update model weights and perfomance metrics

README.md

@@ -61,22 +61,23 @@ Inference is performed on WSI in a sliding window manner with specified stride.
 ## Performance
 
 FROC score is used for evaluating the performance of the model. After inference is done, `evaluate_froc.sh` needs to be run to evaluate FROC score based on predicted probability map (output of inference) and the ground truth tumor masks.
-This model achieve the ~0.91 accuracy on validation patches, and FROC of 0.685 on the 48 Camelyon testing data that have ground truth annotations available.
+This model achieve the 0.91 accuracy on validation patches, and FROC of 0.72 on the 48 Camelyon testing data that have ground truth annotations available.
 
-![A Graph showing Train Acc, Train Loss, and Validation Acc](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_tumor_detection_train_and_val_metrics.png)
+![A Graph showing Train Acc, Train Loss, and Validation Acc](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_tumor_detection_train_and_val_metrics_v3.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.
 
 For more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html).
 
-#### Execute training:
+#### Execute training
 
 ```
 python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf
 ```
 
-#### Override the `train` config to execute multi-GPU training:
+#### Override the `train` config to execute multi-GPU training
 
 ```
 torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.json','configs/multi_gpu_train.json']" --logging_file configs/logging.conf
@@ -84,19 +85,19 @@ torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training
 
 Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
 
-#### Execute inference:
+#### Execute inference
 
 ```
 CUDA_LAUNCH_BLOCKING=1 python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf
 ```
 
-#### Evaluate FROC metric:
+#### Evaluate FROC metric
 
 ```
 cd scripts && source evaluate_froc.sh
 ```
 
-#### Export checkpoint to TorchScript file:
+#### Export checkpoint to TorchScript file
 
 TorchScript conversion is currently not supported.
 

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": "0.4.4",
+    "version": "0.4.5",
     "changelog": {
+        "0.4.5": "update model weights and perfomance metrics",
         "0.4.4": "restructure readme to match updated template",
         "0.4.3": "fix wrong figure url",
         "0.4.2": "update metadata with new metrics",

configs/train.json

@@ -372,7 +372,7 @@
         }
     },
     "training": [
-        "$monai.utils.set_determinism(seed=123)",
+        "$monai.utils.set_determinism(seed=15)",
         "$setattr(torch.backends.cudnn, 'benchmark', True)",
         "$@train#trainer.run()"
     ]

docs/README.md

@@ -54,22 +54,23 @@ Inference is performed on WSI in a sliding window manner with specified stride.
 ## Performance
 
 FROC score is used for evaluating the performance of the model. After inference is done, `evaluate_froc.sh` needs to be run to evaluate FROC score based on predicted probability map (output of inference) and the ground truth tumor masks.
-This model achieve the ~0.91 accuracy on validation patches, and FROC of 0.685 on the 48 Camelyon testing data that have ground truth annotations available.
+This model achieve the 0.91 accuracy on validation patches, and FROC of 0.72 on the 48 Camelyon testing data that have ground truth annotations available.
 
-![A Graph showing Train Acc, Train Loss, and Validation Acc](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_tumor_detection_train_and_val_metrics.png)
+![A Graph showing Train Acc, Train Loss, and Validation Acc](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_tumor_detection_train_and_val_metrics_v3.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.
 
 For more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html).
 
-#### Execute training:
+#### Execute training
 
 ```
 python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf
 ```
 
-#### Override the `train` config to execute multi-GPU training:
+#### Override the `train` config to execute multi-GPU training
 
 ```
 torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.json','configs/multi_gpu_train.json']" --logging_file configs/logging.conf
@@ -77,19 +78,19 @@ torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training
 
 Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
 
-#### Execute inference:
+#### Execute inference
 
 ```
 CUDA_LAUNCH_BLOCKING=1 python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf
 ```
 
-#### Evaluate FROC metric:
+#### Evaluate FROC metric
 
 ```
 cd scripts && source evaluate_froc.sh
 ```
 
-#### Export checkpoint to TorchScript file:
+#### Export checkpoint to TorchScript file
 
 TorchScript conversion is currently not supported.
 

models/model.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9b2684dad87e6c1d25e765c6408a6a0f387c1887eb318a6b5b367e3d963231ac
+oid sha256:7383884d1a14a2e3d7e51ad209181f18c70ad2af0fa3f69d42faed2b165d0455
 size 44780565