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. 2020 Sep 28;10(10):1762. doi: 10.3390/ani10101762

Table 2.

Detailed architectures for residual network (ResNet).

Stage of Convolution ResNet18 ResNet34 ResNet50 ResNet101 ResNet152 ResNet1000
C1 7 × 7, 64, stride 2
3 × 3 max pooling, stride 2
C2 [3×3, 643×3, 64]×2 [3×3, 643×3, 64]×3 [1×1, 643×3, 641×1, 256]×3 [1×1, 643×3, 641×1, 256]×3 [1×1, 643×3, 641×1, 256]×3 [1×1, 643×3, 641×1, 256]×248
C3 [3×3, 1283×3, 128]×2 [3×3, 1283×3, 128]×4 [1×1, 1283×3, 1281×1, 512]×4 [1×1, 1283×3, 1281×1, 512]×4 [1×1, 1283×3, 1281×1, 512]×22 [1×1, 1283×3, 1281×1, 512]×248
C4 [3×3, 2563×3, 256]×2 [3×3, 2563×3, 256]×6 [1×1, 2563×3, 2561×1, 1024]×6 [1×1, 2563×3, 2561×1, 1024]×23 [1×1, 2563×3, 2561×1, 1024]×22 [1×1, 2563×3, 2561×1, 1024]×247
C5 [3×3, 5123×3, 512]×2 [3×3, 5123×3, 512]×3 [1×1, 5123×3, 5121×1, 2048]×3 [1×1, 5123×3, 5121×1, 2048]×247
Average pooling, 1000-d FC, softmax

Note: ResNet18-ResNet1000 are residual network with 18–1000 layers of convolution; C1–C5 are convolutional stages 1 to 5 in the ResNet; and FC is fully-connected.