ResNeXt

ResNeXt is a CNN architecture designed to improve accuracy in deep convolutional neural networks while maintaining parameter efficiency. Proposed in 2017 by Facebook AI Research (FAIR), this design enhances the residual connection principle of ResNet by integrating the group-based discrete transformation concept from the Inception architecture. The key innovation of ResNeXt lies in aggregating multiple transformations within the same structure across parallel branches (cardinality).

ResNeXt builds upon the residual learning blocks of ResNet but incorporates multiple parallel transformation paths within each block. These parallel paths are then combined to produce the final output, enabling the model to achieve higher capacity without increasing the number of parameters.

One of the most important concepts in the ResNeXt architecture is cardinality, which refers to the number of parallel transformation paths. This determines how many distinct paths each block contains and is one of the most effective parameters for increasing model capacity.

$y=x+{\sum }_{i=1}^C{\mathcal{T}}_i(x)$

ResNeXt blocks process multiple transformation paths within the same structure in parallel, and their outputs are aggregated to produce the final result.

In the ResNeXt architecture, each transformation path is implemented using group convolutions. This approach increases parameter efficiency and allows each group to learn distinct features, thereby enhancing the model’s overall efficiency.

ResNeXt blocks adhere to a standardized structure, which provides advantages in terms of scalability and transfer learning across different datasets.

It is possible to achieve higher accuracy with the same number of FLOPs (Floating Point Operations) and parameters. In this regard, ResNeXt delivers more effective results compared to models such as VGG and ResNet.

• Image classification
• Object detection
• Video classification (ResNeXt-3D variants)
• Medical image processing
• Large-scale visual search systems