Learn how to classify images in a multi-class weather dataset using deep learning in PyTorch. Train your own model from scratch and predict various weather patterns and conditions. #DeepLearning #PyTorch #WeatherClassification
This is my project for the course "Deep Learning with PyTorch: Zero to GANs" where I train a deep learning model in Pytorch from scratch for weather analysis by making it predict and recognize various weather patterns and conditions from still (colour)images.
PyTorch is an optimized open source tensor library for deep learning using GPUs and CPUs. It is based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab. It is highly popular for its Automatic Differentiation feature and CUDA support.
Multi-class weather dataset(MWD) for image classification is a valuable dataset featured in the research paper entitled “Multi-class weather recognition from still image using heterogeneous ensemble method”. The dataset provides a platform for outdoor weather analysis by extracting various features for recognizing different weather conditions. This dataset is a collection of 1125 images divided into four groups ---> sunrise, shine, rain, and cloudy.