Defect detection datasets
Pixel-accurate labeled defect data at scale, including the rare cases that real data misses.
Synthetic datasets for defect detection.
Image datasets of parts and products with realistic defects. Each sample comes with pixel masks, bounding boxes, and class labels. The dataset is built around the defects you actually need to catch.
Why defect data is the bottleneck.
Real defects are rare
A working line produces few failures. Capturing enough samples to train a model takes months.
Pixel labels are slow
Segmentation masks for cracks, scratches, and contamination take time and trained annotators.
Edge cases dominate
The defects that matter most for safety and yield are the ones the dataset rarely contains.
How we build defect datasets.
Render the defect, not just the part
Cracks, dents, scratches, contamination, and assembly errors. Each generated with exact pixel masks.
Mix simulation and generative
Simulation controls geometry and lighting. Generative models add texture variation from a few real samples.
Balance the dataset
Generate the rare classes until the model has enough signal to learn them.
Talk to us about your dataset.
Tell us the inspection task and the conditions. We will come back with what is feasible, the timeline, and the cost.