| tables/table1_imagenet_architectures.md |
Table 1, §3.3 |
C03, C05 |
Per-depth ImageNet ResNet architectures — block layouts and FLOPs for ResNet-{18, 34, 50, 101, 152}. |
| tables/table2_imagenet_plain_vs_residual.md |
Table 2, §4.1 |
C01, C02 |
Top-1 ImageNet validation error — plain-{18, 34} vs. ResNet-{18, 34} with 10-crop testing. |
| tables/table3_imagenet_validation_full.md |
Table 3, §4.1 |
C01, C02, C03, C04, C05 |
Full ImageNet validation error table (10-crop): VGG-16, GoogLeNet, PReLU-net, plain-34, ResNet-{34A, 34B, 34C, 50, 101, 152}. |
| tables/derived_from_table3_shortcut_options.md |
Derived from Table 3 |
C04 |
Subset of Table 3 isolating ResNet-34 shortcut options A / B / C plus plain-34 baseline. |
| tables/table4_imagenet_singlemodel.md |
Table 4, §4.1 |
C03 |
ImageNet single-model validation error (multi-scale fully-convolutional testing). |
| tables/table5_imagenet_ensembles.md |
Table 5, §4.1 |
C03 |
ImageNet ensemble top-5 error on the test set — ResNet 6-model ensemble achieves 3.57%. |
| tables/table6_cifar10.md |
Table 6, §4.2 |
C06, C07 |
CIFAR-10 test error vs. depth for ResNet-{20, 32, 44, 56, 110, 1202} with baselines. |
| tables/table7_pascal_voc_detection.md |
Table 7, §4.3 |
C08 |
PASCAL VOC 07/12 detection mAP with baseline Faster R-CNN — VGG-16 vs. ResNet-101. |
| tables/table8_coco_detection.md |
Table 8, §4.3 |
C08 |
COCO val detection mAP with baseline Faster R-CNN — VGG-16 vs. ResNet-101. |
