UNet3D

Reported on 4 benchmarks across 2 tasks

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Medical2 results

Semantic SegmentationonLombardia Sentinel-2 Image Time Series for Crop Mapping
Overall Accuracy
80.77
Semantic SegmentationonMunich Sentinel2 Crop Segmentation
Overall Accuracy
94.73
best: 95.26 (Swin UNETR)

Audio2 results

10-shot image generationonLombardia Sentinel-2 Image Time Series for Crop Mapping
Overall Accuracy
80.77
10-shot image generationonMunich Sentinel2 Crop Segmentation
Overall Accuracy
94.73
best: 95.26 (Swin UNETR)