Collections:
  - Name: EMANet
    Metadata:
      Training Data:
        - Cityscapes

Models:

  - Name: emanet_r50-d8_512x1024_80k_cityscapes
    In Collection: EMANet
    Metadata:
      inference time (ms/im):
        - value: 218.34
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 77.59
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth
    Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py



  - Name: emanet_r101-d8_512x1024_80k_cityscapes
    In Collection: EMANet
    Metadata:
      inference time (ms/im):
        - value: 348.43
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 79.10
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth
    Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py



  - Name: emanet_r50-d8_769x769_80k_cityscapes
    In Collection: EMANet
    Metadata:
      inference time (ms/im):
        - value: 507.61
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 79.33
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth
    Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py



  - Name: emanet_r101-d8_769x769_80k_cityscapes
    In Collection: EMANet
    Metadata:
      inference time (ms/im):
        - value: 819.67
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 79.62
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth
    Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py