Collections:
  - Name: ResNeSt
    Metadata:
      Training Data:
        - Cityscapes
        - ADE20K

Models:

  - Name: fcn_s101-d8_512x1024_80k_cityscapes
    In Collection: FCN
    Metadata:
      inference time (ms/im):
        - value: 418.41
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 77.56
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth
    Config: configs/fcn/fcn_s101-d8_512x1024_80k_cityscapes.py



  - Name: pspnet_s101-d8_512x1024_80k_cityscapes
    In Collection: PSPNet
    Metadata:
      inference time (ms/im):
        - value: 396.83
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 78.57
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth
    Config: configs/pspnet/pspnet_s101-d8_512x1024_80k_cityscapes.py



  - Name: deeplabv3_s101-d8_512x1024_80k_cityscapes
    In Collection: DeepLabV3
    Metadata:
      inference time (ms/im):
        - value: 531.91
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 79.67
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth
    Config: configs/deeplabv3/deeplabv3_s101-d8_512x1024_80k_cityscapes.py



  - Name: deeplabv3plus_s101-d8_512x1024_80k_cityscapes
    In Collection: DeepLabV3+
    Metadata:
      inference time (ms/im):
        - value: 423.73
          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/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth
    Config: configs/deeplabv3+/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py



  - Name: fcn_s101-d8_512x512_160k_ade20k
    In Collection: FCN
    Metadata:
      inference time (ms/im):
        - value: 77.76
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 45.62
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth
    Config: configs/fcn/fcn_s101-d8_512x512_160k_ade20k.py



  - Name: pspnet_s101-d8_512x512_160k_ade20k
    In Collection: PSPNet
    Metadata:
      inference time (ms/im):
        - value: 76.8
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 45.44
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth
    Config: configs/pspnet/pspnet_s101-d8_512x512_160k_ade20k.py



  - Name: deeplabv3_s101-d8_512x512_160k_ade20k
    In Collection: DeepLabV3
    Metadata:
      inference time (ms/im):
        - value: 107.76
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 45.71
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth
    Config: configs/deeplabv3/deeplabv3_s101-d8_512x512_160k_ade20k.py



  - Name: deeplabv3plus_s101-d8_512x512_160k_ade20k
    In Collection: DeepLabV3+
    Metadata:
      inference time (ms/im):
        - value: 83.61
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 46.47
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth
    Config: configs/deeplabv3+/deeplabv3plus_s101-d8_512x512_160k_ade20k.py