update retrained validation results and training curve

README.md

@@ -57,18 +57,18 @@ In Training Mode: A dictionary of classification and box regression loss.
 In Evaluation Mode: A list of dictionaries of predicted box, classification label, and classification score.
 
 ## Performance
-Coco metric is used for evaluating the performance of the model. The pre-trained model was trained and validated on data fold 0. This model achieves a mAP=0.853, mAR=0.994, AP(IoU=0.1)=0.862, AR(IoU=0.1)=1.0.
+Coco metric is used for evaluating the performance of the model. The pre-trained model was trained and validated on data fold 0. This model achieves a mAP=0.852, mAR=0.998, AP(IoU=0.1)=0.858, AR(IoU=0.1)=1.0.
 
 Please note that this bundle is non-deterministic because of the max pooling layer used in the network. Therefore, reproducing the training process may not get exactly the same performance.
 Please refer to https://pytorch.org/docs/stable/notes/randomness.html#reproducibility for more details about reproducibility.
 
 #### Training Loss
-![A graph showing the detection train loss](https://developer.download.nvidia.com/assets/Clara/Images/monai_retinanet_detection_train_loss.png)
+![A graph showing the detection train loss](https://developer.download.nvidia.com/assets/Clara/Images/monai_retinanet_detection_train_loss_v2.png)
 
 #### Validation Accuracy
 The validation accuracy in this curve is the mean of mAP, mAR, AP(IoU=0.1), and AR(IoU=0.1) in Coco metric.
 
-![A graph showing the detection val accuracy](https://developer.download.nvidia.com/assets/Clara/Images/monai_retinanet_detection_val_acc.png)
+![A graph showing the detection val accuracy](https://developer.download.nvidia.com/assets/Clara/Images/monai_retinanet_detection_val_acc_v2.png)
 
 ## MONAI Bundle Commands
 In addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.

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": "0.5.4",
+    "version": "0.5.5",
     "changelog": {
+        "0.5.5": "update retrained validation results and training curve",
         "0.5.4": "add non-deterministic note",
         "0.5.3": "adapt to BundleWorkflow interface",
         "0.5.2": "black autofix format and add name tag",
@@ -37,9 +38,9 @@
     "label_classes": "dict data, containing Nx6 box and Nx1 classification labels.",
     "pred_classes": "dict data, containing Nx6 box, Nx1 classification labels, Nx1 classification scores.",
     "eval_metrics": {
-        "mAP_IoU_0.10_0.50_0.05_MaxDet_100": 0.853,
-        "AP_IoU_0.10_MaxDet_100": 0.862,
-        "mAR_IoU_0.10_0.50_0.05_MaxDet_100": 0.994,
+        "mAP_IoU_0.10_0.50_0.05_MaxDet_100": 0.852,
+        "AP_IoU_0.10_MaxDet_100": 0.858,
+        "mAR_IoU_0.10_0.50_0.05_MaxDet_100": 0.998,
         "AR_IoU_0.10_MaxDet_100": 1.0
     },
     "intended_use": "This is an example, not to be used for diagnostic purposes",

configs/train.json

@@ -11,7 +11,7 @@
     "train_datalist": "$monai.data.load_decathlon_datalist(@data_list_file_path, is_segmentation=True, data_list_key='training', base_dir=@dataset_dir)",
     "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
     "epochs": 300,
-    "val_interval": 10,
+    "val_interval": 5,
     "learning_rate": 0.01,
     "amp": true,
     "batch_size": 4,
@@ -89,7 +89,7 @@
     "after_scheduler": {
         "_target_": "torch.optim.lr_scheduler.StepLR",
         "optimizer": "@optimizer",
-        "step_size": 150,
+        "step_size": 160,
         "gamma": 0.1
     },
     "lr_scheduler": {
@@ -447,9 +447,7 @@
         }
     },
     "initialize": [
-        "os.environ['CUDA_LAUNCH_BLOCKING']=1",
-        "$monai.utils.set_determinism(seed=123)",
-        "$setattr(torch.backends.cudnn, 'benchmark', True)"
+        "$monai.utils.set_determinism(seed=0)"
     ],
     "run": [
         "$@train#trainer.run()"

docs/README.md

@@ -50,18 +50,18 @@ In Training Mode: A dictionary of classification and box regression loss.
 In Evaluation Mode: A list of dictionaries of predicted box, classification label, and classification score.
 
 ## Performance
-Coco metric is used for evaluating the performance of the model. The pre-trained model was trained and validated on data fold 0. This model achieves a mAP=0.853, mAR=0.994, AP(IoU=0.1)=0.862, AR(IoU=0.1)=1.0.
+Coco metric is used for evaluating the performance of the model. The pre-trained model was trained and validated on data fold 0. This model achieves a mAP=0.852, mAR=0.998, AP(IoU=0.1)=0.858, AR(IoU=0.1)=1.0.
 
 Please note that this bundle is non-deterministic because of the max pooling layer used in the network. Therefore, reproducing the training process may not get exactly the same performance.
 Please refer to https://pytorch.org/docs/stable/notes/randomness.html#reproducibility for more details about reproducibility.
 
 #### Training Loss
-![A graph showing the detection train loss](https://developer.download.nvidia.com/assets/Clara/Images/monai_retinanet_detection_train_loss.png)
+![A graph showing the detection train loss](https://developer.download.nvidia.com/assets/Clara/Images/monai_retinanet_detection_train_loss_v2.png)
 
 #### Validation Accuracy
 The validation accuracy in this curve is the mean of mAP, mAR, AP(IoU=0.1), and AR(IoU=0.1) in Coco metric.
 
-![A graph showing the detection val accuracy](https://developer.download.nvidia.com/assets/Clara/Images/monai_retinanet_detection_val_acc.png)
+![A graph showing the detection val accuracy](https://developer.download.nvidia.com/assets/Clara/Images/monai_retinanet_detection_val_acc_v2.png)
 
 ## MONAI Bundle Commands
 In addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.

models/model.pt

@@ -1,3 +1,3 @@
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-size 83719685
+oid sha256:b5e79231466adae93a6fe8e8594029e9add142914e223b879aa0343bb2402d01
+size 83709381

models/model.ts

@@ -1,3 +1,3 @@
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+oid sha256:68afd1ed4be8d01196d575d13931dab24cc50d46a74528a47d54496ba29e2583
+size 83784539