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# RoboSat.pink Configuration
[dataset]
# The datasets base directory.
path = "~/rsp_dataset"
# Optional PostgreSQL Database connection, using psycopg2 syntax (could be use by rasterize tool).
pg_dsn = "host=127.0.0.1 dbname=rsp user=postgres"
# Classes configuration.
# Nota: available colors are either CSS3 colors names or #RRGGBB hexadecimal representation.
[[classes]]
title = "background"
color = "white"
[[classes]]
title = "building"
color = "deeppink"
# Indicate which dataset sub-directory and bands to take as input.
# You could so, add several channels blocks to compose your input Tensor. Orders are meaningful.
[[channels]]
sub = "images"
bands = [1, 2, 3]
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
[model]
# Model name.
name = "albunet"
# Encoder model name.
encoder = "resnet50"
# Use, or not, ImageNet weights pretraining.
pretrained = true
# Loss function name.
loss = "lovasz"
# Batch size for training.
batch_size = 8
# tile side size in pixels.
tile_size = 512
# Total number of epochs to train for.
epochs = 10
# Learning rate for the optimizer.
lr = 0.000025
# Data augmentation, Flip or Rotate probability.
data_augmentation = 0.75
# Weight decay l2 penalty for the optimizer.
decay = 0.0