Binary Surgical Instrument Segmentation on EndoVis 2017 1 (test)
84.06DSCEndoVis-2018 Baseline
Evaluation Results
| Method | Links | |
|---|---|---|
| EndoVis-2018 BaselineTrain Dataset=EndoVis-2018, Augmentation Mixing=Soft2022.06 | 84.06 | |
| EndoVis-2018 BaselineTrain Dataset=EndoVis-2018, Augmentation Mixing=Hard2022.06 | 83.43 | |
| EndoVis-2018 BaselineTrain Dataset=EndoVis-2018, Augmentation Mixing=None2022.06 | 83.21 | |
| Synthetic-CTrain Dataset=Synthetic-C, Augmentation Mixing=Soft2022.06 | 75.69 | |
| Synthetic-CTrain Dataset=Synthetic-C, Augmentation Mixing=Hard2022.06 | 75.16 | |
| Synthetic-CTrain Dataset=Synthetic-C, Augmentation Mixing=None2022.06 | 74.37 | |
| Synthetic-BTrain Dataset=Synthetic-B, Augmentation Mixing=Soft2022.06 | 73.21 | |
| Synthetic-ATrain Dataset=Synthetic-A, Augmentation Mixing=None2022.06 | 72.74 | |
| Synthetic-BTrain Dataset=Synthetic-B, Augmentation Mixing=None2022.06 | 72.65 | |
| Synthetic-BTrain Dataset=Synthetic-B, Augmentation Mixing=Hard2022.06 | 72.41 | |
| Synthetic-ATrain Dataset=Synthetic-A, Augmentation Mixing=Soft2022.06 | 72.23 | |
| Synthetic-ATrain Dataset=Synthetic-A, Augmentation Mixing=Hard2022.06 | 65.13 |