BPMN element detection

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

It achieves the following results on the evaluation set:

Loss:
Precision:
Recall:
F1:
Accuracy:

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.

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate:
train_batch_size:
eval_batch_size:
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs:

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
2.0586	1.0	10	1.5601	0.1278	0.1559	0.1404	0.4750
1.3702	2.0	20	1.0113	0.3947	0.5645	0.4646	0.7150
0.8872	3.0	30	0.6645	0.5224	0.6882	0.5940	0.8051
0.5341	4.0	40	0.4741	0.6754	0.8280	0.7440	0.8541
0.3221	5.0	50	0.3831	0.7523	0.8817	0.8119	0.8883
0.2168	6.0	60	0.3297	0.7731	0.8978	0.8308	0.9079
0.1565	7.0	70	0.2998	0.8195	0.9032	0.8593	0.9128
0.1227	8.0	80	0.3227	0.8038	0.9032	0.8506	0.9099
0.0957	9.0	90	0.2840	0.8431	0.9247	0.8821	0.9216
0.077	10.0	100	0.2914	0.8252	0.9140	0.8673	0.9216
0.0691	11.0	110	0.2850	0.8431	0.9247	0.8821	0.9285
0.059	12.0	120	0.2886	0.8564	0.9301	0.8918	0.9285
0.0528	13.0	130	0.2838	0.8564	0.9301	0.8918	0.9305
0.0488	14.0	140	0.2881	0.8515	0.9247	0.8866	0.9305
0.049	15.0	150	0.2909	0.8557	0.9247	0.8889	0.9285