CellProfiler-plugins/tests/test_deepprofiler/config/profiling.json at ef47d668f210065cc505459337014d2b393d9213 · CellProfiler/CellProfiler-plugins · GitHub

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{
    "dataset": {
        "metadata": {
            "label_field": "Metadata_Plate",
            "control_value": "X"
        },
        "images": {
            "channels": [
                "DNA",
                "RNA",
                "ER",
                "AGP",
                "Mito"
              ],
            "file_format": "tif",
            "bits": 16,
            "width": 1224,
            "height": 904
        },
	"locations":{
	    "mode": "single_cells",
        "area_coverage": 0.75,
        "box_size": 128,
        "mask_objects": false
	}
    },
    "prepare": {
        "illumination_correction": {
            "down_scale_factor": 4,
            "median_filter_size": 24
        },
        "compression": {
            "implement": false,
            "scaling_factor": 1.0
        }
    },
    "train": {
        "partition": {
            "targets": [
                "Metadata_Plate"
            ],
            "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
    }
}