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	---
	license: mpl-2.0
	language:
	- en
	metrics:
	- f1
	- accuracy
	- recall
	- precision
	---
	---
	license: apache-2.0

	# BPMN element detection


	## Model description

	This project aims to detect Business Process Model and Notation (BPMN) elements from hand-drawn diagrams using a machine learning model. The model is trained to recognize various BPMN elements such as tasks, events, gateways, and connectors from images of hand-drawn diagrams.


	The dataset contains 15 target labels:

	- AGENT
	* `pool`
	* `lane`

	- TASK
	* `task`
	* `subProcess`

	- TASK_INFO
	* `dataObject`
	* `dataStore`

	- PROCESS_INFO
	* `background`

	- CONDITION
	* `exclusiveGateway`
	* `parallelGateway`
	* `eventBasedGateway`

	- EVENT
	* `event`
	* `messageEvent`
	* `timerEvent`

	- FLOW
	* `sequenceFlow`
	* `dataAssociation`
	* `messageFlow`

	## Results per type

	It achieves the following results on the evaluation set with objects:
	- Labels Precision: 0.97
	- Precision: 0.97
	- Recall: 0.95
	- F1: 0.96

	It achieves the following results on the evaluation set with arrows:
	- Labels precision: 0.98
	- Precision: 0.92
	- Recall: 0.93
	- F1: 0.92
	- Keypoints Accuracy: 0.71

	# Results per class

	\| Class \| Precision \| Recall \| F1 \|
	\|:-----------------:\|:---------:\|:--------:\|:-------:\|
	\| task \| 0.9518 \| 0.9875 \| 0.9693 \|
	\| exclusiveGateway \| 0.9548 \| 0.9427 \| 0.9487 \|
	\| event \| 0.9515 \| 0.9235 \| 0.9373 \|
	\| parallelGateway \| 0.9333 \| 0.9180 \| 0.9256 \|
	\| messageEvent \| 0.9291 \| 0.9365 \| 0.9328 \|
	\| pool \| 0.8797 \| 0.936 \| 0.9070 \|
	\| lane \| 0.9178 \| 0.67 \| 0.7746 \|
	\| dataObject \| 0.9333 \| 0.9565 \| 0.9448 \|
	\| dataStore \| 1.0 \| 0.64 \| 0.7805 \|
	\| eventBasedGateway \| 0.7273 \| 0.7273 \| 0.7273 \|
	\| timerEvent \| 0.8571 \| 0.75 \| 0.8 \|
	\| sequenceFlow \| 0.9292 \| 0.9605 \| 0.9446 \|
	\| dataAssociation \| 0.8472 \| 0.8095 \| 0.8279 \|
	\| messageFlow \| 0.8589 \| 0.7910 \| 0.8235 \|


	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 0.0176
	- train_batch_size: 4
	- eval_batch_size: 1
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 50

