Download and extract dataset from https://xview2.org/dataset and put it to data directory (or whatever)
Run python generate_polygons.py --input data/train
This script will generate pixel masks from json files.
Dockerfile has all the required libraries.
Most of the hyperparameters for training are defined by json files, see configs directory.
Other parameters are passed directly to train scripts.
Localization and classification networks are trained separately
train_localization.py- to train binary segmentation models. By default O0 opt level (FP32) is used for Apex due to unstable loss during training.train.py- to train classification models. By default O1 opt level (Mixed-Precision) is used for Apex as multiclass lossFocalLossWithDiceis stable in mixed precision .
For localization network ordinary U-Net like network was used with pretrained DPN92 and Densenet161 encoders (see models/unet.py for U-Nets Zoo)
For classification Siamese-UNet was used with shared encoder weights (see models/siamese_unet.py for Siamese U-Nets Zoo)
