config.yaml

experiment:
  name: "Automated Nuclei Detection and Segmentation"
  description: "Evaluating Performance Between Variation of UNet Models for Nuclei Detection and Segmentation"
  date: "2024-03-15"

dataset:
  name: "Nuclei Dataset"
  description: "A dataset of nuclei images for training and testing"
  url: "https://drive.google.com/file/d/1xgwanFgjYzhJiDtmgg5qekppCadgZB-m/view?usp=sharing"
  stage: 1

model:
  spatial_dims: 2
  in_channels: 4
  out_channels: 2
  image_size: [512, 512] ## Image size for the model, either List or Tuple is accepted (256, 256)
  channels: [16, 32, 64, 128, 256, 512] ## Number of channels in the encoder, either List or Tuple is accepted (16, 32, 64, 128, 256)
  strides: [2, 2, 2, 2, 2] ## Strides in the encoder, either List or Tuple is accepted (2, 2, 2, 2)
  kernel_size: 3
  activation: "LeakyReLU"
  normalization: "Batch"
  dropout: 0.2
  num_res_units: 5

training:
  batch_size: 5
  loss: "DiceFocalLoss"
  metrics: ["Dice", "IoU"]
  save_dir: ""
  val_period: 5
  num_workers: 8
  device: "cuda"

inference:
  model_dir: ""
  predict_dir: ""