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Update README.md

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@@ -119,6 +119,10 @@ The metric takes four optional input parameters: __label2id__, __stuff__, __per_
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  Setting this to False will aggregate the results (average the _scores_, sum up the _numbers_; see below for explanation of _scores_ and _numbers_)
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  * `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.
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  Setting this to False will return the PQ score only (PQ=RQ*SQ).
 
 
 
 
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  ## Output Values
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  A dictionary containing the following keys:
 
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  Setting this to False will aggregate the results (average the _scores_, sum up the _numbers_; see below for explanation of _scores_ and _numbers_)
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  * `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.
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  Setting this to False will return the PQ score only (PQ=RQ*SQ).
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+ * `area_rng: List[Tuple[float]]`: The list holds all the area ranges for which results are calculated.
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+ Each range is represented by a Tuple, where the first element is the lower limit and the second is the upper limit of the area range.
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+ Each value represents total number of pixels of a mask.
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+ The parameter defaults to [(0, 1e8)].
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  ## Output Values
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  A dictionary containing the following keys: