Binary Surgical Instrument Segmentation on EndoVis 2018 (test)
83.15DSCEndoVis-2018 Baseline
Evaluation Results
| Method | Links | |
|---|---|---|
| EndoVis-2018 BaselineTrain Dataset=EndoVis-2018, Augmentation Mixing=Soft2022.06 | 83.15 | |
| EndoVis-2018 BaselineTrain Dataset=EndoVis-2018, Augmentation Mixing=Hard2022.06 | 82.91 | |
| EndoVis-2018 BaselineTrain Dataset=EndoVis-2018, Augmentation Mixing=None2022.06 | 81.58 | |
| Synthetic-CTrain Dataset=Synthetic-C, Augmentation Mixing=Hard2022.06 | 73.51 | |
| Synthetic-BTrain Dataset=Synthetic-B, Augmentation Mixing=Hard2022.06 | 72.53 | |
| Synthetic-CTrain Dataset=Synthetic-C, Augmentation Mixing=Soft2022.06 | 71.48 | |
| Synthetic-ATrain Dataset=Synthetic-A, Augmentation Mixing=Hard2022.06 | 71.03 | |
| Synthetic-BTrain Dataset=Synthetic-B, Augmentation Mixing=Soft2022.06 | 69.42 | |
| Synthetic-ATrain Dataset=Synthetic-A, Augmentation Mixing=Soft2022.06 | 66.74 | |
| Synthetic-CTrain Dataset=Synthetic-C, Augmentation Mixing=None2022.06 | 59.28 | |
| Synthetic-BTrain Dataset=Synthetic-B, Augmentation Mixing=None2022.06 | 57.37 | |
| Synthetic-ATrain Dataset=Synthetic-A, Augmentation Mixing=None2022.06 | 56.82 |