Update README.md

README.md

@@ -15,44 +15,47 @@ base_model:
 - glasses/densenet201
 pipeline_tag: image-segmentation
 model-index:
-  - name: Skin-Lesion-Segmentation
-    results:
-      - task:
-          type: image-segmentation
-        dataset:
-          name: isic2016
-          type: image
-        metrics:
-          - name: accuracy
-            type: float
-            value: 98.04
-          - name: precision
-            type: float
-            value: 97.09
-          - name: IoU (jaccard index)
-            type: float
-            value: 90.86
-          - name: F1-score (dice coefficient)
-            type: float
-            value: 94.78
-      - task:
-          type: image-segmentation
-        dataset:
-          name: isic2017
-          type: image
-        metrics:
-          - name: accuracy
-            type: float
-            value: 93.06
-          - name: precision
-            type: float
-            value: 98.63
-          - name: IoU (jaccard index)
-            type: float
-            value: 89.97
-          - name: F1-score (dice coefficient)
-            type: float
-            value: 94.35
+- name: Skin-Lesion-Segmentation
+  results:
+  - task:
+      type: image-segmentation
+    dataset:
+      name: isic2016
+      type: image
+    metrics:
+    - name: accuracy
+      type: float
+      value: 98.04
+    - name: precision
+      type: float
+      value: 97.09
+    - name: IoU (jaccard index)
+      type: float
+      value: 90.86
+    - name: F1-score (dice coefficient)
+      type: float
+      value: 94.78
+  - task:
+      type: image-segmentation
+    dataset:
+      name: isic2017
+      type: image
+    metrics:
+    - name: accuracy
+      type: float
+      value: 93.06
+    - name: precision
+      type: float
+      value: 98.63
+    - name: IoU (jaccard index)
+      type: float
+      value: 89.97
+    - name: F1-score (dice coefficient)
+      type: float
+      value: 94.35
+tags:
+- tensorflow
+- keras
 ---
 
 A precise segmentation model trained on the ISIC2016 and 2017 datasets. Throws an accuracy of 98.06% and a Jaccard Index of 90.86. Based on the U-Net architecture with a DenseNet201 backbone.