Cell Classification on Cell classification dataset (test)
89.1F1 ScoreSwin Transformer Base
Evaluation Results
| Method | Links | |
|---|---|---|
| Swin Transformer BasePre-training=ImageNet, Patch Size=4, Window Size=122022.07 | 89.1 | |
| nfnet-f3Pre-training=ImageNet2022.07 | 88.98 | |
| nfnet-f4Pre-training=ImageNet2022.07 | 88.48 | |
| Swin Transformer largePre-training=ImageNet, Patch Size=4, Window Size=122022.07 | 88.39 | |
| Resnet-101Pre-training=ImageNet2022.07 | 87.1 | |
| nfnet-f2Pre-training=ImageNet2022.07 | 86.52 | |
| Resnet-18Pre-training=ImageNet2022.07 | 86.35 | |
| Vit-Base/32Pre-training=ImageNet, Patch Size=322022.07 | 85.07 | |
| Resnet-50Pre-training=ImageNet2022.07 | 84.99 | |
| ConvNext-smallPre-training=ImageNet2022.07 | 84.795 | |
| Efficient-net B5Pre-training=ImageNet2022.07 | 84.63 | |
| ConvNext-largePre-training=ImageNet2022.07 | 84.52 | |
| Vit-large/32Pre-training=ImageNet, Patch Size=322022.07 | 84.5 | |
| Vit-Base/16Pre-training=ImageNet, Patch Size=162022.07 | 84.26 | |
| Efficient-net B4Pre-training=ImageNet2022.07 | 84.18 | |
| nfnet-f6Pre-training=ImageNet2022.07 | 83.78 | |
| Efficient-net B0Pre-training=ImageNet2022.07 | 83.72 | |
| Efficient-net B3Pre-training=ImageNet2022.07 | 83.69 | |
| Efficient-net B1Pre-training=ImageNet2022.07 | 83.46 | |
| nfnet-f1Pre-training=ImageNet2022.07 | 83.4 | |
| Efficient-net B2Pre-training=ImageNet2022.07 | 82.8 | |
| Resnet-34Pre-training=ImageNet2022.07 | 82.765 | |
| Efficient-net B6Pre-training=ImageNet2022.07 | 82.63 | |
| nfnet-f0Pre-training=ImageNet2022.07 | 82.035 | |
| nfnet-f5Pre-training=ImageNet2022.07 | 81.61 | |
| ConvNext-tinyPre-training=ImageNet2022.07 | 81.355 | |
| Efficient-net B7Pre-training=ImageNet2022.07 | 81.29 | |
| ConvNext-basePre-training=ImageNet2022.07 | 80.675 | |
| Vit-large/16Pre-training=ImageNet, Patch Size=162022.07 | 80.495 |