ocrnet_hrnetw18_road_extraction_768x768_15k.yml

batch_size: 4
iters: 15000

train_dataset:
  type: MiniDeepGlobeRoadExtraction
  dataset_root: data/MiniDeepGlobeRoadExtraction
  transforms:
    - type: ResizeStepScaling
      min_scale_factor: 0.5
      max_scale_factor: 2.0
      scale_step_size: 0.25
    - type: RandomPaddingCrop
      crop_size: [768, 768]
    - type: RandomHorizontalFlip
    - type: Normalize
  mode: train

val_dataset:
  type: MiniDeepGlobeRoadExtraction
  dataset_root: data/MiniDeepGlobeRoadExtraction
  transforms:
    - type: Normalize
  mode: val

model:
  type: OCRNet
  backbone:
    type: HRNet_W18
    pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w18_ssld.tar.gz
  backbone_indices: [0]

optimizer:
  type: SGD

lr_scheduler:
  type: PolynomialDecay
  learning_rate: 0.01
  power: 0.9


loss:
  types:
    - type: CrossEntropyLoss
    - type: CrossEntropyLoss
  coef: [1, 0.4]