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143 lines
4.4 KiB
143 lines
4.4 KiB
# (Gluon) ResNeXt
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A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) $C$, as an essential factor in addition to the dimensions of depth and width.
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The weights from this model were ported from [Gluon](https://cv.gluon.ai/model_zoo/classification.html).
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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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@article{DBLP:journals/corr/XieGDTH16,
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author = {Saining Xie and
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Ross B. Girshick and
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Piotr Doll{\'{a}}r and
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Zhuowen Tu and
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Kaiming He},
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title = {Aggregated Residual Transformations for Deep Neural Networks},
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journal = {CoRR},
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volume = {abs/1611.05431},
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year = {2016},
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url = {http://arxiv.org/abs/1611.05431},
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archivePrefix = {arXiv},
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eprint = {1611.05431},
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timestamp = {Mon, 13 Aug 2018 16:45:58 +0200},
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biburl = {https://dblp.org/rec/journals/corr/XieGDTH16.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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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: Gloun ResNeXt
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Paper:
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Title: Aggregated Residual Transformations for Deep Neural Networks
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URL: https://paperswithcode.com/paper/aggregated-residual-transformations-for-deep
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Models:
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- Name: gluon_resnext101_32x4d
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In Collection: Gloun ResNeXt
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Metadata:
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FLOPs: 10298145792
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Parameters: 44180000
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File Size: 177367414
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Global Average Pooling
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- Grouped Convolution
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- Max Pooling
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- ReLU
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- ResNeXt Block
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- Residual Connection
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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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ID: gluon_resnext101_32x4d
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L193
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Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_32x4d-b253c8c4.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: 80.33%
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Top 5 Accuracy: 94.91%
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- Name: gluon_resnext101_64x4d
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In Collection: Gloun ResNeXt
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Metadata:
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FLOPs: 19954172928
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Parameters: 83460000
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File Size: 334737852
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Global Average Pooling
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- Grouped Convolution
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- Max Pooling
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- ReLU
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- ResNeXt Block
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- Residual Connection
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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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ID: gluon_resnext101_64x4d
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L201
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Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_64x4d-f9a8e184.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: 80.63%
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Top 5 Accuracy: 95.0%
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- Name: gluon_resnext50_32x4d
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In Collection: Gloun ResNeXt
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Metadata:
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FLOPs: 5472648192
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Parameters: 25030000
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File Size: 100441719
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Global Average Pooling
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- Grouped Convolution
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- Max Pooling
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- ReLU
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- ResNeXt Block
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- Residual Connection
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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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ID: gluon_resnext50_32x4d
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L185
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Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext50_32x4d-e6a097c1.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: 79.35%
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Top 5 Accuracy: 94.42%
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-->
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