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The ResNeXt [7] architecture is built based on the advantages of ResNet (residual networks) and GoogleNet (multi-branch architecture) and requires less number of hyperparameters compared to the traditional ResNet. The next defines the next dimension (“cardinality”), an additional dimension on top of the depth and width of ResNet. The input is split channelwise into groups. The standard residual block is replaced with a “split-transform-merge” procedure. This architecture uses a series of residual blocks and uses the following rules. (1) If the spatial maps are of same size, the blocks will split the hyperparameters; (2) The spatial map is pooled by two factors; block width is doubled by two factors. ResNeXt becomes the 1^st runner up of ILSVRC classification task and produces better results than ResNet. Figure 1.8 shows the architecture of ResNeXt, and the comparison with REsNet is shown in Table 1.8.

Figure 1.7 (a) A residual block.

Table 1.7 Various parameters of ResNet.

Figure 1.8 Architecture of ResNeXt.