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添加openmmlab测试用例  
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Collections:
  - Name: ResNet
    Metadata:
      Training Data: ImageNet
      Training Techniques:
        - SGD with Momentum
        - Weight Decay
      Training Resources: 8x V100 GPUs
      Epochs: 100
      Batch Size: 256
      Architecture:
        - ResNet
    Paper: https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html
    README: configs/resnet/README.md

Models:
- Config: configs/resnet/resnet18_b16x8_cifar10.py
  In Collection: ResNet
  Metadata:
    FLOPs: 560000000
    Parameters: 11170000
    Training Data: CIFAR-10
    Training Resources: 8x 1080 GPUs
    Epochs: 200
    Batch Size: 128
  Name: resnet18_b16x8_cifar10
  Results:
  - Dataset: CIFAR-10
    Metrics:
      Top 1 Accuracy: 94.72
    Task: Image Classification
  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20200823-f906fa4e.pth
- Config: configs/resnet/resnet34_b16x8_cifar10.py
  In Collection: ResNet
  Metadata:
    FLOPs: 1160000000
    Parameters: 21280000
    Training Data: CIFAR-10
    Training Resources: 8x 1080 GPUs
    Epochs: 200
    Batch Size: 128
  Name: resnet34_b16x8_cifar10
  Results:
  - Dataset: CIFAR-10
    Metrics:
      Top 1 Accuracy: 95.34
    Task: Image Classification
  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20200823-52d5d832.pth
- Config: configs/resnet/resnet50_b16x8_cifar10.py
  In Collection: ResNet
  Metadata:
    FLOPs: 1310000000
    Parameters: 23520000
    Training Data: CIFAR-10
    Training Resources: 8x 1080 GPUs
    Epochs: 200
    Batch Size: 128
  Name: resnet50_b16x8_cifar10
  Results:
  - Dataset: CIFAR-10
    Metrics:
      Top 1 Accuracy: 95.36
    Task: Image Classification
  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20200823-882aa7b1.pth
- Config: configs/resnet/resnet101_b16x8_cifar10.py
  In Collection: ResNet
  Metadata:
    FLOPs: 2520000000
    Parameters: 42510000
    Training Data: CIFAR-10
    Training Resources: 8x 1080 GPUs
    Epochs: 200
    Batch Size: 128
  Name: resnet101_b16x8_cifar10
  Results:
  - Dataset: CIFAR-10
    Metrics:
      Top 1 Accuracy: 95.66
    Task: Image Classification
  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20200823-d9501bbc.pth
- Config: configs/resnet/resnet152_b16x8_cifar10.py
  In Collection: ResNet
  Metadata:
    FLOPs: 3740000000
    Parameters: 58160000
    Training Data: CIFAR-10
    Training Resources: 8x 1080 GPUs
    Epochs: 200
    Batch Size: 128
  Name: resnet152_b16x8_cifar10
  Results:
  - Dataset: CIFAR-10
    Metrics:
      Top 1 Accuracy: 95.96
    Task: Image Classification
  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20200823-ad4d5d0c.pth
- Config: configs/resnet/resnet50_b16x8_cifar100.py
  In Collection: ResNet
  Metadata:
    FLOPs: 1310000000
    Parameters: 23710000
    Training Data: CIFAR-100
    Training Resources: 8x 1080 GPUs
    Epochs: 200
    Batch Size: 128
  Name: resnet50_b16x8_cifar100
  Results:
  - Dataset: CIFAR-100
    Metrics:
      Top 1 Accuracy: 80.51
      Top 5 Accuracy: 95.27
    Task: Image Classification
  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_cifar100_20210410-37f13c16.pth
- Config: configs/resnet/resnet18_b32x8_imagenet.py
  In Collection: ResNet
  Metadata:
    FLOPs: 1820000000
    Parameters: 11690000
  Name: resnet18_b32x8_imagenet
  Results:
  - Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 70.07
      Top 5 Accuracy: 89.44
    Task: Image Classification
  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.pth
- Config: configs/resnet/resnet34_b32x8_imagenet.py
  In Collection: ResNet
  Metadata:
    FLOPs: 3680000000
    Parameters: 2180000
  Name: resnet34_b32x8_imagenet
  Results:
  - Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 73.85
      Top 5 Accuracy: 91.53
    Task: Image Classification
  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.pth
- Config: configs/resnet/resnet50_b32x8_imagenet.py
  In Collection: ResNet
  Metadata:
    FLOPs: 4120000000
    Parameters: 25560000
  Name: resnet50_b32x8_imagenet
  Results:
  - Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 76.55
      Top 5 Accuracy: 93.15
    Task: Image Classification
  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth
- Config: configs/resnet/resnet101_b32x8_imagenet.py
  In Collection: ResNet
  Metadata:
    FLOPs: 7850000000
    Parameters: 44550000
  Name: resnet101_b32x8_imagenet
  Results:
  - Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 78.18
      Top 5 Accuracy: 94.03
    Task: Image Classification
  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.pth
- Config: configs/resnet/resnet152_b32x8_imagenet.py
  In Collection: ResNet
  Metadata:
    FLOPs: 11580000000
    Parameters: 60190000
  Name: resnet152_b32x8_imagenet
  Results:
  - Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 78.63
      Top 5 Accuracy: 94.16
    Task: Image Classification
  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.pth
- Config: configs/resnet/resnetv1d50_b32x8_imagenet.py
  In Collection: ResNet
  Metadata:
    FLOPs: 4360000000
    Parameters: 25580000
  Name: resnetv1d50_b32x8_imagenet
  Results:
  - Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 77.4
      Top 5 Accuracy: 93.66
    Task: Image Classification
  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_batch256_imagenet_20200708-1ad0ce94.pth
- Config: configs/resnet/resnetv1d101_b32x8_imagenet.py
  In Collection: ResNet
  Metadata:
    FLOPs: 8090000000
    Parameters: 44570000
  Name: resnetv1d101_b32x8_imagenet
  Results:
  - Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 78.85
      Top 5 Accuracy: 94.38
    Task: Image Classification
  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_batch256_imagenet_20200708-9cb302ef.pth
- Config: configs/resnet/resnetv1d152_b32x8_imagenet.py
  In Collection: ResNet
  Metadata:
    FLOPs: 11820000000
    Parameters: 60210000
  Name: resnetv1d152_b32x8_imagenet
  Results:
  - Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 79.35
      Top 5 Accuracy: 94.61
    Task: Image Classification
  Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_batch256_imagenet_20200708-e79cb6a2.pth