Update README.md

README.md

@@ -111,17 +111,15 @@ The metric takes four optional input parameters: __label2id__, __stuff__, __per_
         ["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"
+    Setting this to False will aggregate the results (average the _scores_, sum up the _numbers_; see below for explanation of _scoress_ and _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 return the PQ score only (PQ=RQ*SQ).
 
 ## Output Values
 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__.
+* __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__.
+* __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