GPU Memory Usage on Hecktor 2022 Medical Image Segmentation
562Peak GPU Memory Usage (Training)Slim UNETR
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| Slim UNETRType=CNN-Transformer, Batch size=22025.09 | 562 | 124 | |
| VeloxSegType=CNN-Transformer, Batch size=22025.09 | 1,006 | 178 | |
| U-KANType=CNN-KAN, Batch size=22025.09 | 2,360 | 640 | |
| SegFormer-3DType=CNN-Transformer, Batch size=22025.09 | 2,702 | 436 | |
| H-DenseFormerType=CNN-Transformer, Batch size=22025.09 | 3,778 | 1,474 | |
| UNETR++Type=CNN-Transformer, Batch size=22025.09 | 3,906 | 1,088 | |
| VNetType=CNN, Batch size=22025.09 | 4,460 | 2,072 | |
| UNETRType=CNN-Transformer, Batch size=22025.09 | 4,626 | 1,914 | |
| NestedFormerType=CNN-Transformer, Batch size=22025.09 | 5,182 | 1,966 | |
| U-RWKVType=CNN-RWKV, Batch size=22025.09 | 5,388 | 1,044 | |
| UNetType=CNN, Batch size=22025.09 | 5,942 | 2,698 | |
| HCMA-UNetType=CNN-Mamba, Batch size=22025.09 | 9,098 | 1,892 | |
| A2FSegType=CNN, Batch size=22025.09 | 9,102 | 2,352 | |
| SuperLightNetType=CNN-Transformer, Batch size=22025.09 | 9,192 | 2,730 | |
| Swin UNETRType=CNN-Transformer, Batch size=22025.09 | 13,258 | 3,704 | |
| VSmTransType=CNN-Transformer, Batch size=22025.09 | 13,318 | 2,532 | |
| MedNeXtType=CNN, Batch size=22025.09 | 20,372 | 4,376 |