Panoptic Segmentation On Lars
Metrics
PQ
Results
Performance results of various models on this benchmark
Model Name | PQ | Paper Title | Repository |
|---|---|---|---|
| Panoptic FPN (ResNet-101) | 38.7 | LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark | |
| Mask2Former (Swin-T) | 39.2 | LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark | |
| Panoptic Deeplab (ResNet-50) | 34.7 | LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark | |
| Mask2Former (ResNet-50) | 37.6 | LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark | |
| Mask2Former (ResNet-101) | 37.2 | LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark | |
| Mask2Former (Swin-B) | 41.7 | LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark | |
| Panoptic FPN (ResNet-50) | 40.1 | LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark | |
| MaX-DeepLab | 31.9 | LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark |
0 of 8 row(s) selected.