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PERFORMANCE.md

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Performances

  • Baseline

    • Model: se_resnext50_32x4d
    • image_size: 512x512
    • batch_size: 64
    • grad_accum: 2
    • augmentations:
      def train_aug(image_size=512):
      return Compose([
          Resize(image_size, image_size),
          RandomRotate90(),
          Flip(),
          Transpose(),
      ], p=1)


  def valid_aug(image_size=512):
      return Compose([
          # CenterCrop(448, 448),
          Resize(image_size, image_size)
          # Normalize(),
      ], p=1)
    • Optimizers:
    criterion_params:
  criterion: CrossEntropyLoss

optimizer_params:
  optimizer: Adam
  lr: 0.0003
  weight_decay: 0.0001

scheduler_params:
  scheduler: MultiStepLR
  milestones: [25, 30, 40]
  gamma: 0.5

data_params:
  batch_size: 64
  num_workers: 4
  drop_last: False

  image_size: &image_size 512
  train_csv: "./csv/train_0.csv"
  valid_csv: "./csv/valid_0.csv"
  root: "/raid/data/kaggle/recursion-cellular-image-classification/"
  site: 2
  channels: [1, 2, 3]

    Results: (fold 0)

    Experiment CV LB
    c123_s1 42.9% 30.6%
    c123_s2 41% 23.6%
    Ensemble 0.7 * c123_s1 + 0.3 * c123_s2 - 32.5

    c123_s1: using channels=[1,2,3] and site = 1