Update README.md

README.md

@@ -29,55 +29,60 @@ To get started with PanopticQuality, make sure you have the necessary dependenci
 >>>                         models=MODEL_FIELD,
 >>>                         sequence_list=["Trip_55_Seq_2", "Trip_197_Seq_1", "Trip_197_Seq_68"],
 >>>                         excluded_classes=[""]).payload
->>> module = evaluate.load("SEA-AI/PanopticQuality")
+>>> module = evaluate.load("SEA-AI/PanopticQuality", area_rng=[(0, 100),(100, 1e9)])
 >>> module.add_payload(payload, model_name=MODEL_FIELD[0])
 >>> module.compute()
 100%|██████████| 3/3 [00:03<00:00,  1.30s/it]
 Added data ...
 Start computing ...
 Finished!
-{'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]}}
+{'scores': {'MOTORBOAT': array([[0.        , 0.25889117],
+         [0.        , 0.79029936],
+         [0.        , 0.3275862 ]]),
+  'FAR_AWAY_OBJECT': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'SAILING_BOAT_WITH_CLOSED_SAILS': array([[0.        , 0.35410052],
+         [0.        , 0.75246359],
+         [0.        , 0.47058824]]),
+  'SHIP': array([[0.        , 0.47743301],
+         [0.        , 0.90181785],
+         [0.        , 0.52941179]]),
+  'WATERCRAFT': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'SPHERICAL_BUOY': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'FLOTSAM': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'SAILING_BOAT_WITH_OPEN_SAILS': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'CONTAINER': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'PILLAR_BUOY': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'AERIAL_ANIMAL': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'HUMAN_IN_WATER': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'WOODEN_LOG': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'MARITIME_ANIMAL': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'WATER': array([[0.        , 0.96737861],
+         [0.        , 0.96737861],
+         [0.        , 1.        ]]),
+  'SKY': array([[0.        , 0.93018024],
+         [0.   
 ```
 
 ## Metric Settings
@@ -118,15 +123,16 @@ 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 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.
+              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.
 * __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:
+               For each key, it contains an array that consists of four elements in the columns: 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.
+  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.
 
 ## Further References