| Benchmark | Model | Rank | Results |
|---|---|---|---|
| monocular-depth-estimation-on-cityscapes | SwinMTL | #1 | RMSE: 5.481RMSE log: 0.139Absolute relative error (AbsRel): 0.089… |
| multi-task-learning-on-cityscapes-test | SwinMTL | #1 | mIoU: 76.41RMSE: 0.51 |
| semantic-segmentation-on-nyu-depth-v2 | SwinMTL | #8 | Mean IoU: 58.14% |
| real-time-semantic-segmentation-on-cityscapes-test | SwinMTL | #9 | mIoU: 76.41% |
| semantic-segmentation-on-cityscapes-test | SwinMTL | #60 | Mean IoU (class): 76.41% |
| semantic-segmentation-on-cityscapes-val | SwinMTL | #64 | mIoU: 76.41 |