A Convolutional Neural Network (CNN) model for weather prediction, implemented in PyTorch. This model utilizes the dc-weather-prediction dataset from Hugging Face to predict weather attributes from satellite images.

1. Clone this repository:

git clone https://github.com/Arkay92/WeatherNet.git

2. Install the required dependencies:

pip install -r requirements.txt

To train the model, run the following command:

python train.py

The model is trained on the dc-weather-prediction dataset from Hugging Face, which contains satellite images and corresponding weather attributes.

The CNN architecture is designed to extract features from satellite images and predict weather attributes. It includes convolutional layers, batch normalization, dropout layers, and fully connected layers.

The model is trained with a mean squared error loss function and an Adam optimizer. A learning rate scheduler is employed to adjust the learning rate based on the validation loss.

This project utilizes the dc-weather-prediction dataset available on Hugging Face.

This project is open-sourced under the MIT License. See the LICENSE file for more detail