Update README.md

README.md

@@ -36,11 +36,52 @@ To get started with PanopticQuality, make sure you have the necessary dependenci
 Added data ...
 Start computing ...
 Finished!
-tensor(0.2082, dtype=torch.float64)
+{'scores': {'MOTORBOAT': [0.18632257426639526,
+   0.698709617058436,
+   0.2666666805744171],
+  'FAR_AWAY_OBJECT': [0.0, 0.0, 0.0],
+  'SAILING_BOAT_WITH_CLOSED_SAILS': [0.0, 0.0, 0.0],
+  'SHIP': [0.3621737026917471, 0.684105846616957, 0.529411792755127],
+  'WATERCRAFT': [0.0, 0.0, 0.0],
+  'SPHERICAL_BUOY': [0.0, 0.0, 0.0],
+  'FLOTSAM': [0.0, 0.0, 0.0],
+  'SAILING_BOAT_WITH_OPEN_SAILS': [0.0, 0.0, 0.0],
+  'CONTAINER': [0.0, 0.0, 0.0],
+  'PILLAR_BUOY': [0.0, 0.0, 0.0],
+  'AERIAL_ANIMAL': [0.0, 0.0, 0.0],
+  'HUMAN_IN_WATER': [0.0, 0.0, 0.0],
+  'WOODEN_LOG': [0.0, 0.0, 0.0],
+  'MARITIME_ANIMAL': [0.0, 0.0, 0.0],
+  'WATER': [0.9397601008415222, 0.9397601008415222, 1.0],
+  'SKY': [0.9674496332804362, 0.9674496332804362, 1.0],
+  'LAND': [0.30757412078761204, 0.8304501533508301, 0.37037035822868347],
+  'CONSTRUCTION': [0.0, 0.0, 0.0],
+  'OWN_BOAT': [0.0, 0.0, 0.0],
+  'ALL': [0.14543579641409013, 0.21686712374464112, 0.16665520166095935]},
+ 'numbers': {'MOTORBOAT': [6, 15, 18, 4.1922577023506165],
+  'FAR_AWAY_OBJECT': [0, 8, 9, 0.0],
+  'SAILING_BOAT_WITH_CLOSED_SAILS': [0, 2, 0, 0.0],
+  'SHIP': [9, 1, 15, 6.156952619552612],
+  'WATERCRAFT': [0, 9, 12, 0.0],
+  'SPHERICAL_BUOY': [0, 4, 22, 0.0],
+  'FLOTSAM': [0, 0, 1, 0.0],
+  'SAILING_BOAT_WITH_OPEN_SAILS': [0, 6, 0, 0.0],
+  'CONTAINER': [0, 0, 0, 0.0],
+  'PILLAR_BUOY': [0, 0, 9, 0.0],
+  'AERIAL_ANIMAL': [0, 0, 0, 0.0],
+  'HUMAN_IN_WATER': [0, 0, 0, 0.0],
+  'WOODEN_LOG': [0, 0, 0, 0.0],
+  'MARITIME_ANIMAL': [0, 0, 0, 0.0],
+  'WATER': [15, 0, 0, 14.096401512622833],
+  'SKY': [15, 0, 0, 14.511744499206543],
+  'LAND': [5, 9, 8, 4.15225076675415],
+  'CONSTRUCTION': [0, 0, 0, 0.0],
+  'OWN_BOAT': [0, 0, 8, 0.0],
+  'ALL': [50, 54, 102, 43.109607100486755]}}
 ```
 
 ## Metric Settings
-The metric takes two optional input parameters: __label2id__ and __stuff__.
+The metric takes four optional input parameters: __label2id__, __stuff__, __per_class__ and __split_sq_rq__.
 
 * `label2id: Dict[str, int]`: this dictionary is used to map string labels to an integer representation.
     if not provided a default setting will be used:
@@ -69,8 +110,24 @@ The metric takes two optional input parameters: __label2id__ and __stuff__.
         `
         ["WATER", "SKY", "LAND", "CONSTRUCTION", "ICE", "OWN_BOAT"]`
 
+* `per_class: bool = True`: By default, the results are split up per class.
+    Setting this to False will aggregate the results:
+      - average the "scores"
+      - sum up the "numbers"
+* `split_sq_rq: bool = True`: By default, the PQ-score is returned in three parts: the PQ score itself, and split into the segmentation quality (SQ) and recognition quality (RQ) part.
+    Setting this to False will aggregate return the PQ score only (PQ=RQ*SQ).
+
 ## Output Values
-A single float number between 0 and 1 is returned, which represents the PQ score. The bigger the number the better the PQ score, and vice versa.
+A dictionary containing the following keys:
+* __scores__: This is a dictionary, that contains a key for each label, if `per_class == True`. Otherwise it only contains the key __all__.
+              For each key, it contains a list that holds the scores in the following order: PQ, SQ and RQ. If `split_sq_rq == False`, the list consists of PQ only.
+* __numbers__: This is a dictionary, that contains a key for each label, if `per_class == True`. Otherwise it only contains the key __all__.
+               For each key, it contains a list that consists of four elements: TP, FP, FN and IOU:
+                 - __TP__: number of true positive predictions
+                 - __FP__: number of false positive predictions
+                 - __FN__: number of false negative predictions
+                 - __IOU__: sum of IOU of all TP predictions with ground truth
+               With all these values, it is possible to calculate the final scores.
 
 ## Further References