Update README.md

README.md

@@ -112,8 +112,8 @@ The metric takes four optional input parameters: __label2id__, __stuff__, __per_
 
 * `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"
+  * 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 return the PQ score only (PQ=RQ*SQ).
 
@@ -123,10 +123,10 @@ A dictionary containing the following keys:
               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
+  * __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