The article discusses building Generative Adversarial Networks (GANs) from scratch in Python. The author aims to remove clouds from satellite RGB images using GANs. The EuroSat dataset, consisting of 27,000 labeled RGB images, is used for this purpose. The process involves generating clouds using Perlin noise, overlaying the clouds on the images, and training the GANs. The training loop includes training the discriminator (ResNet) and the generator (UNet) separately. The loss functions include Binary Cross-Entropy (BCE) and L1 loss. The data is split into training and testing sets, and the training loop is executed for a specified number of epochs. The losses for both the generator and discriminator are tracked and visualized using matplotlib. The code includes the implementation of ResNet and UNet architectures, as well as the dataset class for loading the data. The training loop involves optimizing the discriminator and generator, calculating losses, and updating the weights. The final step involves plotting the losses to monitor the training progress. The code is written in PyTorch and includes detailed explanations for each step of the process.
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Source link: https://towardsdatascience.com/erasing-clouds-from-satellite-imagery-using-gans-generative-adversarial-networks-2d7f8467ef2e
Using GANs to Remove Clouds from Satellite Images #ImageProcessing
