full-models/SegFormerB2-S2-unambiguous/config.yaml

NAME: SegformerB2

#-------------
# Model options
#-------------
PATCH_SIZE: 512
SEnSeIv2: false
MODEL_TYPE: 'Segformer'
SEGFORMER_CONFIG: 'nvidia/mit-b2'
RECOVERY_MODULE: null
MULTIMODAL: false
NUM_CHANNELS: 13 # Set to null for sensor independent models
CLASSES: 4

#----------------
# Training options (not needed for inference)
#----------------
EPOCHS: 60
BATCH_SIZE: 8
PHASES: [0, 1, 2, 40, 50, 55]
ACCUMULATE_STEPS: [1, 1, 1, 1, 4, 8]
LR: [0.000005, 0.00002, 0.0001, 0.00001, 0.00001, 0.000002]
EPSILON: 0.000001
WEIGHT_DECAY: 0.0001
L1_REG: 0
RECOVERY_WARMUP_STEPS: 0
RECOVERY_LOSS_FACTOR: 0
LOSS: 'ambiguous_crossentropy_loss' # Not actually ambiguous, because labels are not ambiguous

#------------
#Data options
#------------
TRAIN_DIRS:
  - '/home/ali/data/cloud-masking/processed/S2CS12/train'
VALID_DIRS:
  - '/home/ali/data/cloud-masking/processed/S2CS12/valid'
MIN_BANDS: null
MAX_BANDS: null