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The results of the same experiments with LOM images are provided in this table.
| Method | Input Data | Data | Training Strategy | Test Accuracy |
|---|---|---|---|---|
| CIFARNet | cropped object | LOM | from scratch | 51,27% |
| VGG-19- DeCAF-RBF-kernel SVM | cropped object | LOM | — | 56.56% |
| VGG-16 | cropped object | LOM | fine tuning | 60,02% |
| MVFCNN | cropped patch | LOM | fine tuning | 70.14% |
| MVFCNN | cropped patch | SEM | fine tuning | 93.94% |
he results show inferior performance using LOM instead of SEM images.
