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image-matting-app
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batch_size: 4
iters: 300000

train_dataset:
  type: MattingDataset
  dataset_root: data/matting/Distinctions-646
  train_file: train.txt
  transforms:
    - type: LoadImages
    - type: Padding
      target_size: [512, 512]
    - type: RandomCrop
      crop_size: [[512, 512],[640, 640], [800, 800]]
    - type: Resize
      target_size: [512, 512]
    - type: RandomDistort
    - type: RandomBlur
      prob: 0.1
    - type: RandomHorizontalFlip
    - type: Normalize
  mode: train
  separator: '|'

val_dataset:
  type: MattingDataset
  dataset_root: data/matting/Distinctions-646
  val_file: val.txt
  transforms:
    - type: LoadImages
    - type: LimitShort
      max_short: 1536
    - type: ResizeToIntMult
      mult_int: 32
    - type: Normalize
  mode: val
  get_trimap: False
  separator: '|'

model:
  type: PPMatting
  backbone:
    type: HRNet_W48
    # pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w48_ssld.tar.gz
  pretrained: Null

optimizer:
  type: sgd
  momentum: 0.9
  weight_decay: 4.0e-5

lr_scheduler:
  type: PolynomialDecay
  learning_rate: 0.01
  end_lr: 0
  power: 0.9