| 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647 |
- Collections:
- - Name: SEResNet
- Metadata:
- Training Data: ImageNet-1k
- Training Techniques:
- - SGD with Momentum
- - Weight Decay
- Training Resources: 8x V100 GPUs
- Epochs: 140
- Batch Size: 256
- Architecture:
- - ResNet
- Paper:
- URL: https://openaccess.thecvf.com/content_cvpr_2018/html/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.html
- Title: "Squeeze-and-Excitation Networks"
- README: configs/seresnet/README.md
- Code:
- URL: https://github.com/open-mmlab/mmpretrain/blob/v0.15.0/mmcls/models/backbones/seresnet.py#L58
- Version: v0.15.0
- Models:
- - Name: seresnet50_8xb32_in1k
- Metadata:
- FLOPs: 4130000000
- Parameters: 28090000
- In Collection: SEResNet
- Results:
- - Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 77.74
- Top 5 Accuracy: 93.84
- Task: Image Classification
- Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth
- Config: configs/seresnet/seresnet50_8xb32_in1k.py
- - Name: seresnet101_8xb32_in1k
- Metadata:
- FLOPs: 7860000000
- Parameters: 49330000
- In Collection: SEResNet
- Results:
- - Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 78.26
- Top 5 Accuracy: 94.07
- Task: Image Classification
- Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth
- Config: configs/seresnet/seresnet101_8xb32_in1k.py
|
