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68 lines
1.8 KiB
68 lines
1.8 KiB
# (Legacy) SENet
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A **SENet** is a convolutional neural network architecture that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
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The weights from this model were ported from Gluon.
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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{hu2019squeezeandexcitation,
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title={Squeeze-and-Excitation Networks},
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author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu},
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year={2019},
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eprint={1709.01507},
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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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Models:
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- Name: legacy_senet154
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Metadata:
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FLOPs: 26659556016
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Epochs: 100
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Batch Size: 1024
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Training Data:
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- ImageNet
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Training Techniques:
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- Label Smoothing
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA Titan X GPUs
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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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- Max Pooling
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- Softmax
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- Squeeze-and-Excitation Block
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File Size: 461488402
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Tasks:
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- Image Classification
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ID: legacy_senet154
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LR: 0.6
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Layers: 154
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Dropout: 0.2
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L440
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In Collection: Legacy SENet
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Collections:
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- Name: Legacy SENet
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Paper:
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title: Squeeze-and-Excitation Networks
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url: https://paperswithcode.com//paper/squeeze-and-excitation-networks
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type: model-index
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Type: model-index
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-->
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