{
    "model": "unet",
    "dataset" : "mapillary",
    "preprocessing": {
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            "height": 512,
            "width": 1024
        },
        "normalize": {
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            "std" : [0.229, 0.224, 0.225]
        }
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    "input_info": {
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    },

    "weighing": [0.0000, 0.0554, 0.3358, 0.0843, 0.7865, 0.7753, 1.1966, 5.1031, 2.4255,
        0.0679, 0.8589, 0.0389, 2.8977, 9.4937, 0.2531, 1.8852, 2.1179, 2.1978,
        5.9516, 6.4394],
    "batch_size" : 5,
    "optimizer": {
        "type": "Adam",
        "optimizer_params": {
            "lr": 1.0e-5,
            "weight_decay": 2.0e-4
        },
        "schedule_type": "step",
        "step": 20,
        "gamma": 0.1
    },

    "model_params": {
         "input_size_hw": [512, 1024]
    },
    "compression": [
      {
        "algorithm": "filter_pruning",
        "pruning_init": 0.05,
        "params": {
            "schedule": "exponential",
            "pruning_target": 0.25,
            "pruning_steps": 10,
            "filter_importance": "geometric_median"
        },
        "ignored_scopes": [
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            "{re}UNet/ModuleList\\[up_path\\]/UNetUpBlock\\[1\\]/NNCFConvTranspose2d\\[up\\].*",
            "{re}UNet/ModuleList\\[up_path\\]/UNetUpBlock\\[2\\]/NNCFConvTranspose2d\\[up\\].*",
            "{re}UNet/ModuleList\\[up_path\\]/UNetUpBlock\\[3\\]/NNCFConvTranspose2d\\[up\\].*",
            "{re}UNet/ModuleList\\[up_path\\]/UNetUpBlock\\[3\\]/UNetConvBlock\\[conv_block\\]/Sequential\\[block\\]/NNCFConv2d\\[0\\].*",
            "{re}UNet/ModuleList\\[up_path\\]/UNetUpBlock\\[3\\]/UNetConvBlock\\[conv_block\\]/Sequential\\[block\\]/NNCFConv2d\\[3\\].*"
        ]
      }
    ]
}