resnet101原理ignore subsequent bad blocks
ResNet-101 is a convolutional neural network architecture that was introduced by Microsoft Research in 2015. It is a deep neural network that combines residual learning principles with a 101-layer structure.
The main idea behind the ResNet-101 architecture is the use of residual learning blocks, which allows the network to effectively train very deep models. A residual learning block consists of a bypass connection, known as a "skip connection," which directly passes the input of a layer to the output of a subsequent layer. This skip connection helps the network to learn the residual error between the desired output and the current prediction. By propagating this error through skip connections, the network can quickly learn and adjust the weights to improve the accuracy of its predictions.
In ResNet-101, the residual learning blocks are arranged in a hierarchical manner, with four "layer stages." Each stage contains a different number of residual learning blocks with increasing depth. The first stage has a single block, while the subsequent stages have 3, 4,
and 23 blocks respectively. This progressive increase in depth allows the network to learn increasingly complex and abstract features as it goes deeper into the network.
Additionally, ResNet-101 utilizes several techniques to improve training and inference efficiency, such as using bottleneck layers in the residual blocks to reduce the number of parameters and operations. It also uses 1x1 convolutional layers to adjust the dimensions of the feature maps before and after the main convolutional layers, allowing for better flow of information within the network and reducing the computational cost.
Overall, ResNet-101 has achieved state-of-the-art performance on various image classification tasks and has become a popular choice for deep learning models in computer vision applications.

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