update deterministic training results

README.md

@@ -43,13 +43,13 @@ Two channels
 - Label 0: everything else
 
 ## Performance
-Dice score is used for evaluating the performance of the model. This model achieves a mean dice score of 0.959.
+Dice score is used for evaluating the performance of the model. This model achieves a mean dice score of 0.961.
 
 #### Training Loss
-![A graph showing the training loss over 1260 epochs (10080 iterations).](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_train_3.png)
+![A graph showing the training loss over 1260 epochs (10080 iterations).](https://developer.download.nvidia.com/assets/Clara/Images/monai_spleen_ct_segmentation_train.png)
 
 #### Validation Dice
-![A graph showing the validation mean Dice over 1260 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_val_3.png)
+![A graph showing the validation mean Dice over 1260 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_spleen_ct_segmentation_val.png)
 
 #### TensorRT speedup
 The `spleen_ct_segmentation` bundle supports the TensorRT acceleration. The table below shows the speedup ratios benchmarked on an A100 80G GPU.

configs/evaluate.json

@@ -72,9 +72,6 @@
             "summary_ops": "*"
         }
     ],
-    "initialize": [
-        "$setattr(torch.backends.cudnn, 'benchmark', True)"
-    ],
     "run": [
         "$@validate#evaluator.run()"
     ]

configs/inference.json

@@ -147,7 +147,7 @@
         "amp": true
     },
     "initialize": [
-        "$setattr(torch.backends.cudnn, 'benchmark', True)"
+        "$monai.utils.set_determinism(seed=123)"
     ],
     "run": [
         "$@evaluator.run()"

configs/metadata.json

@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.4.7",
+    "version": "0.4.8",
     "changelog": {
+        "0.4.8": "update deterministic training results",
         "0.4.7": "update the TensorRT part in the README file",
         "0.4.6": "fix mgpu finalize issue",
         "0.4.5": "enable deterministic training",
@@ -43,7 +44,7 @@
     "label_classes": "single channel data, 1 is spleen, 0 is everything else",
     "pred_classes": "2 channels OneHot data, channel 1 is spleen, channel 0 is background",
     "eval_metrics": {
-        "mean_dice": 0.959
+        "mean_dice": 0.961
     },
     "intended_use": "This is an example, not to be used for diagnostic purposes",
     "references": [

docs/README.md

@@ -36,13 +36,13 @@ Two channels
 - Label 0: everything else
 
 ## Performance
-Dice score is used for evaluating the performance of the model. This model achieves a mean dice score of 0.959.
+Dice score is used for evaluating the performance of the model. This model achieves a mean dice score of 0.961.
 
 #### Training Loss
-![A graph showing the training loss over 1260 epochs (10080 iterations).](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_train_3.png)
+![A graph showing the training loss over 1260 epochs (10080 iterations).](https://developer.download.nvidia.com/assets/Clara/Images/monai_spleen_ct_segmentation_train.png)
 
 #### Validation Dice
-![A graph showing the validation mean Dice over 1260 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_val_3.png)
+![A graph showing the validation mean Dice over 1260 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_spleen_ct_segmentation_val.png)
 
 #### TensorRT speedup
 The `spleen_ct_segmentation` bundle supports the TensorRT acceleration. The table below shows the speedup ratios benchmarked on an A100 80G GPU.

models/model.pt

@@ -1,3 +1,3 @@
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+oid sha256:502c312893994ec071a85c4f2e7d83a43f7789969faa73dae62f1291177f50fe
 size 19297197

models/model.ts

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f325fbb60833b0946e234ab14590bde652223503d41f445da879f0396f08a21a
-size 19411907
+oid sha256:d8d7b04a24b1a6016aa32c662913b907dab4fb91cd88a91cb7e0a8f11eeafec0
+size 19412163