	### Example of Training results
	\| Epoch \| Avg Loss \| Test Loss \| Classifier Loss \| Box Reg Loss \| Objectness Loss \| RPN Box Reg Loss \| Precision \| Recall \| F1 Score \|
	\|:-----:\|:--------:\|:---------:\|:---------------:\|:------------:\|:---------------:\|:----------------:\|:---------:\|:------:\|:--------:\|
	\| 1 \| 3.9451 \| 2.0591 \| 2.4416 \| 0.5426 \| 0.6502 \| 0.3107 \| 0.2763 \| 0.0393 \| 0.0689 \|
	\| 2 \| 2.7259 \| 1.5387 \| 1.6724 \| 0.6697 \| 0.1868 \| 0.1969 \| 0.5754 \| 0.3358 \| 0.4241 \|
	\| 3 \| 2.2004 \| 1.1307 \| 1.3860 \| 0.5330 \| 0.1216 \| 0.1598 \| 0.8657 \| 0.6841 \| 0.7643 \|
	\| 4 \| 1.8611 \| 1.0110 \| 1.1775 \| 0.4172 \| 0.1099 \| 0.1565 \| 0.7708 \| 0.7790 \| 0.7749 \|
	\| 5 \| 1.7461 \| 0.9593 \| 1.1202 \| 0.3820 \| 0.0971 \| 0.1468 \| 0.8542 \| 0.8046 \| 0.8287 \|
	\| 6 \| 1.5859 \| 0.8956 \| 0.9986 \| 0.3590 \| 0.0872 \| 0.1412 \| 0.8884 \| 0.8002 \| 0.8420 \|
	\| 7 \| 1.5621 \| 0.9073 \| 1.0214 \| 0.3351 \| 0.0776 \| 0.1280 \| 0.9435 \| 0.8034 \| 0.8678 \|
	\| 8 \| 1.5194 \| 0.8695 \| 0.9881 \| 0.3261 \| 0.0738 \| 0.1314 \| 0.9048 \| 0.8246 \| 0.8628 \|
	\| 9 \| 1.5449 \| 0.9014 \| 1.0105 \| 0.3229 \| 0.0769 \| 0.1346 \| 0.9478 \| 0.8046 \| 0.8704 \|
	\| 10 \| 1.5805 \| 0.8134 \| 1.0333 \| 0.3338 \| 0.0703 \| 0.1431 \| 0.8920 \| 0.8920 \| 0.8920 \|
	\| 11 \| 1.3838 \| 0.8097 \| 0.8743 \| 0.3065 \| 0.0653 \| 0.1376 \| 0.9634 \| 0.8371 \| 0.8958 \|
	\| 12 \| 1.3582 \| 0.7362 \| 0.8751 \| 0.2909 \| 0.0617 \| 0.1306 \| 0.9457 \| 0.8596 \| 0.9006 \|
	\| 13 \| 1.3126 \| 0.7149 \| 0.8347 \| 0.2921 \| 0.0593 \| 0.1264 \| 0.9152 \| 0.9295 \| 0.9223 \|
	\| 14 \| 1.3532 \| 0.7775 \| 0.9079 \| 0.2783 \| 0.0543 \| 0.1128 \| 0.9639 \| 0.8508 \| 0.9038 \|
	\| 15 \| 1.3188 \| 0.6738 \| 0.8986 \| 0.2720 \| 0.0434 \| 0.1048 \| 0.8856 \| 0.9419 \| 0.9129 \|
	\| 16 \| 1.2512 \| 0.7478 \| 0.7840 \| 0.2784 \| 0.0621 \| 0.1268 \| 0.9181 \| 0.9101 \| 0.9141 \|
	\| 17 \| 1.2909 \| 0.6556 \| 0.8425 \| 0.2778 \| 0.0547 \| 0.1159 \| 0.9012 \| 0.9282 \| 0.9145 \|
	\| 18 \| 1.2526 \| 0.7003 \| 0.8442 \| 0.2607 \| 0.0443 \| 0.1034 \| 0.9169 \| 0.9020 \| 0.9094 \|
	\| 19 \| 1.1980 \| 0.7136 \| 0.8062 \| 0.2528 \| 0.0361 \| 0.1029 \| 0.9520 \| 0.9157 \| 0.9335 \|
	\| 20 \| 1.1821 \| 0.6308 \| 0.7895 \| 0.2517 \| 0.0378 \| 0.1030 \| 0.9023 \| 0.9513 \| 0.9262 \|
	\| 21 \| 1.0843 \| 0.6883 \| 0.7168 \| 0.2402 \| 0.0316 \| 0.0957 \| 0.9348 \| 0.9032 \| 0.9187 \|
	\| 22 \| 1.1058 \| 0.6192 \| 0.7367 \| 0.2336 \| 0.0374 \| 0.0981 \| 0.9321 \| 0.9513 \| 0.9416 \|
	\| 23 \| 1.0699 \| 0.5962 \| 0.7119 \| 0.2340 \| 0.0306 \| 0.0935 \| 0.9353 \| 0.9476 \| 0.9414 \|
