This is unofficial implementation of "Asymmetric student-teacher networks for industrial anomaly detection"
- Write your data directory in config.py
- set config.py
- train_teacher.py
- train_student.py
- eval.py
mean | max | Paper | |
---|---|---|---|
leather | 1 | 1 | 1 |
zipper | 0.991 | 0.977 | 0.991 |
metal_nut | 0.989 | 0.996 | 0.985 |
wood | 0.988 | 0.992 | 1 |
pill | 0.992 | 0.964 | 0.991 |
transistor | 0.990 | 0.987 | 0.993 |
grid | 0.990 | 0.999 | 0.991 |
tile | 0.999 | 0.996 | 1 |
capsule | 0.992 | 0.971 | 0.997 |
hazelnut | 0.998 | 0.997 | 1 |
toothbrush | 0.961 | 0.864 | 0.966 |
screw | 0.993 | 0.944 | 0.997 |
carpet | 0.972 | 0.972 | 0.975 |
bottle | 0.998 | 0.994 | 1 |
cable | 0.992 | 0.939 | 0.985 |
mean | max | |
---|---|---|
foam | 0.864375 | 0.89875 |
tire | 0.68092 | 0.665287 |
peach | 0.821843 | 0.993832 |
cable_gland | 0.910783 | 0.958402 |
carrot | 0.8656 | 0.992985 |
rope | 0.927989 | 0.880435 |
potato | 0.666502 | 0.935277 |
cookie | 0.93516 | 0.992718 |
bagel | 0.855888 | 0.910124 |
dowel | 0.984837 | 0.95858 |
AVerage | 0.85139 | 0.918639 |