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Merge pull request #133 from allenai/favyen/20250514-pastis
Add PASTIS dataset, + evaluation for Helios
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‎data/helios/pastis/baseline_satlaspretrain.yaml

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model:
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class_path: rslearn.train.lightning_module.RslearnLightningModule
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init_args:
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model:
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class_path: rslearn.models.multitask.MultiTaskModel
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init_args:
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encoder:
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- class_path: rslearn.models.simple_time_series.SimpleTimeSeries
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init_args:
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encoder:
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class_path: rslearn.models.swin.Swin
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init_args:
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pretrained: true
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input_channels: 9
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output_layers: [1, 3, 5, 7]
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image_channels: 9
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groups: [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
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decoders:
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segment:
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- class_path: rslearn.models.unet.UNetDecoder
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init_args:
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in_channels: [[4, 128], [8, 256], [16, 512], [32, 1024]]
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out_channels: 20
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conv_layers_per_resolution: 2
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- class_path: rslearn.train.tasks.segmentation.SegmentationHead
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lr: 0.0001
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plateau: true
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plateau_factor: 0.2
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plateau_patience: 2
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plateau_min_lr: 0
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plateau_cooldown: 10
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restore_config:
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restore_path: https://ai2-public-datasets.s3.amazonaws.com/satlas/satlas-model-v1-lowres-band-multi.pth
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remap_prefixes:
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- ["backbone.backbone.backbone.", "encoder.0.encoder.model."]
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data:
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class_path: rslearn.train.data_module.RslearnDataModule
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init_args:
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path: /weka/dfive-default/rslearn-eai/datasets/pastis/rslearn_dataset/
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inputs:
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sentinel2_0:
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data_type: "raster"
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layers: ["sentinel2"]
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bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
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passthrough: true
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sentinel2_1:
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data_type: "raster"
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layers: ["sentinel2.1"]
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bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
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passthrough: true
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sentinel2_2:
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data_type: "raster"
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layers: ["sentinel2.2"]
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bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
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passthrough: true
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sentinel2_3:
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data_type: "raster"
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layers: ["sentinel2.3"]
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bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
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passthrough: true
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sentinel2_4:
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data_type: "raster"
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layers: ["sentinel2.4"]
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bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
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passthrough: true
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sentinel2_5:
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data_type: "raster"
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layers: ["sentinel2.5"]
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bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
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passthrough: true
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sentinel2_6:
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data_type: "raster"
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layers: ["sentinel2.6"]
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bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
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passthrough: true
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sentinel2_7:
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data_type: "raster"
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layers: ["sentinel2.7"]
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bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
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passthrough: true
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sentinel2_8:
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data_type: "raster"
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layers: ["sentinel2.8"]
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bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
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passthrough: true
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sentinel2_9:
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data_type: "raster"
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layers: ["sentinel2.9"]
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bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
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passthrough: true
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sentinel2_10:
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data_type: "raster"
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layers: ["sentinel2.10"]
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bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
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passthrough: true
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sentinel2_11:
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data_type: "raster"
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layers: ["sentinel2.11"]
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bands: ["B04", "B03", "B02", "B05", "B06", "B07", "B08", "B11", "B12"]
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passthrough: true
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targets:
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data_type: "raster"
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layers: ["label"]
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bands: ["class"]
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is_target: true
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task:
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class_path: rslearn.train.tasks.multi_task.MultiTask
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init_args:
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tasks:
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segment:
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class_path: rslearn.train.tasks.segmentation.SegmentationTask
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init_args:
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num_classes: 20
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remap_values: [[0, 1], [0, 255]]
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zero_is_invalid: true
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metric_kwargs:
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average: "micro"
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enable_miou_metric: true
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input_mapping:
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segment:
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targets: "targets"
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batch_size: 8
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num_workers: 32
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default_config:
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transforms:
