update to use monai 1.0.1

configs/metadata.json

@@ -1,14 +1,15 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.2.1",
+    "version": "0.2.2",
     "changelog": {
+        "0.2.2": "update to use monai 1.0.1",
         "0.2.1": "enhance readme on commands example",
         "0.2.0": "update license files",
         "0.1.0": "complete the first version model package",
         "0.0.1": "initialize the model package structure"
     },
-    "monai_version": "1.0.0",
-    "pytorch_version": "1.12.0",
+    "monai_version": "1.0.1",
+    "pytorch_version": "1.13.0",
     "numpy_version": "1.22.4",
     "optional_packages_version": {
         "nibabel": "4.0.1",

docs/readme.md

@@ -6,7 +6,7 @@ This model is trained using the SEResNet50 structure, whose details can be found
 The [pytorch model](https://drive.google.com/file/d/14CS-s1uv2q6WedYQGeFbZeEWIkoyNa-x/view?usp=sharing) and [torchscript model](https://drive.google.com/file/d/1fOoJ4n5DWKHrt9QXTZ2sXwr9C-YvVGCM/view?usp=sharing) are shared in google drive. Modify the "bundle_root" parameter specified in configs/train.json and configs/inference.json to reflect where models are downloaded. Expected directory path to place downloaded models is "models/" under "bundle_root".
 
 ## Data
-Datasets used in this work were provided by [Activ Surgical](https://www.activsurgical.com/). Here is a [link](https://drive.google.com/uc?id=1rQQfHcZFs74OG0uJsL4vg7YqMWXV-s4k) of 20 samples (10 in-body and 10 out-body) to show what this dataset looks like. Modify the "dataset_dir" parameter specified in configs/train.json and configs/inference.json to reflect where the data is.
+Datasets used in this work were provided by [Activ Surgical](https://www.activsurgical.com/). Here is a [link](https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/inbody_outbody_samples.zip) of 20 samples (10 in-body and 10 out-body) to show what this dataset looks like. Modify the "dataset_dir" parameter specified in configs/train.json and configs/inference.json to reflect where the data is.
 
 The input label json should be a list made up by dicts which includes "image" and "label" keys. An example format is shown below.