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71 lines
2.1 KiB
71 lines
2.1 KiB
# SK-ResNeXt
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**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.
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{% include 'code_snippets.md' %}
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## How do I train this model?
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You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
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## Citation
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```BibTeX
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@misc{li2019selective,
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title={Selective Kernel Networks},
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author={Xiang Li and Wenhai Wang and Xiaolin Hu and Jian Yang},
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year={2019},
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eprint={1903.06586},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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<!--
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Type: model-index
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Collections:
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- Name: SKResNeXt
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Paper:
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Title: Selective Kernel Networks
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URL: https://paperswithcode.com/paper/selective-kernel-networks
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Models:
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- Name: skresnext50_32x4d
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In Collection: SKResNeXt
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Metadata:
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FLOPs: 5739845824
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Parameters: 27480000
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File Size: 110340975
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Architecture:
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- Convolution
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- Dense Connections
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- Global Average Pooling
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- Grouped Convolution
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- Max Pooling
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- Residual Connection
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- Selective Kernel
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- Softmax
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Tasks:
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- Image Classification
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Training Data:
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- ImageNet
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Training Resources: 8x GPUs
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ID: skresnext50_32x4d
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LR: 0.1
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Epochs: 100
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Layers: 50
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Crop Pct: '0.875'
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Momentum: 0.9
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Batch Size: 256
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/sknet.py#L210
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnext50_ra-f40e40bf.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 80.15%
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Top 5 Accuracy: 94.64%
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-->
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