# Summary **SK ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNext are replaced by the proposed [SK convolutions](https://paperswithcode.com/method/selective-kernel-convolution), enabling the network to choose appropriate receptive field sizes in an adaptive manner. {% include 'code_snippets.md' %} ## How do I train this model? You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh. ## Citation ```BibTeX @misc{li2019selective, title={Selective Kernel Networks}, author={Xiang Li and Wenhai Wang and Xiaolin Hu and Jian Yang}, year={2019}, eprint={1903.06586}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```