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- class_path: rslearn.train.transforms.normalize.Normalize
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init_args:
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mean: 0
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std: 3000
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valid_range: [0, 1]
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bands: [0, 1, 2]
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selectors:
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- sentinel2_0
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- sentinel2_1
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- sentinel2_2
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- sentinel2_3
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- sentinel2_4
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- sentinel2_5
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- sentinel2_6
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- sentinel2_7
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- sentinel2_8
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- sentinel2_9
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- sentinel2_10
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- sentinel2_11
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- class_path: rslearn.train.transforms.normalize.Normalize
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init_args:
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mean: 0
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std: 8160
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valid_range: [0, 1]
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bands: [3, 4, 5, 6, 7, 8]
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selectors:
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- sentinel2_0
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- sentinel2_1
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- sentinel2_2
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- sentinel2_3
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- sentinel2_4
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- sentinel2_5
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- sentinel2_6
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- sentinel2_7
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- sentinel2_8
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- sentinel2_9
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- sentinel2_10
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- sentinel2_11
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- class_path: rslearn.train.transforms.concatenate.Concatenate
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init_args:
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selections:
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sentinel2_0: []
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sentinel2_1: []
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sentinel2_2: []
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sentinel2_3: []
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sentinel2_4: []
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sentinel2_5: []
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sentinel2_6: []
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sentinel2_7: []
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sentinel2_8: []
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sentinel2_9: []
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sentinel2_10: []
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sentinel2_11: []
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output_selector: image
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train_config:
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patch_size: 64
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transforms:
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- class_path: rslearn.train.transforms.normalize.Normalize
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init_args:
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mean: 0
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std: 3000
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valid_range: [0, 1]
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bands: [0, 1, 2]
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selectors:
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- sentinel2_0
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- sentinel2_1
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- sentinel2_2
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- sentinel2_3
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- sentinel2_4
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- sentinel2_5
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- sentinel2_6
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- sentinel2_7
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- sentinel2_8
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- sentinel2_9
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- sentinel2_10
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- sentinel2_11
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- class_path: rslearn.train.transforms.normalize.Normalize
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init_args:
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mean: 0
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std: 8160
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valid_range: [0, 1]
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bands: [3, 4, 5, 6, 7, 8]
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selectors:
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- sentinel2_0
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- sentinel2_1
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- sentinel2_2
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- sentinel2_3
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- sentinel2_4
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- sentinel2_5
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- sentinel2_6
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- sentinel2_7
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- sentinel2_8
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- sentinel2_9
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- sentinel2_10
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- sentinel2_11
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- class_path: rslearn.train.transforms.concatenate.Concatenate
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init_args:
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selections:
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sentinel2_0: []
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sentinel2_1: []
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sentinel2_2: []
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sentinel2_3: []
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sentinel2_4: []
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sentinel2_5: []
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sentinel2_6: []
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sentinel2_7: []
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sentinel2_8: []
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sentinel2_9: []
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sentinel2_10: []
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sentinel2_11: []
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output_selector: image
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- class_path: rslearn.train.transforms.flip.Flip
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init_args:
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image_selectors: ["image", "target/segment/classes", "target/segment/valid"]
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groups: ["fold1", "fold2", "fold3"]
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val_config:
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patch_size: 128
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groups: ["fold4"]
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test_config:
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patch_size: 128
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groups: ["fold5"]
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trainer:
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max_epochs: 500
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callbacks:
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- class_path: lightning.pytorch.callbacks.LearningRateMonitor
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init_args:
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logging_interval: "epoch"
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- class_path: rslearn.train.callbacks.freeze_unfreeze.FreezeUnfreeze
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init_args:
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module_selector: ["model", "encoder", 0, "encoder", "model"]
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unfreeze_at_epoch: 5
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- class_path: lightning.pytorch.callbacks.ModelCheckpoint
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init_args:
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save_top_k: 1
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save_last: true
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monitor: val_segment/accuracy
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mode: max
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rslp_project: helios_finetuning
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rslp_experiment: placeholder

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