DOC: Update HoVerNetPlus metrics

tiatoolbox/models/architecture/hovernetplus.py

@@ -34,22 +34,61 @@
 
 
 class HoVerNetPlus(HoVerNet):
-    """Initialise HoVerNet+.
+    """Initialise HoVerNet+ [1].
 
     HoVerNet+ takes an RGB input image, and provides the option to simultaneously
     segment and classify the nuclei present, aswell as semantically segment different
     regions or layers in the images. Note the HoVerNet+ architecture assumes an image
     resolution of 0.5 mpp, in contrast to HoVerNet at 0.25 mpp.
 
+    The tiatoolbox model should produce following results on the specified datasets
+    that is was trained on.
+
+    .. list-table:: HoVerNet+ Performance for Nuclear Instance Segmentation
+       :widths: 15 15 15 15 15 15 15
+       :header-rows: 1
+
+       * - Model name
+         - Data set
+         - DICE
+         - AJI
+         - DQ
+         - SQ
+         - PQ
+       * - hovernetplus-oed
+         - OED
+         - 0.84
+         - 0.69
+         - 0.86
+         - 0.80
+         - 0.69
+
+    .. list-table:: HoVerNet+ Mean Performance for Semantic Segmentation
+       :widths: 15 15 15 15 15 15
+       :header-rows: 1
+
+       * - Model name
+         - Data set
+         - F1
+         - Precision
+         - Recall
+         - Accuracy
+       * - hovernetplus-oed
+         - OED
+         - 0.82
+         - 0.82
+         - 0.82
+         - 0.84
+
     Args:
         num_input_channels (int): The number of input channels, default = 3 for RGB.
         num_types (int): The number of types of nuclei present in the images.
         num_layers (int): The number of layers/different regions types present.
 
     References:
-        Shephard, Adam J., et al. "Simultaneous Nuclear Instance and Layer Segmentation
-        in Oral Epithelial Dysplasia." Proceedings of the IEEE/CVF International
-        Conference on Computer Vision. 2021.
+        [1] Shephard, Adam J., et al. "Simultaneous Nuclear Instance and Layer
+        Segmentation in Oral Epithelial Dysplasia." Proceedings of the IEEE/CVF
+        International Conference on Computer Vision. 2021.
 
 
     """