model_name: xdxd_spacenet4 model_path: train: true infer: true pretrained: false nn_framework: torch batch_size: 1 data_specs: width: 512 height: 512 image_type: 16bit channels: 4 label_type: mask is_categorical: false mask_channels: 1 val_holdout_frac: 0.05 dtype: training_data_csv: 'csv_file_path' validation_data_csv: 'auto_split_from_training_dataset' inference_data_csv: 'csv_file_path' training_augmentation: augmentations: HorizontalFlip: p: 0.5 RandomRotate90: p: 0.5 RandomCrop: height: 512 width: 512 p: 1.0 Normalize: mean: - 0.0 - 0.0 - 0.0 - 0.0 std: - 1.0 - 1.0 - 1.0 - 20.0 max_pixel_value: 255 p: 1.0 p: 1.0 shuffle: true validation_augmentation: augmentations: CenterCrop: height: 512 width: 512 p: 1.0 Normalize: mean: - 0.0 - 0.0 - 0.0 - 0.0 std: - 1.0 - 1.0 - 1.0 - 20.0 max_pixel_value: 255 p: 1.0 p: 1.0 inference_augmentation: augmentations: Normalize: mean: - 0.0 - 0.0 - 0.0 - 0.0 std: - 1.0 - 1.0 - 1.0 - 20.0 max_pixel_value: 255 p: 1.0 p: 1.0 training: epochs: 10 optimizer: AdamW lr: 1e-4 opt_args: loss: bcewithlogits: jaccard: loss_weights: bcewithlogits: 10 jaccard: 2.5 metrics: training: validation: callbacks: model_checkpoint: filepath: './models/trees/RGB+ELEV/checkpoint.pth' monitor: periodic period: 10 model_dest_path: './models/trees/RGB+ELEV.pth' verbose: true inference: output_dir: './results/trees/RGB+ELEV/pred_mask' window_step_size_x: window_step_size_y: