Synthetic✦ Production-Ready⬡ Open SourceIndustrialManufacturingAnomaly DetectionComputer VisionQuality Control

MVTec Industrial Object Defects

High-resolution industrial components featuring precise bounding box annotations for structural defects and assembly errors. Diverse lighting and orientation variations provide robust training data for automated visual inspection and quality control systems.

Sample Frames

20 annotated samples drawn from the train split. Toggle annotations to inspect bounding-box quality.

All images in this dataset are 100% synthetically generated. No real-world footage was used.

Class Distribution

20 annotation classes · 1,000 total images. Sorted by object count, descending.

Annotation counts
020406080cable_missing_wire87transistor_cut_lead86transistor_bent_lead85cable_bent_wire72cable_cut_inner_insulation71metal_nut_scratch61cable_cut_outer_insulation54cable_missing_cable53cable_poke_insulation51cable_cable_swap50transistor_damaged_case50transistor_misplaced50metal_nut_bent50metal_nut_color50metal_nut_flip50screw_manipulated_front50screw_scratch_head50screw_scratch_neck50screw_thread_side50screw_thread_top50

Class Balance

Per-class counts, frequency, and average bounding-box area. Sort any column to surface the rarest or most prevalent classes for re-balancing.

20 / 20
Class
Images
with class
Objects
total
Per image
average
Area
% of frame
cable_cut_inner_insulation
50
71
1.42
11.50%
cable_missing_cable
50
53
1.06
9.18%
cable_cut_outer_insulation
50
54
1.08
2.49%
cable_cable_swap
50
50
1
10.26%
cable_missing_wire
50
87
1.74
10.33%
cable_poke_insulation
50
51
1.02
4.05%
transistor_bent_lead
50
85
1.70
6.44%
transistor_cut_lead
50
86
1.72
0.69%
transistor_damaged_case
50
50
1
16.11%
transistor_misplaced
50
50
1
59.71%
metal_nut_bent
50
50
1
4.49%
metal_nut_color
50
50
1
5.29%
metal_nut_flip
50
50
1
76.22%
metal_nut_scratch
50
61
1.22
9.77%
screw_manipulated_front
50
50
1
1.68%
screw_scratch_head
50
50
1
5.25%
screw_scratch_neck
50
50
1
5.07%
screw_thread_side
50
50
1
11.43%
screw_thread_top
50
50
1
2.23%
cable_bent_wire
49
72
1.47
11.51%

Co-occurrence Matrix

How frequently pairs of classes appear in the same image. Diagonal cells show standalone image count for that class. Useful for spotting biased or correlated labels.

Pair frequency
cable_bent_wi…cable_cut_inn…cable_missing…cable_cut_out…cable_cable_s…cable_missing…cable_poke_in…transistor_be…transistor_cu…transistor_da…transistor_mi…metal_nut_bentmetal_nut_col…metal_nut_flipmetal_nut_scr…screw_manipul…screw_scratch…screw_scratch…screw_thread_…screw_thread_…
cable_bent_wire49
cable_cut_inner_insulation50
cable_missing_cable50
cable_cut_outer_insulation50
cable_cable_swap50
cable_missing_wire50
cable_poke_insulation50
transistor_bent_lead50
transistor_cut_lead50
transistor_damaged_case50
transistor_misplaced50
metal_nut_bent50
metal_nut_color50
metal_nut_flip50
metal_nut_scratch50
screw_manipulated_front50
screw_scratch_head50
screw_scratch_neck50
screw_thread_side50
screw_thread_top50
Hover any cell for image count00

Average Object Area

Each rectangle is one class, sized by the average area its bounding boxes occupy as a percentage of the frame. Surfaces tiny vs. dominant objects at a glance.

% of frame
metal_nut_flip76.2%transistor_misplaced59.7%cable_bent_wire11.5%screw_thread_side11.4%cable_cable_swap10.3%cable_missing_cable9.2%metal_nut_color5.3%screw_scratch_neck5.1%cable_poke_insulation4.0%screw_thread_top2.2%

Spatial Distribution

Where annotations of each class tend to fall across the frame. Brighter regions indicate higher density — useful for detecting positional bias in your training data.

Per-class heatmaps
cable_bent_wire72
cable_cut_inner_insulation71
cable_missing_cable53
cable_cut_outer_insulation54
cable_cable_swap50
cable_missing_wire87
cable_poke_insulation51
transistor_bent_lead85
transistor_cut_lead86
transistor_damaged_case50
transistor_misplaced50
metal_nut_bent50
metal_nut_color50
metal_nut_flip50
metal_nut_scratch61
screw_manipulated_front50
screw_scratch_head50
screw_scratch_neck50
screw_thread_side50
screw_thread_top50
lowhigh density

Model Performance

Validation metrics from a YOLOv8 detector trained on this dataset. Reference checkpoint: yolov8m.pt.

Validation set
These results are based on training on 100% synthetic images from this dataset, validated on 100% real-world held-out images. For production deployments, AnywayLabs.ai recommends mixing 10–25% real images into your training set.
0.8996
mAP@0.5
0.6852
mAP@0.5:0.95
0.8771
Precision
0.8490
Recall

Validation curves over training

mAP@0.5
0.000.250.500.7511316191120
mAP@0.5:0.95
0.000.250.500.7511316191120
Precision
0.000.250.500.7511316191120
Recall
0.000.250.500.7511316191120

Dataset Metadata

Annotation formatYOLO
Total images1,000
Classes20
ResolutionMixed
Last updated2026-05

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