Upload endoscopic_tool_segmentation version 0.6.2

configs/metadata.json

@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
-    "version": "0.6.1",
+    "version": "0.6.2",
     "changelog": {
+        "0.6.2": "enhance metadata with improved descriptions and task specification",
         "0.6.1": "update to huggingface hosting and fix missing dependencies",
         "0.6.0": "use monai 1.4 and update large files",
         "0.5.9": "update to use monai 1.3.1",
@@ -42,11 +43,11 @@
         "tensorboard": "2.17.0"
     },
     "supported_apps": {},
-    "name": "Endoscopic tool segmentation",
-    "task": "Endoscopic tool segmentation",
-    "description": "A pre-trained binary segmentation model for endoscopic tool segmentation",
-    "authors": "NVIDIA DLMED team",
-    "copyright": "Copyright (c) 2021-2022, NVIDIA CORPORATION",
+    "name": "Endoscopic Tool Segmentation",
+    "task": "Binary Segmentation of Surgical Tools in Endoscopic Images",
+    "description": "A 2D segmentation model that identifies and delineates surgical instruments in endoscopic video frames. The model processes 736x480 pixel RGB images and provides binary segmentation masks. Based on an EfficientNet-UNet architecture, the model supports real-time analysis of surgical procedures.",
+    "authors": "MONAI team",
+    "copyright": "Copyright (c) MONAI Consortium",
     "data_source": "private dataset",
     "data_type": "RGB",
     "image_classes": "three channel data, intensity [0-255]",