add mvtec example imgs
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lukas-heilgenbrunner 2024-11-11 14:30:21 +01:00
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#import "@preview/subpar:0.1.1"
= Material and Methods = Material and Methods
== Material == Material
=== MVTec AD === MVTec AD
MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on industrial inspection. MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on industrial inspection.
It contains over 5000 high-resolution images divided into fifteen different object and texture categories. It contains 5354 high-resolution images divided into fifteen different object and texture categories.
Each category comprises a set of defect-free training images and a test set of images with various kinds of defects as well as images without defects. Each category comprises a set of defect-free training images and a test set of images with various kinds of defects as well as images without defects.
#figure( #figure(
image("rsc/dataset_overview_large.png", width: 80%), image("rsc/mvtec/dataset_overview_large.png", width: 80%),
caption: [Architecture convolutional neural network. #cite(<datasetsampleimg>)], caption: [Architecture convolutional neural network. #cite(<datasetsampleimg>)],
) <datasetoverview> ) <datasetoverview>
// todo In this bachelor thesis only two categories are used. The categories are "Bottle" and "Cable".
Todo: descibe which categories are used in this bac and how many samples there are.
The bottle category contains of 3 different defect classes: 'broken_large', 'broken_small' and 'contamination'.
#subpar.grid(
figure(image("rsc/mvtec/bottle/broken_large_example.png"), caption: [
Broken large defect
]), <a>,
figure(image("rsc/mvtec/bottle/broken_small_example.png"), caption: [
Broken small defect
]), <b>,
figure(image("rsc/mvtec/bottle/contamination_example.png"), caption: [
Contamination defect
]), <c>,
columns: (1fr, 1fr, 1fr),
caption: [Bottle category different defect classes],
label: <full>,
)
Whereas cable has a lot more defect classes: 'bent_wire', 'cable_swap', 'combined', 'cut_inner_insulation',
'cut_outer_insulation', 'missing_cable', 'missing_wire', 'poke_insulation'.
So many more defect classes are already an indication that a classification task will be more difficult for the cable category.
#subpar.grid(
figure(image("rsc/mvtec/cable/bent_wire_example.png"), caption: [
Bent wire defect
]), <a>,
figure(image("rsc/mvtec/cable/cable_swap_example.png"), caption: [
Cable swap defect
]), <b>,
figure(image("rsc/mvtec/cable/combined_example.png"), caption: [
Combined defect
]), <c>,
figure(image("rsc/mvtec/cable/cut_inner_insulation_example.png"), caption: [
Cut inner insulation
]), <d>,
figure(image("rsc/mvtec/cable/cut_outer_insulation_example.png"), caption: [
Cut outer insulation
]), <e>,
figure(image("rsc/mvtec/cable/missing_cable_example.png"), caption: [
Mising cable defect
]), <e>,
figure(image("rsc/mvtec/cable/poke_insulation_example.png"), caption: [
Poke insulation defect
]), <f>,
figure(image("rsc/mvtec/cable/missing_wire_example.png"), caption: [
Missing wire defect
]), <g>,
columns: (1fr, 1fr, 1fr, 1fr),
caption: [Cable category different defect classes],
label: <full>,
)
== Methods == Methods

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