48 lines
1.5 KiB
YAML
48 lines
1.5 KiB
YAML
Collections:
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- Name: SEResNet
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Metadata:
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Training Data: ImageNet-1k
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x V100 GPUs
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Epochs: 140
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Batch Size: 256
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Architecture:
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- ResNet
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Paper:
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URL: https://openaccess.thecvf.com/content_cvpr_2018/html/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.html
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Title: "Squeeze-and-Excitation Networks"
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README: configs/seresnet/README.md
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Code:
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URL: https://github.com/open-mmlab/mmpretrain/blob/v0.15.0/mmcls/models/backbones/seresnet.py#L58
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Version: v0.15.0
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Models:
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- Name: seresnet50_8xb32_in1k
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Metadata:
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FLOPs: 4130000000
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Parameters: 28090000
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In Collection: SEResNet
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Results:
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- Dataset: ImageNet-1k
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Metrics:
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Top 1 Accuracy: 77.74
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Top 5 Accuracy: 93.84
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth
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Config: configs/seresnet/seresnet50_8xb32_in1k.py
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- Name: seresnet101_8xb32_in1k
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Metadata:
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FLOPs: 7860000000
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Parameters: 49330000
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In Collection: SEResNet
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Results:
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- Dataset: ImageNet-1k
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Metrics:
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Top 1 Accuracy: 78.26
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Top 5 Accuracy: 94.07
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth
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Config: configs/seresnet/seresnet101_8xb32_in1k.py
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