	\| 24 \| 1.0616 \| 0.6674 \| 0.7031 \| 0.2367 \| 0.0311 \| 0.0908 \| 0.9418 \| 0.9301 \| 0.9359 \|
	\| 25 \| 1.0784 \| 0.6158 \| 0.7275 \| 0.2311 \| 0.0295 \| 0.0904 \| 0.9176 \| 0.9320 \| 0.9247 \|
	\| 26 \| 1.0618 \| 0.6483 \| 0.7121 \| 0.2283 \| 0.0297 \| 0.0916 \| 0.9411 \| 0.9182 \| 0.9295 \|
	\| 27 \| 1.0530 \| 0.5958 \| 0.7139 \| 0.2236 \| 0.0279 \| 0.0876 \| 0.9477 \| 0.9395 \| 0.9436 \|
	\| 28 \| 1.0452 \| 0.5964 \| 0.7097 \| 0.2223 \| 0.0283 \| 0.0849 \| 0.9465 \| 0.9494 \| 0.9480 \|
	\| 29 \| 1.0966 \| 0.6288 \| 0.7795 \| 0.2176 \| 0.0203 \| 0.0792 \| 0.9558 \| 0.9320 \| 0.9437 \|
	\| 30 \| 1.0506 \| 0.5956 \| 0.7312 \| 0.2142 \| 0.0195 \| 0.0856 \| 0.9370 \| 0.9370 \| 0.9370 \|
	\| 31 \| 1.0030 \| 0.6099 \| 0.6777 \| 0.2163 \| 0.0204 \| 0.0886 \| 0.9506 \| 0.9251 \| 0.9377 \|
	\| 32 \| 0.9748 \| 0.5976 \| 0.6610 \| 0.2098 \| 0.0201 \| 0.0839 \| 0.9527 \| 0.9313 \| 0.9419 \|
	\| 33 \| 0.9540 \| 0.5907 \| 0.6402 \| 0.2059 \| 0.0216 \| 0.0863 \| 0.9536 \| 0.9238 \| 0.9385 \|
	\| 34 \| 0.9730 \| 0.5809 \| 0.6500 \| 0.2076 \| 0.0281 \| 0.0873 \| 0.9407 \| 0.9413 \| 0.9410 \|
	\| 35 \| 0.9894 \| 0.5837 \| 0.6831 \| 0.2066 \| 0.0202 \| 0.0794 \| 0.9451 \| 0.9345 \| 0.9397 \|
	\| 36 \| 0.9042 \| 0.5534 \| 0.5873 \| 0.2096 \| 0.0214 \| 0.0860 \| 0.9460 \| 0.9519 \| 0.9490 \|
	\| 37 \| 0.9546 \| 0.5562 \| 0.6400 \| 0.2112 \| 0.0216 \| 0.0818 \| 0.9260 \| 0.9457 \| 0.9358 \|
	\| 38 \| 0.9806 \| 0.5792 \| 0.6800 \| 0.2031 \| 0.0175 \| 0.0800 \| 0.9476 \| 0.9363 \| 0.9419 \|
	\| 39 \| 0.9294 \| 0.5703 \| 0.6247 \| 0.2016 \| 0.0204 \| 0.0826 \| 0.9401 \| 0.9501 \| 0.9450 \|
	\| 40 \| 0.9786 \| 0.5880 \| 0.6733 \| 0.2010 \| 0.0268 \| 0.0775 \| 0.9375 \| 0.9170 \| 0.9271 \|
	\| 41 \| 1.0026 \| 0.5875 \| 0.7073 \| 0.2033 \| 0.0179 \| 0.0742 \| 0.9476 \| 0.9251 \| 0.9362 \|
	\| 42 \| 0.9567 \| 0.5724 \| 0.6677 \| 0.1992 \| 0.0164 \| 0.0734 \| 0.9468 \| 0.9332 \| 0.9400 \|
	\| 43 \| 0.8747 \| 0.5709 \| 0.5794 \| 0.1980 \| 0.0159 \| 0.0814 \| 0.9557 \| 0.9432 \| 0.9494 \|
	\| 44 \| 1.0310 \| 0.5497 \| 0.7392 \| 0.1956 \| 0.0254 \| 0.0709 \| 0.9589 \| 0.9313 \| 0.9449 \|
	\| 45 \| 0.9526 \| 0.5580 \| 0.6598 \| 0.1982 \| 0.0185 \| 0.0762 \| 0.9401 \| 0.9413 \| 0.9407 \|
	\| 46 \| 0.8753 \| 0.5548 \| 0.5940 \| 0.1939 \| 0.0176 \| 0.0698 \| 0.9468 \| 0.9438 \| 0.9453 \|
	\| 47 \| 0.9328 \| 0.5735 \| 0.6493 \| 0.1953 \| 0.0163 \| 0.0720 \| 0.9534 \| 0.9320 \| 0.9426 \|
	\| 48 \| 0.9019 \| 0.5605 \| 0.6071 \| 0.2002 \| 0.0182 \| 0.0765 \| 0.9496 \| 0.9413 \| 0.9455 \|
	\| 49 \| 0.8335 \| 0.5637 \| 0.5459 \| 0.1918 \| 0.0175 \| 0.0783 \| 0.9588 \| 0.9307 \| 0.9446 \|
	\| 50 \| 0.9043 \| 0.5617 \| 0.6179 \| 0.1933 \| 0.0154 \| 0.0776 \| 0.9597 \| 0.9370 \| 0.9482 \|

	---
	license: mpl-2.0
	language:
	- en
	metrics:
	- f1
	- accuracy
	- recall
	- precision
	---
	---
	license: apache-2.0

	# BPMN element detection


	## Model description

	This project aims to detect Business Process Model and Notation (BPMN) elements from hand-drawn diagrams using a machine learning model. The model is trained to recognize various BPMN elements such as tasks, events, gateways, and connectors from images of hand-drawn diagrams.


	The dataset contains 15 target labels:

	- AGENT
	* `pool`
	* `lane`

	- TASK
	* `task`
	* `subProcess`

	- TASK_INFO
	* `dataObject`
	* `dataStore`

	- PROCESS_INFO
	* `background`

	- CONDITION
	* `exclusiveGateway`
	* `parallelGateway`
	* `eventBasedGateway`

	- EVENT
	* `event`
	* `messageEvent`
	* `timerEvent`

	- FLOW
	* `sequenceFlow`
	* `dataAssociation`
	* `messageFlow`

	## Results per type

	It achieves the following results on the evaluation set with objects:
	- Labels Precision: 0.97
	- Precision: 0.97
	- Recall: 0.95
	- F1: 0.96

	It achieves the following results on the evaluation set with arrows:
	- Labels precision: 0.98
	- Precision: 0.92
	- Recall: 0.93
	- F1: 0.92
	- Keypoints Accuracy: 0.71

	# Results per class

	\| Class \| Precision \| Recall \| F1 \|
	\|:-----------------:\|:---------:\|:--------:\|:-------:\|
	\| task \| 0.9518 \| 0.9875 \| 0.9693 \|
	\| exclusiveGateway \| 0.9548 \| 0.9427 \| 0.9487 \|
	\| event \| 0.9515 \| 0.9235 \| 0.9373 \|
	\| parallelGateway \| 0.9333 \| 0.9180 \| 0.9256 \|
	\| messageEvent \| 0.9291 \| 0.9365 \| 0.9328 \|
	\| pool \| 0.8797 \| 0.936 \| 0.9070 \|
	\| lane \| 0.9178 \| 0.67 \| 0.7746 \|
	\| dataObject \| 0.9333 \| 0.9565 \| 0.9448 \|
	\| dataStore \| 1.0 \| 0.64 \| 0.7805 \|
	\| eventBasedGateway \| 0.7273 \| 0.7273 \| 0.7273 \|
	\| timerEvent \| 0.8571 \| 0.75 \| 0.8 \|
	\| sequenceFlow \| 0.9292 \| 0.9605 \| 0.9446 \|
	\| dataAssociation \| 0.8472 \| 0.8095 \| 0.8279 \|
	\| messageFlow \| 0.8589 \| 0.7910 \| 0.8235 \|


	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 0.0176
	- train_batch_size: 4
	- eval_batch_size: 1
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 50

	### Example of Training results
	\| Epoch \| Avg Loss \| Test Loss \| Classifier Loss \| Box Reg Loss \| Objectness Loss \| RPN Box Reg Loss \| Precision \| Recall \| F1 Score \|
	\|:-----:\|:--------:\|:---------:\|:---------------:\|:------------:\|:---------------:\|:----------------:\|:---------:\|:------:\|:--------:\|
	\| 1 \| 3.9451 \| 2.0591 \| 2.4416 \| 0.5426 \| 0.6502 \| 0.3107 \| 0.2763 \| 0.0393 \| 0.0689 \|
	\| 2 \| 2.7259 \| 1.5387 \| 1.6724 \| 0.6697 \| 0.1868 \| 0.1969 \| 0.5754 \| 0.3358 \| 0.4241 \|
	\| 3 \| 2.2004 \| 1.1307 \| 1.3860 \| 0.5330 \| 0.1216 \| 0.1598 \| 0.8657 \| 0.6841 \| 0.7643 \|
	\| 4 \| 1.8611 \| 1.0110 \| 1.1775 \| 0.4172 \| 0.1099 \| 0.1565 \| 0.7708 \| 0.7790 \| 0.7749 \|
	\| 5 \| 1.7461 \| 0.9593 \| 1.1202 \| 0.3820 \| 0.0971 \| 0.1468 \| 0.8542 \| 0.8046 \| 0.8287 \|
	\| 6 \| 1.5859 \| 0.8956 \| 0.9986 \| 0.3590 \| 0.0872 \| 0.1412 \| 0.8884 \| 0.8002 \| 0.8420 \|
	\| 7 \| 1.5621 \| 0.9073 \| 1.0214 \| 0.3351 \| 0.0776 \| 0.1280 \| 0.9435 \| 0.8034 \| 0.8678 \|
	\| 8 \| 1.5194 \| 0.8695 \| 0.9881 \| 0.3261 \| 0.0738 \| 0.1314 \| 0.9048 \| 0.8246 \| 0.8628 \|
	\| 9 \| 1.5449 \| 0.9014 \| 1.0105 \| 0.3229 \| 0.0769 \| 0.1346 \| 0.9478 \| 0.8046 \| 0.8704 \|
	\| 10 \| 1.5805 \| 0.8134 \| 1.0333 \| 0.3338 \| 0.0703 \| 0.1431 \| 0.8920 \| 0.8920 \| 0.8920 \|
	\| 11 \| 1.3838 \| 0.8097 \| 0.8743 \| 0.3065 \| 0.0653 \| 0.1376 \| 0.9634 \| 0.8371 \| 0.8958 \|
	\| 12 \| 1.3582 \| 0.7362 \| 0.8751 \| 0.2909 \| 0.0617 \| 0.1306 \| 0.9457 \| 0.8596 \| 0.9006 \|
	\| 13 \| 1.3126 \| 0.7149 \| 0.8347 \| 0.2921 \| 0.0593 \| 0.1264 \| 0.9152 \| 0.9295 \| 0.9223 \|
	\| 14 \| 1.3532 \| 0.7775 \| 0.9079 \| 0.2783 \| 0.0543 \| 0.1128 \| 0.9639 \| 0.8508 \| 0.9038 \|
	\| 15 \| 1.3188 \| 0.6738 \| 0.8986 \| 0.2720 \| 0.0434 \| 0.1048 \| 0.8856 \| 0.9419 \| 0.9129 \|
	\| 16 \| 1.2512 \| 0.7478 \| 0.7840 \| 0.2784 \| 0.0621 \| 0.1268 \| 0.9181 \| 0.9101 \| 0.9141 \|
	\| 17 \| 1.2909 \| 0.6556 \| 0.8425 \| 0.2778 \| 0.0547 \| 0.1159 \| 0.9012 \| 0.9282 \| 0.9145 \|
	\| 18 \| 1.2526 \| 0.7003 \| 0.8442 \| 0.2607 \| 0.0443 \| 0.1034 \| 0.9169 \| 0.9020 \| 0.9094 \|
	\| 19 \| 1.1980 \| 0.7136 \| 0.8062 \| 0.2528 \| 0.0361 \| 0.1029 \| 0.9520 \| 0.9157 \| 0.9335 \|
	\| 20 \| 1.1821 \| 0.6308 \| 0.7895 \| 0.2517 \| 0.0378 \| 0.1030 \| 0.9023 \| 0.9513 \| 0.9262 \|
	\| 21 \| 1.0843 \| 0.6883 \| 0.7168 \| 0.2402 \| 0.0316 \| 0.0957 \| 0.9348 \| 0.9032 \| 0.9187 \|
	\| 22 \| 1.1058 \| 0.6192 \| 0.7367 \| 0.2336 \| 0.0374 \| 0.0981 \| 0.9321 \| 0.9513 \| 0.9416 \|
	\| 23 \| 1.0699 \| 0.5962 \| 0.7119 \| 0.2340 \| 0.0306 \| 0.0935 \| 0.9353 \| 0.9476 \| 0.9414 \|
	\| 24 \| 1.0616 \| 0.6674 \| 0.7031 \| 0.2367 \| 0.0311 \| 0.0908 \| 0.9418 \| 0.9301 \| 0.9359 \|
	\| 25 \| 1.0784 \| 0.6158 \| 0.7275 \| 0.2311 \| 0.0295 \| 0.0904 \| 0.9176 \| 0.9320 \| 0.9247 \|
	\| 26 \| 1.0618 \| 0.6483 \| 0.7121 \| 0.2283 \| 0.0297 \| 0.0916 \| 0.9411 \| 0.9182 \| 0.9295 \|
	\| 27 \| 1.0530 \| 0.5958 \| 0.7139 \| 0.2236 \| 0.0279 \| 0.0876 \| 0.9477 \| 0.9395 \| 0.9436 \|
	\| 28 \| 1.0452 \| 0.5964 \| 0.7097 \| 0.2223 \| 0.0283 \| 0.0849 \| 0.9465 \| 0.9494 \| 0.9480 \|
	\| 29 \| 1.0966 \| 0.6288 \| 0.7795 \| 0.2176 \| 0.0203 \| 0.0792 \| 0.9558 \| 0.9320 \| 0.9437 \|
	\| 30 \| 1.0506 \| 0.5956 \| 0.7312 \| 0.2142 \| 0.0195 \| 0.0856 \| 0.9370 \| 0.9370 \| 0.9370 \|
	\| 31 \| 1.0030 \| 0.6099 \| 0.6777 \| 0.2163 \| 0.0204 \| 0.0886 \| 0.9506 \| 0.9251 \| 0.9377 \|
	\| 32 \| 0.9748 \| 0.5976 \| 0.6610 \| 0.2098 \| 0.0201 \| 0.0839 \| 0.9527 \| 0.9313 \| 0.9419 \|
	\| 33 \| 0.9540 \| 0.5907 \| 0.6402 \| 0.2059 \| 0.0216 \| 0.0863 \| 0.9536 \| 0.9238 \| 0.9385 \|
	\| 34 \| 0.9730 \| 0.5809 \| 0.6500 \| 0.2076 \| 0.0281 \| 0.0873 \| 0.9407 \| 0.9413 \| 0.9410 \|
	\| 35 \| 0.9894 \| 0.5837 \| 0.6831 \| 0.2066 \| 0.0202 \| 0.0794 \| 0.9451 \| 0.9345 \| 0.9397 \|
	\| 36 \| 0.9042 \| 0.5534 \| 0.5873 \| 0.2096 \| 0.0214 \| 0.0860 \| 0.9460 \| 0.9519 \| 0.9490 \|
	\| 37 \| 0.9546 \| 0.5562 \| 0.6400 \| 0.2112 \| 0.0216 \| 0.0818 \| 0.9260 \| 0.9457 \| 0.9358 \|
	\| 38 \| 0.9806 \| 0.5792 \| 0.6800 \| 0.2031 \| 0.0175 \| 0.0800 \| 0.9476 \| 0.9363 \| 0.9419 \|
	\| 39 \| 0.9294 \| 0.5703 \| 0.6247 \| 0.2016 \| 0.0204 \| 0.0826 \| 0.9401 \| 0.9501 \| 0.9450 \|
	\| 40 \| 0.9786 \| 0.5880 \| 0.6733 \| 0.2010 \| 0.0268 \| 0.0775 \| 0.9375 \| 0.9170 \| 0.9271 \|
	\| 41 \| 1.0026 \| 0.5875 \| 0.7073 \| 0.2033 \| 0.0179 \| 0.0742 \| 0.9476 \| 0.9251 \| 0.9362 \|
	\| 42 \| 0.9567 \| 0.5724 \| 0.6677 \| 0.1992 \| 0.0164 \| 0.0734 \| 0.9468 \| 0.9332 \| 0.9400 \|
	\| 43 \| 0.8747 \| 0.5709 \| 0.5794 \| 0.1980 \| 0.0159 \| 0.0814 \| 0.9557 \| 0.9432 \| 0.9494 \|
	\| 44 \| 1.0310 \| 0.5497 \| 0.7392 \| 0.1956 \| 0.0254 \| 0.0709 \| 0.9589 \| 0.9313 \| 0.9449 \|
	\| 45 \| 0.9526 \| 0.5580 \| 0.6598 \| 0.1982 \| 0.0185 \| 0.0762 \| 0.9401 \| 0.9413 \| 0.9407 \|
	\| 46 \| 0.8753 \| 0.5548 \| 0.5940 \| 0.1939 \| 0.0176 \| 0.0698 \| 0.9468 \| 0.9438 \| 0.9453 \|
	\| 47 \| 0.9328 \| 0.5735 \| 0.6493 \| 0.1953 \| 0.0163 \| 0.0720 \| 0.9534 \| 0.9320 \| 0.9426 \|
	\| 48 \| 0.9019 \| 0.5605 \| 0.6071 \| 0.2002 \| 0.0182 \| 0.0765 \| 0.9496 \| 0.9413 \| 0.9455 \|
	\| 49 \| 0.8335 \| 0.5637 \| 0.5459 \| 0.1918 \| 0.0175 \| 0.0783 \| 0.9588 \| 0.9307 \| 0.9446 \|
	\| 50 \| 0.9043 \| 0.5617 \| 0.6179 \| 0.1933 \| 0.0154 \| 0.0776 \| 0.9597 \| 0.9370 \| 0.9482 \|