3-2-1. ResNet-v2-152:
ResNet-v2-152 [16] is a deep neural network architecture with 152
layers, based on the ResNet (Residual Network) design. It is an updated
version of the original ResNet, which aims to overcome some of the
challenges faced during deep network training. With a large number of
parameters, ResNet-v2-152 is considered a very deep network, making it
suitable for computer vision tasks such as image classification, object
detection, and segmentation. Its depth and the residual connections in
its design allow the network to effectively learn hierarchical features
and handle high-level abstractions.
3-2-2. Inception-v3 :
Inception-v3 [17] is a deep neural network architecture for image
classification. It is a variation of the Inception architecture, which
was created to tackle challenges associated with training deep networks,
such as computational and memory demands. Inception-v3 has been
optimized to be computationally efficient while still delivering high
accuracy in image classification tasks. The architecture features a
modular design, utilizing multiple Inception modules that perform
convolution and pooling with various kernel sizes, allowing the network
to learn both detailed and broad features. This architecture has been
trained on the massive ImageNet dataset and has been shown to achieve
top results in different computer vision tasks.