update the model weights with the deterministic training

README.md

@@ -66,13 +66,13 @@ Two Channels
 - Label 1: out body
 
 ## Performance
-Accuracy was used for evaluating the performance of the model. This model achieves an accuracy score of 0.98
+Accuracy was used for evaluating the performance of the model. This model achieves an accuracy score of 0.99
 
 #### Training Loss
-![A graph showing the training loss over 25 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_endoscopic_inbody_classification_train_loss.png)
+![A graph showing the training loss over 25 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_endoscopic_inbody_classification_train_loss_v2.png)
 
 #### Validation Accuracy
-![A graph showing the validation accuracy over 25 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_endoscopic_inbody_classification_val_accuracy.png)
+![A graph showing the validation accuracy over 25 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_endoscopic_inbody_classification_val_accuracy_v2.png)
 
 #### TensorRT speedup
 The `endoscopic_inbody_classification` bundle supports the TensorRT acceleration through the ONNX-TensorRT way. The table below shows the speedup ratios benchmarked on an A100 80G GPU.

configs/evaluate.json

@@ -41,9 +41,6 @@
             "summary_ops": "*"
         }
     ],
-    "initialize": [
-        "$setattr(torch.backends.cudnn, 'benchmark', True)"
-    ],
     "run": [
         "$@validate#evaluator.run()"
     ]

configs/inference.json

@@ -106,7 +106,7 @@
         "val_handlers": "@handlers"
     },
     "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": "0.4.0",
+    "version": "0.4.1",
     "changelog": {
+        "0.4.1": "update the model weights with the deterministic training",
         "0.4.0": "add the ONNX-TensorRT way of model conversion",
         "0.3.9": "fix mgpu finalize issue",
         "0.3.8": "enable deterministic training",
@@ -37,7 +38,7 @@
     "label_classes": "0: inbody, 1: outbody",
     "pred_classes": "vector whose length equals to 2, [1,0] means in body, [0,1] means out body",
     "eval_metrics": {
-        "accuracy": 0.98
+        "accuracy": 0.99
     },
     "references": [
         "J. Hu, L. Shen and G. Sun, Squeeze-and-Excitation Networks, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132-7141. https://arxiv.org/pdf/1709.01507.pdf"

docs/README.md

@@ -59,13 +59,13 @@ Two Channels
 - Label 1: out body
 
 ## Performance
-Accuracy was used for evaluating the performance of the model. This model achieves an accuracy score of 0.98
+Accuracy was used for evaluating the performance of the model. This model achieves an accuracy score of 0.99
 
 #### Training Loss
-![A graph showing the training loss over 25 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_endoscopic_inbody_classification_train_loss.png)
+![A graph showing the training loss over 25 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_endoscopic_inbody_classification_train_loss_v2.png)
 
 #### Validation Accuracy
-![A graph showing the validation accuracy over 25 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_endoscopic_inbody_classification_val_accuracy.png)
+![A graph showing the validation accuracy over 25 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_endoscopic_inbody_classification_val_accuracy_v2.png)
 
 #### TensorRT speedup
 The `endoscopic_inbody_classification` bundle supports the TensorRT acceleration through the ONNX-TensorRT way. The table below shows the speedup ratios benchmarked on an A100 80G GPU.

models/model.pt

@@ -1,3 +1,3 @@
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-size 104506369
+oid sha256:dc8d874efa195e4416124aad4e1469e0b3ef873753e20948d762e7bfdb37b929
+size 104502013

models/model.ts

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:65f38a4ad17d48414c6a194a6cda11189535ec6263241d3b21f4c56cac579044
-size 104596723
+oid sha256:ba8c62eca55d37044d6d7631b1b0444709ac9009c202839dc054ca38bf732edf
+size 104609651