Image Classification on ImageNet 1k (test) (Sensitivity Analysis)
15.9Top-1 Error RateSwinTransformer B V2
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| SwinTransformer B V2Pre-training=ImageNet2026.06 | 15.9 | 1.064 | 0.27 | 0.186 | 0.18 | |
| SwinTransformer BPre-training=ImageNet2026.06 | 16.4 | 1.065 | 0.28 | 0.186 | 0.183 | |
| ResNet152 V2Pre-training=ImageNet2026.06 | 17.7 | 1.052 | 0.267 | 0.2 | 0.2 | |
| SwinTransformer T V2Pre-training=ImageNet2026.06 | 17.9 | 1.087 | 0.327 | 0.25 | 0.248 | |
| ResNet101 V2Pre-training=ImageNet2026.06 | 18.1 | 1.06 | 0.285 | 0.216 | 0.216 | |
| SwinTransformer TPre-training=ImageNet2026.06 | 18.5 | 1.1 | 0.358 | 0.298 | 0.247 | |
| ResNet50 V2Pre-training=ImageNet2026.06 | 19.1 | 1.089 | 0.344 | 0.258 | 0.255 | |
| ResNet152 V1Pre-training=ImageNet2026.06 | 21.7 | 1.101 | 0.379 | 0.314 | 0.31 | |
| ResNet101 V1Pre-training=ImageNet2026.06 | 22.6 | 1.105 | 0.392 | 0.334 | 0.332 | |
| DenseNet161Pre-training=ImageNet2026.06 | 22.9 | 1.105 | 0.386 | 0.318 | 0.317 | |
| DenseNet201Pre-training=ImageNet2026.06 | 23.1 | 1.098 | 0.378 | 0.316 | 0.312 | |
| ResNet50 V1Pre-training=ImageNet2026.06 | 23.9 | 1.131 | 0.443 | 0.413 | 0.412 | |
| DenseNet169Pre-training=ImageNet2026.06 | 24.4 | 1.124 | 0.427 | 0.368 | 0.367 | |
| DenseNet121Pre-training=ImageNet2026.06 | 25.6 | 1.156 | 0.484 | 0.44 | 0.438 | |
| VGG19 BNPre-training=ImageNet2026.06 | 25.8 | 1.159 | 0.499 | 0.488 | 0.485 | |
| ResNet34 V1Pre-training=ImageNet2026.06 | 26.7 | 1.157 | 0.499 | 0.455 | 0.454 | |
| VGG19Pre-training=ImageNet2026.06 | 27.6 | 1.161 | 0.512 | 0.52 | 0.516 | |
| VGG13 BNPre-training=ImageNet2026.06 | 28.4 | 1.192 | 0.564 | 0.605 | 0.602 | |
| VGG13Pre-training=ImageNet2026.06 | 30.1 | 1.184 | 0.563 | 0.601 | 0.599 | |
| ResNet18 V1Pre-training=ImageNet2026.06 | 30.2 | 1.212 | 0.602 | 0.593 | 0.591 |