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nnUNet_results/Dataset789_ChronoRoot2/nnUNetTrainer__nnUNetPlans__2d/dataset.json

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+{
+    "channel_names": {
+        "0": "Image"
+    },
+    "labels": {
+        "background": 0,
+        "main root": 1,
+        "lateral root": 2,
+        "seed": 3,
+        "hypocotil": 4,
+        "leaf": 5,
+        "petiole": 6
+    },
+    "numTraining": 945,
+    "file_ending": ".png"
+}

nnUNet_results/Dataset789_ChronoRoot2/nnUNetTrainer__nnUNetPlans__2d/fold_0/checkpoint_best.pth

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nnUNet_results/Dataset789_ChronoRoot2/nnUNetTrainer__nnUNetPlans__2d/fold_0/checkpoint_final.pth

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nnUNet_results/Dataset789_ChronoRoot2/nnUNetTrainer__nnUNetPlans__2d/fold_0/debug.json

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+{
+    "_best_ema": "None",
+    "batch_size": "12",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [448, 576], 'median_image_size_in_voxels': [2464.0, 3280.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}",
+    "configuration_name": "2d",
+    "cudnn_version": 90100,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7b7d52794920>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_2d.nnUNetDataLoader2D object at 0x7b7d53166cf0>",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "Compose ( [SpatialTransform( independent_scale_for_each_axis = False, p_rot_per_sample = 0.2, p_scale_per_sample = 0.2, p_el_per_sample = 0, data_key = 'data', label_key = 'seg', patch_size = [448, 576], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-3.141592653589793, 3.141592653589793), angle_y = (0, 0), angle_z = (0, 0), do_scale = True, scale = (0.7, 1.4), border_mode_data = 'constant', border_cval_data = 0, order_data = 3, border_mode_seg = 'constant', border_cval_seg = -1, order_seg = 1, random_crop = False, p_rot_per_axis = 1, p_independent_scale_per_axis = 1 ), GaussianNoiseTransform( p_per_sample = 0.1, data_key = 'data', noise_variance = (0, 0.1), p_per_channel = 1, per_channel = False ), GaussianBlurTransform( p_per_sample = 0.2, different_sigma_per_channel = True, p_per_channel = 0.5, data_key = 'data', blur_sigma = (0.5, 1.0), different_sigma_per_axis = False, p_isotropic = 0 ), BrightnessMultiplicativeTransform( p_per_sample = 0.15, data_key = 'data', multiplier_range = (0.75, 1.25), per_channel = True ), ContrastAugmentationTransform( p_per_sample = 0.15, data_key = 'data', contrast_range = (0.75, 1.25), preserve_range = True, per_channel = True, p_per_channel = 1 ), SimulateLowResolutionTransform( order_upsample = 3, order_downsample = 0, channels = None, per_channel = True, p_per_channel = 0.5, p_per_sample = 0.25, data_key = 'data', zoom_range = (0.5, 1), ignore_axes = None ), GammaTransform( p_per_sample = 0.1, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = True ), GammaTransform( p_per_sample = 0.3, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = False ), MirrorTransform( p_per_sample = 1, data_key = 'data', label_key = 'seg', axes = (0, 1) ), RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[np.float64(1.0), np.float64(1.0)], [np.float64(0.5), np.float64(0.5)], [np.float64(0.25), np.float64(0.25)], [np.float64(0.125), np.float64(0.125)], [np.float64(0.0625), np.float64(0.0625)], [np.float64(0.03125), np.float64(0.03125)]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7b7d527c65d0>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_2d.nnUNetDataLoader2D object at 0x7b7d53167290>",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "Compose ( [RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[np.float64(1.0), np.float64(1.0)], [np.float64(0.5), np.float64(0.5)], [np.float64(0.25), np.float64(0.25)], [np.float64(0.125), np.float64(0.125)], [np.float64(0.0625), np.float64(0.0625)], [np.float64(0.03125), np.float64(0.03125)]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataset_json": "{'channel_names': {'0': 'Image'}, 'labels': {'background': 0, 'main root': 1, 'lateral root': 2, 'seed': 3, 'hypocotil': 4, 'leaf': 5, 'petiole': 6}, 'numTraining': 945, 'file_ending': '.png'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "fold": "0",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA GeForce RTX 3090",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7b7d52797860>",
+    "hostname": "apoloml-B760M-K-DDR4",
+    "inference_allowed_mirroring_axes": "(0, 1)",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7b7d526bc980>",
+    "local_rank": "0",
+    "log_file": "nnUNet_results/Dataset789_ChronoRoot2/nnUNetTrainer__nnUNetPlans__2d/fold_0/training_log_2025_1_20_15_33_32.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7b7d53167230>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7b7d527e2ea0>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset789_ChronoRoot2', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [999.0, 1.0, 1.0], 'original_median_shape_after_transp': [1, 2464, 3280], 'image_reader_writer': 'NaturalImage2DIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [448, 576], 'median_image_size_in_voxels': [2464.0, 3280.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 225.0, 'mean': 119.90813446044922, 'median': 122.0, 'min': 0.0, 'percentile_00_5': 0.3148415684700012, 'percentile_99_5': 196.0, 'std': 38.823753356933594}}}, 'configuration': '2d', 'fold': 0, 'dataset_json': {'channel_names': {'0': 'Image'}, 'labels': {'background': 0, 'main root': 1, 'lateral root': 2, 'seed': 3, 'hypocotil': 4, 'leaf': 5, 'petiole': 6}, 'numTraining': 945, 'file_ending': '.png'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "1000",
+    "num_input_channels": "1",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "nnUNet_results/Dataset789_ChronoRoot2/nnUNetTrainer__nnUNetPlans__2d/fold_0",
+    "output_folder_base": "nnUNet_results/Dataset789_ChronoRoot2/nnUNetTrainer__nnUNetPlans__2d",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset789_ChronoRoot2', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [999.0, 1.0, 1.0], 'original_median_shape_after_transp': [1, 2464, 3280], 'image_reader_writer': 'NaturalImage2DIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [448, 576], 'median_image_size_in_voxels': [2464.0, 3280.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 225.0, 'mean': 119.90813446044922, 'median': 122.0, 'min': 0.0, 'percentile_00_5': 0.3148415684700012, 'percentile_99_5': 196.0, 'std': 38.823753356933594}}}",
+    "preprocessed_dataset_folder": "nnUNet_preprocessed/Dataset789_ChronoRoot2/nnUNetPlans_2d",
+    "preprocessed_dataset_folder_base": "nnUNet_preprocessed/Dataset789_ChronoRoot2",
+    "save_every": "50",
+    "torch_version": "2.5.1",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

