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237 lines
6.7 KiB
237 lines
6.7 KiB
# ECA-ResNet
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An **ECA ResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that utilises an [Efficient Channel Attention module](https://paperswithcode.com/method/efficient-channel-attention). Efficient Channel Attention is an architectural unit based on [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) that reduces model complexity without dimensionality reduction.
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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{wang2020ecanet,
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title={ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks},
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author={Qilong Wang and Banggu Wu and Pengfei Zhu and Peihua Li and Wangmeng Zuo and Qinghua Hu},
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year={2020},
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eprint={1910.03151},
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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: ECAResNet
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Paper:
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Title: 'ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks'
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URL: https://paperswithcode.com/paper/eca-net-efficient-channel-attention-for-deep
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Models:
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- Name: ecaresnet101d
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In Collection: ECAResNet
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Metadata:
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FLOPs: 10377193728
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Parameters: 44570000
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File Size: 178815067
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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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- Efficient Channel Attention
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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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- 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: 4x RTX 2080Ti GPUs
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ID: ecaresnet101d
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LR: 0.1
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Epochs: 100
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Layers: 101
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Crop Pct: '0.875'
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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/resnet.py#L1087
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Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet101D_281c5844.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: 82.18%
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Top 5 Accuracy: 96.06%
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- Name: ecaresnet101d_pruned
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In Collection: ECAResNet
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Metadata:
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FLOPs: 4463972081
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Parameters: 24880000
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File Size: 99852736
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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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- Efficient Channel Attention
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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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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: ecaresnet101d_pruned
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Layers: 101
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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/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1097
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Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45610/outputs/ECAResNet101D_P_75a3370e.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: 80.82%
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Top 5 Accuracy: 95.64%
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- Name: ecaresnet50d
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In Collection: ECAResNet
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Metadata:
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FLOPs: 5591090432
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Parameters: 25580000
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File Size: 102579290
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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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- Efficient Channel Attention
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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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- 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: 4x RTX 2080Ti GPUs
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ID: ecaresnet50d
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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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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/resnet.py#L1045
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Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet50D_833caf58.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: 80.61%
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Top 5 Accuracy: 95.31%
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- Name: ecaresnet50d_pruned
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In Collection: ECAResNet
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Metadata:
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FLOPs: 3250730657
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Parameters: 19940000
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File Size: 79990436
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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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- Efficient Channel Attention
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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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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: ecaresnet50d_pruned
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Layers: 50
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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/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1055
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Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45899/outputs/ECAResNet50D_P_9c67f710.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: 79.71%
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Top 5 Accuracy: 94.88%
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- Name: ecaresnetlight
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In Collection: ECAResNet
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Metadata:
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FLOPs: 5276118784
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Parameters: 30160000
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File Size: 120956612
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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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- Efficient Channel Attention
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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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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: ecaresnetlight
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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/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1077
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Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNetLight_4f34b35b.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: 80.46%
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Top 5 Accuracy: 95.25%
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-->
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