# Ours # location: server location: local data: type: semantic_seg name: s2self num_classes: 128 window_size: 5 image_size: 128 time_steps: 2 weights: [] # channels: B02|B03|B04|B05|B06|B07|B08|B11|B12|B8A channels: B02|B03|B04|B05|B06|B07|B08|B11|B12 local: model_save_dir: /home/native/projects/data/smart_watch/models/ train_dir: /media/native/data2/data/S2_Unlabeled_AOIs/ test_dir: /media/native/data2/data/S2_Unlabeled_AOIs/ train_coco_json: /media/native/data/data/smart_watch_dvc/extern/onera_2018/onera_train.kwcoco.json test_coco_json: /media/native/data/data/smart_watch_dvc/extern/onera_2018/onera_train.kwcoco.json val_dir: server: model_save_dir: /data4/peri/smart_watch/models/ train_dir: /data4/datasets/smart_watch_dvc/extern/onera_2018/ test_dir: /data4/datasets/smart_watch_dvc/extern/onera_2018/ val_dir: /data4/datasets/smart_watch_dvc/extern/onera_2018/ train_coco_json: /data4/datasets/smart_watch_dvc/extern/onera_2018/onera_train.kwcoco.json test_coco_json: /data4/datasets/smart_watch_dvc/extern/onera_2018/onera_test.kwcoco.json training: backbone: #resnet18 #resnet101, resnet50 model_name: shallow_seg #shallow_seg, deeplabWS model_feats_channels: [32, 32, 64, 64, 128] #[32, 32, 64, 64, 128], [64, 64, 128, 256, 512], [32, 32, 64, 128, 256], [64, 128, 256, 512, 1024] # this needs to match the correct number of layers in the model gn_n_groups: 32 num_channels: 13 out_features_dim: 10 weight_std: True beta: False pretrained: False distributed: False learning_rate: 0.002 # best: 0.00007 # resume: /home/native/projects/data/smart_watch/models/experiments_onera/tasks_experiments_onera_2021-10-04-22:26/experiments_epoch_75_loss_0.250858994956116_valmIoU_0.5220931783424729_time_2021-10-05-13:53:50.pth resume: False train_val_test_split: [0.95, 0.02, 0.03] epochs: 200 start_epoch: 0 batch_size: 8 drop_last_batch: True momentum: 0.9 weight_decay: 0.0001 num_workers: 4 test_with_full_supervision: 1 high_confidence_threshold: train_cutoff: 0.4 val_cutoff: 0.4 train_low_cutoff: 0.0 val_low_cutoff: 0.0 evaluation: use_crf: False crf_t: 1 crf_scale_factor: 1 inference_window: 11 procedures: train: True validate: True