plus for semantic segmentation and others for classification such as BigTransfer (BiT), Vision Transformer (ViT), ResNet101, VGG16, VGG19, and DenseNet121. Meanwhile, we have collected a dataset

SUBMITTER: Ahmed M 

PROVIDER: S-EPMC11685903 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature