Classify
Classify parts by predicting their label and creating part sets.
Perform this task on parts that do not have manually assigned labels.
For example, if a machine learning model trained to distinguish springs from dampers is used to classify wheel rims and suspension control arms, the spring and damper labels are also going to be assigned to all wheel rims and control arms selected for classification.
Certainty Ratio, Unclear Predictions, and Unrecognized Parts
When a part is processed by a trained machine learning model, the likelihood of the part matching each label is calculated. Sometimes, predictions are returned that the machine learning model knows are uncertain.
- If the certainty ratio calculated for a part is greater than the threshold
value, then the machine learning model prediction is considered uncertain,
and the part is assigned to a part set
named:
<label_name>Unclear(_<certainty_ratio>)_<iteration>
- If the certainty ratio calculated for a part is lower than the defined
threshold, then the prediction is considered certain enough and the part is
assigned to a part set
named:
<label_name>(_<certainty_ratio>)_<iteration>
- If the part does not appear to be shaped like any parts that have been
labeled, then the machine learning model marks the part as unrecognized and
assigns it to a part set
named:
Unrecognized(_<certainty_ratio>)_<iteration>
Add Trained Model File
Add a third-party trained machine learning model file.
The resultant binary (.aic) files from model training can be shared and used on different machines.