Statistics

Calculating statistics

To calculate the statistics, you first need to classify OK and NOK images by hand or using the ‘Class name by filename’ function. It is also possible to select multiple images at once using the ‘Shift’ key and classify them in bulk. After you submit your classification, click the ‘Show result’ button.

To be able to calculate all statistics, it is necessary to enable evaluation in any active module. If evaluation is not enabled, only the processing times are computed for annotated images.

Information in statistics

The statistics result shows a confusion matrix, which illustrates how the ‘predicted’ (evaluated by application) and ‘actual’ (annotated for statistics) results correspond (or not) to each other.

There can be 4 results, as shown in this image:

True positive (TP) - user classified image as ‘Good’ and application evaluated image as ‘Good’
False positive (FP) - user classified image as ‘Bad’ but application evaluated image as ‘Good’
False negative (FN) - user classified image as ‘Good’ but application evaluated image as ‘Bad’
True negative (TN) - user classified image as ‘Bad’ but application evaluated image as ‘Bad’

The matrix shows how many images ended up in each of those categories.

Next to the matrix is a table showing values for recall, precision and processing times (min, max and average).

Recall = TP / (TP + FN)

What percentage of images classified as ‘Good’ by the user were evaluated as ‘Good’ by the application.

Precision = TP / (TP + FP)

What percentage of images evaluated as ‘Good’ by the application were actually ‘Good’ (classified as ‘Good’ by the user).

Download report

After the statistics is successfully calculated, you can create an automatically generated report. The resulting report will be in HTML format.

You can choose how many images will be shown and whether you want to display only testing images (tick ‘Testing only’) or also training images. The entered amount of images is then randomly selected from all images classified in statistics (for testing images) and from all training images (if training images are enabled).

You can toggle whether rectangles and heatmaps should be shown on the evaluated images or choose to filter out images (e.g. show only images where ‘predicted’ is different from ‘actual’, images predicted as Good/Bad or images actually Good/Bad).

If you hover your mouse over an image, a zoom icon shows up which allows you to show the image in bigger view when you click it.