A quick overview of ResNet models

I. Introduction

Figure 1: Training error (left) and test error (right) on CIFAR-10 dataset of “plain” networks with 20 layers and 56 layers. Source [1]
Figure 2: The architecture of a residual building block. Source [1]
Table 1: Architectures of Resnet for ImageNet dataset. The residual building blocks are shown in brackets with the numbers of stacked blocks. Source [1]
Figure 3: The architecture of a residual block with two layers (Left) and three layers (Right). Source [1]
Figure 4: Left: VG-19 model, Middle: a plain network with 34 layers, Right: ResNet-34. Source [1]

II. Import some available ResNet models on Keras

from tensorflow.keras.applications import ResNet50ResNet_50 = ResNet50(weights = None)# parameter number: 
ResNet_50.count_params()
>>> 25 636 712
from tensorflow.keras.applications import ResNet101ResNet_101 = ResNet101(weights = None)# parameter number: 
ResNet_101.count_params()
>>> 44 707 176
from tensorflow.keras.applications import ResNet152ResNet_152 = ResNet152()# parameter number:
ResNet_152.count_params()
>>> 60 419 944

III. Conclusion

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