cell_painting_cnn_profiling_example.json

{
  "dataset": {
    "metadata": {
      "label_field": "pert_name",
      "control_value": "EMPTY_"
    },
    "images": {
      "channels": [
        "DNA",
        "RNA",
        "ER",
        "AGP",
        "Mito"
      ],
      "file_format": "tif",
      "bits": 16,
      "width": 1080,
      "height": 1080
    },
    "locations": {
      "mode": "single_cells",
      "box_size": 128,
      "area_coverage": 0.75,
      "mask_objects": false
    }
  },
  "prepare": {
    "illumination_correction": {
      "down_scale_factor": 4,
      "median_filter_size": 24
    },
    "compression": {
      "implement": true,
      "scaling_factor": 1.0
    }
  },
  "train": {
    "partition": {
      "targets": [
        "pert_name"
      ],
      "split_field": "Split",
      "training": [
        0
      ],
      "validation": [
        1
      ]
    },
    "model": {
      "name": "efficientnet",
      "crop_generator": "sampled_crop_generator",
      "augmentations": true,
      "metrics": [
        "accuracy",
        "top_k",
        "average_class_precision"
      ],
      "epochs": 3,
      "initialization": "ImageNet",
      "params": {
        "label_smoothing": 0.0,
        "learning_rate": 0.005,
        "batch_size": 32,
        "conv_blocks": 0
      }
    },
    "sampling": {
      "factor": 1,
      "workers": 1
    },
    "validation": {
      "frequency": 1,
      "top_k": 5,
      "batch_size": 32,
      "frame": "val",
      "sample_first_crops": true
    }
  },
  "profile": {
    "feature_layer": "block6a_activation",
    "checkpoint": "Cell_Painting_CNN_v1.hdf5",
    "batch_size": 32
  }
}