basic.yaml

include: experiments/rsna-intracranial/include/dataset.yaml

entrypoint: classification

model_params:
  arch: 'ResNetClassifier2d'
  name: 'ResNetClassifier2d'
  num_classes: 6
  hidden_dims: [128, 128, 128, 128]
  pooling: max

exp_params:
  loss_params:
    objective: mse
  batch_size: 4
  optimizer:
    lr: 0.0002
    weight_decay: 0.00001
  #visdom:
  #  host: https://visdom.foldy.dev
  #  port: 443
  #  env: rsna-ich
  plot:
    fn: classifier2d
    sample_every_n_steps: 10_000
    examples_per_class: 6
    classes:
    - name: Control
      labels: [0, 0, 0, 0, 0, 0]
      all: true
      baseline: 0.2
    - name: Epidural
      labels: [1, 0, 0, 0, 0, 0]
      all: false
      baseline: 0.2
    - name: Intraparenchymal
      labels: [0, 1, 0, 0, 0, 0]
      all: false
      baseline: 0.2
    - name: Intraventricular
      labels: [0, 0, 1, 0, 0, 0]
      all: false
      baseline: 0.2
    - name: Subarachnoid
      labels: [0, 0, 0, 1, 0, 0]
      all: false
      baseline: 0.2
    - name: Subdural
      labels: [0, 0, 0, 0, 1, 0]
      all: false
      baseline: 0.2
    - name: Any
      labels: [0, 0, 0, 0, 0, 1]
      all: false
      baseline: 0.2
    params:
      img_filter: ct

trainer_params:
  max_epochs: 8
  log_every_n_steps: 20
  check_val_every_n_epoch: 1

manual_seed: 1498

logging_params:
  name: "RSNA_Basic"