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123 lines
3.3 KiB
123 lines
3.3 KiB
# SE-ResNet
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**SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) 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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{% 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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Type: model-index
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Collections:
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- Name: SE ResNet
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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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Models:
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- Name: seresnet152d
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In Collection: SE ResNet
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Metadata:
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FLOPs: 20161904304
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Parameters: 66840000
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File Size: 268144497
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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- Squeeze-and-Excitation Block
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Tasks:
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- Image Classification
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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 Data:
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- ImageNet
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Training Resources: 8x NVIDIA Titan X GPUs
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ID: seresnet152d
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LR: 0.6
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Epochs: 100
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Layers: 152
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Dropout: 0.2
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Crop Pct: '0.94'
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Momentum: 0.9
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Batch Size: 1024
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Image Size: '256'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1206
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.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: 83.74%
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Top 5 Accuracy: 96.77%
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- Name: seresnet50
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In Collection: SE ResNet
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Metadata:
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FLOPs: 5285062320
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Parameters: 28090000
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File Size: 112621903
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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- Squeeze-and-Excitation Block
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Tasks:
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- Image Classification
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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 Data:
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- ImageNet
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Training Resources: 8x NVIDIA Titan X GPUs
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ID: seresnet50
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LR: 0.6
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Epochs: 100
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Layers: 50
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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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Batch Size: 1024
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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/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1180
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet50_ra_224-8efdb4bb.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: 80.26%
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Top 5 Accuracy: 95.07%
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-->
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