Collections:
  - Name: Shufflenet V2
    Metadata:
      Training Data: ImageNet
      Training Techniques:
        - SGD with Momentum
        - Weight Decay
        - No BN decay
      Training Resources: 8x 1080 GPUs
      Epochs: 300
      Batch Size: 1024
      Architecture:
        - Shufflenet V2
    Paper: https://openaccess.thecvf.com/content_ECCV_2018/papers/Ningning_Light-weight_CNN_Architecture_ECCV_2018_paper.pdf
    README: configs/shufflenet_v2/README.md

Models:
- Config: configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py
  In Collection: Shufflenet V2
  Metadata:
    FLOPs: 149000000
    Parameters: 2280000
  Name: shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet
  Results:
  - Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 69.55
      Top 5 Accuracy: 88.92
    Task: Image Classification
  Weights: https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth