Deep Learning for Temporal Weather Patterns: ResNet-50 Perspectives

The classification of weather and time has a wide range of applications, some of which include improving the accuracy of weather forecasting, enhancing the performance of autonomous vehicles, and optimizing the planning and activities that take place outside considering environmental impact and improving quality of life. Utilizing the ResNet-50 neural network architecture and making use of the CCF BDCI dataset, which is comprised of 2600 photos that have been manually tagged, investigate the categorization of weather and time in this study. Dataset has divided these photographs into five distinct weather conditions: overcast, sunny, rainy, snowy, and foggy. Additionally, images have been divided them into five distinct time periods: dawn, morning, afternoon, dusk, and night. Initially, the ResNet-50 model was trained for a total of twenty epochs. This model had previously been pre-trained on the ImageNet dataset and had been fine-tuned for particular classification objective. Following that, the training period was extended to a total of fifty epochs in order to assess the effect that further training had on the overall performance of the model. During the entirety of the training process, crucial parameters such as accuracy and loss were monitored on both the training dataset and the validation dataset. The purpose of this research is to demonstrate that the ResNet-50 model is capable of accurately categorizing weather and time circumstances, as well as to evaluate the ways in which longer training epochs contribute to enhanced classification accuracy and overall model robustness.