3

Signed-off-by: myron <[email protected]>

Author: myron

auto3dseg/tasks/kits23/README.md

@@ -1,9 +1,6 @@
 
 # KiTS23 
 
-TODO
-
-
 The KiTS dataset is from MICCAI 2023 challenge **[The 2023 Kidney and Kidney Tumor Segmentation Challenge (KiTS23)](https://kits-challenge.org/kits23/)**. The solution described here won the 1st place in the KiTS challenge [(NVAUTO team)](https://kits-challenge.org/kits23/#kits23-official-results):
 
 Andriy Myronenko, Dong Yang, Yufan He and Daguang Xu: "Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge". In MICCAI (2023). [arXiv](https://arxiv.org/abs/2310.04110)
@@ -25,10 +22,9 @@ The KiTS tutorial is only supported for **SegResNet** algo, Auto3DSeg runs a ful
 The Auto3DSeg can be run using a config **input.yaml**
 
 ```bash
-python -m monai.apps.auto3dseg AutoRunner run --input='./input.yaml' --algos='segresnet'
+python -m monai.apps.auto3dseg AutoRunner run --input=./input.yaml --algos=segresnet
 ```
 
-
 ## Validation performance: NVIDIA DGX-1 (8x V100 32G)
 
 The validation results can be obtained by running the training script with MONAI 1.3.0 on NVIDIA DGX-1 with (8x V100 32GB) GPUs. The results below are in terms of average dice.

auto3dseg/tasks/kits23/input.yaml

@@ -16,13 +16,12 @@ sigmoid: true
 # the config below is optional, but it explicitly sets params as it was used during KiTS23 challenge
 # otherwise, the defaults are (auto_scale_allowed is True) and the system will attempt to guess these settings according to the available GPU (e.g. make batch size larger) 
 auto_scale_allowed: false
-batch_size: 1
 roi_size: [256, 256, 256]
 num_epochs: 600
-resample: true
-resample_resolution: [0.78125, 0.78125, 0.78125]
 loss: {_target_: DiceLoss}
-
+# batch_size: 1
+# resample: true
+# resample_resolution: [0.78125, 0.78125, 0.78125] # this is auto determined, uncomment to set another value manually