The system in this research uses transfer learning to categorise the many forms of weather. These weather conditions are primarily divided into 5 groups: cloudy, sunny, rainy, foggy, and …

The integration of transfer learning techniques has revolutionized automated weather classification, addressing challenges related to limited labeled data and complex feature …

To enhance user accessibility, a Flask-based web interface was developed, allowing real-time image uploads and instant weather classification. The results demonstrate that transfer …

Jul 27, 2022 · Thanks to transfer learning it is possible to create image classification solutions using a small dataset. In this paper three weather recognition models are proposed. These …

A transfer learning (TL) based multi-classification model has been developed to classify and recognize collected weather dataset with 1000 different weather images belonging to four …

Contribute to Revathy26/Automated-weather-classification-using-transfer-learning development by creating an account on GitHub. Contribute to Revathy26/Automated-weather-classification …

Deep learning methods, particularly transfer learning using VGG19 is employed for high-performance weather classification. Accurate weather classification has far-reaching …

This research proposes a deep learning approach leveraging transfer learning with the pre-trained InceptionV3 model to classify five distinct weather conditions: cloudy, foggy, rainy, sunny, and …

Automated Weather Classification system that utilizes Transfer Learning techniques. The aim of this project was to develop an efficient and accurate system for classifying weather conditions …

We provide an efficient classification model that employ the transfer learning technique for ResNet-18 CNN that is pre-trained with ImageNet dataset to learn the new features of the …