Terminology

Annotate:

Manually select target objects in images and add labels to them.

Label:

The tag added to an image after annotation to identify its class.

Dataset:

The .dlkdb file containing annotated data exported by Mech-DLK.

Labeled:

The image data status of having been annotated manually.

Unlabeled:

The image data status of having not been annotated manually.

Training Set:

An image data set that has been annotated manually and is used to train the model.

Validation Set:

An image data set that has been annotated manually and is used to validate the training effect of the model.

OK Image:

A defect-free image.

NG Image:

An image with object defect.

Train:

The process of using a training set to train a deep learning model.

Validate:

The process of using a trained model to predict on the validation set and comparing the results with the validation set labels.

Accuracy:

The ratio of the number of correctly predicted samples to the total number of samples when the model predicts on a validation set.

Loss:

The degree of inconsistency between the validation set result labels from model prediction and the actual labels.

Epoch:

The number of passes of the entire training set the machine learning algorithm has completed for training.