Update README.md

README.md

@@ -220,16 +220,16 @@ The metric takes four optional input parameters: __label2id__, __stuff__, __per_
 ## 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_.
-              For each key, it contains an array that holds the scores in the the columns in following order: PQ, SQ and RQ. If `split_sq_rq == False`, the columns consist of PQ only.
-              The number of rows corresponds to the given area ranges. That means, the results in each row are for a certain size of objects.
+              For each key, it contains an array that holds the scores in the the rows in following order: PQ, SQ and RQ. If `split_sq_rq == False`, the rows consist of PQ only.
+              The number of columns corresponds to the given area ranges. That means, the results in each column are for a certain size of objects.
 * __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 an array that consists of four elements in the columns: TP, FP, FN and IOU:
+               For each key, it contains an array that consists of four elements in the rows: 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. As for the scores, the number of rows corresponds to the given area ranges. That means, the results in each row are for a certain size of objects.
+  With all these values, it is possible to calculate the final scores. As for the scores, the number of columns corresponds to the given area ranges. That means, the results in each column are for a certain size of objects.
 
 ## Further References