Automated_weather_classification_using _transfer_learning

Automated weather classification using transfer learning is a Deep learning approach that leverages pre-trained models to accurately classify weather conditions. Transfer learning involves utilizing the knowledge gained from training on one task and applying it to a different but related task. In this case, a pre-trained model, such as a convolutional neural network (CNN), is used to extract features from weather images, which are then fed into a classification model. This approach saves time and computational resources by avoiding training a model from scratch. By fine-tuning the pre-trained model with weather-specific data, it can effectively classify weather conditions such as sunny, rainy, cloudy, or snowy, among others.