--> Generating Maps from Satellite images using the Pix2Pix GAN.
Developed and Trained a model that can convert aerial satellite imagery ("input") into map routes ("output"). The architecture for the generator is a U-Net and the Discriminator is a PatchGAN. I have saved the model checkpoint while training(pix2pix_15000.pth), that can be used as a pre-trained model. However, it can be trained from scratch.
adv_criterion = nn.BCEWithLogitsLoss()
recon_criterion = nn.L1Loss()
lambda_recon = 200
n_epochs = 20
input_dim = 3
real_dim = 3
display_step = 200
batch_size = 4
lr = 0.0002
target_shape = 256
device = 'cpu' (could be cuda if your device supports!)
Shape of the input tensor: torch.Size([1, 3, 256, 256])
Shape of the Output tensor: torch.Size([1, 3, 256, 256])
--> Maps.zip
It contains two folders, one for each training and validation data.
The image tensor is horizontally concatenated. The condition(satellite image) is left half of the Image and the label(map) is the right half of each image in the dataset.
