GPU Memory Usage on AutoPET-II for Medical Image Segmentation
488Peak GPU Memory Usage (Training)Slim UNETR
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| Slim UNETRType=CNN-Transformer, Batch size=22025.09 | 488 | 106 | |
| VeloxSegType=CNN-Transformer, Batch size=22025.09 | 842 | 152 | |
| U-KANType=CNN-KAN, Batch size=22025.09 | 2,138 | 558 | |
| SegFormer-3DType=CNN-Transformer, Batch size=22025.09 | 2,272 | 426 | |
| H-DenseFormerType=CNN-Transformer, Batch size=22025.09 | 3,172 | 1,256 | |
| UNETR++Type=CNN-Transformer, Batch size=22025.09 | 3,392 | 940 | |
| VNetType=CNN, Batch size=22025.09 | 3,820 | 1,762 | |
| UNETRType=CNN-Transformer, Batch size=22025.09 | 4,114 | 1,788 | |
| NestedFormerType=CNN-Transformer, Batch size=22025.09 | 4,428 | 1,658 | |
| U-RWKVType=CNN-RWKV, Batch size=22025.09 | 4,582 | 900 | |
| UNetType=CNN, Batch size=22025.09 | 5,054 | 2,268 | |
| HCMA-UNetType=CNN-Mamba, Batch size=22025.09 | 7,730 | 1,480 | |
| A2FSegType=CNN, Batch size=22025.09 | 7,748 | 2,004 | |
| SuperLightNetType=CNN-Transformer, Batch size=22025.09 | 7,812 | 2,510 | |
| Swin UNETRType=CNN-Transformer, Batch size=22025.09 | 11,164 | 2,616 | |
| VSmTransType=CNN-Transformer, Batch size=22025.09 | 13,856 | 1,956 | |
| MedNeXtType=CNN, Batch size=22025.09 | 17,216 | 3,678 |