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添加openmmlab测试用例  
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Collections:
  - Name: SEResNet
    Metadata:
      Training Data: ImageNet
      Training Techniques:
        - SGD with Momentum
        - Weight Decay
      Training Resources: 8x V100 GPUs
      Epochs: 140
      Batch Size: 256
      Architecture:
        - ResNet
    Paper: https://openaccess.thecvf.com/content_cvpr_2018/html/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.html
    README: configs/seresnet/README.md

Models:
- Config: configs/seresnet50/seresnet50_b32x8_imagenet.py
  In Collection: SEResNet
  Metadata:
    FLOPs: 4130000000
    Parameters: 28090000
  Name: seresnet50_b32x8_imagenet
  Results:
  - Dataset: ImageNet
    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/seresnet101/seresnet101_b32x8_imagenet.py
  In Collection: SEResNet
  Metadata:
    FLOPs: 7860000000
    Parameters: 49330000
  Name: seresnet101_b32x8_imagenet
  Results:
  - Dataset: ImageNet
    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