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Defect localisation

Pixel-level localisation of manufacturing defects.

Result

splitpixel AP
train0.9839
val0.9565
test0.9583

The finding

Notes

- Trained from scratch for 120 epochs across 12 MVTec AD object categories. - Test pixel AP 0.9583, pooled IoU 0.8033, per-image IoU 0.6232. The decision threshold was tuned on the validation split.

Figures

metal_nut_bent_ground_truth.png
metal_nut_bent_ground_truth
metal_nut_bent_input.png
metal_nut_bent_input
metal_nut_bent_overlay.png
metal_nut_bent_overlay
metal_nut_bent_pred_heatmap.png
metal_nut_bent_pred_heatmap
metal_nut_bent_pred_mask.png
metal_nut_bent_pred_mask
pill_color_ground_truth.png
pill_color_ground_truth
pill_color_input.png
pill_color_input
pill_color_overlay.png
pill_color_overlay
pill_color_pred_heatmap.png
pill_color_pred_heatmap
pill_color_pred_mask.png
pill_color_pred_mask
screw_manipulated-front_ground_truth.png
screw_manipulated-front_ground_truth
screw_manipulated-front_input.png
screw_manipulated-front_input

Elsewhere

Code and full results on GitHub

Every number is read from the file the run produced. github.com/hammasbuilds · kaggle.com/muhammadhammas13