nnUNet_results/Dataset789_ChronoRoot2/nnUNetTrainer__nnUNetPlans__2d/plans.json

@@ -0,0 +1,169 @@
+{
+    "dataset_name": "Dataset789_ChronoRoot2",
+    "plans_name": "nnUNetPlans",
+    "original_median_spacing_after_transp": [
+        999.0,
+        1.0,
+        1.0
+    ],
+    "original_median_shape_after_transp": [
+        1,
+        2464,
+        3280
+    ],
+    "image_reader_writer": "NaturalImage2DIO",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
+        "2d": {
+            "data_identifier": "nnUNetPlans_2d",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 12,
+            "patch_size": [
+                448,
+                576
+            ],
+            "median_image_size_in_voxels": [
+                2464.0,
+                3280.0
+            ],
+            "spacing": [
+                1.0,
+                1.0
+            ],
+            "normalization_schemes": [
+                "ZScoreNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                6,
+                6
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 512,
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "batch_dice": true
+        }
+    },
+    "experiment_planner_used": "ExperimentPlanner",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 225.0,
+            "mean": 119.90813446044922,
+            "median": 122.0,
+            "min": 0.0,
+            "percentile_00_5": 0.3148415684700012,
+            "percentile_99_5": 196.0,
+            "std": 38.823753356933594
+        }
+    }
+}