mmsegmentation/configs/dnlnet/metafile.yml

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

Models:

  - Name: dnl_r50-d8_512x1024_40k_cityscapes
    In Collection: dnl
    Metadata:
      inference time (ms/im):
        - value: 390.62
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 78.61
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth
    Config: configs/dnl/dnl_r50-d8_512x1024_40k_cityscapes.py



  - Name: dnl_r101-d8_512x1024_40k_cityscapes
    In Collection: dnl
    Metadata:
      inference time (ms/im):
        - value: 510.2
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 78.31
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth
    Config: configs/dnl/dnl_r101-d8_512x1024_40k_cityscapes.py



  - Name: dnl_r50-d8_769x769_40k_cityscapes
    In Collection: dnl
    Metadata:
      inference time (ms/im):
        - value: 666.67
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 78.44
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth
    Config: configs/dnl/dnl_r50-d8_769x769_40k_cityscapes.py



  - Name: dnl_r101-d8_769x769_40k_cityscapes
    In Collection: dnl
    Metadata:
      inference time (ms/im):
        - value: 980.39
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 76.39
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth
    Config: configs/dnl/dnl_r101-d8_769x769_40k_cityscapes.py



  - Name: dnl_r50-d8_512x1024_80k_cityscapes
    In Collection: dnl
    Metadata:
      inference time (ms/im):
        - value: 390.62
          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/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth
    Config: configs/dnl/dnl_r50-d8_512x1024_80k_cityscapes.py



  - Name: dnl_r101-d8_512x1024_80k_cityscapes
    In Collection: dnl
    Metadata:
      inference time (ms/im):
        - value: 510.2
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 80.41
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth
    Config: configs/dnl/dnl_r101-d8_512x1024_80k_cityscapes.py



  - Name: dnl_r50-d8_769x769_80k_cityscapes
    In Collection: dnl
    Metadata:
      inference time (ms/im):
        - value: 666.67
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 79.36
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth
    Config: configs/dnl/dnl_r50-d8_769x769_80k_cityscapes.py



  - Name: dnl_r101-d8_769x769_80k_cityscapes
    In Collection: dnl
    Metadata:
      inference time (ms/im):
        - value: 980.39
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 79.41
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth
    Config: configs/dnl/dnl_r101-d8_769x769_80k_cityscapes.py



  - Name: dnl_r50-d8_512x512_80k_ade20k
    In Collection: dnl
    Metadata:
      inference time (ms/im):
        - value: 48.4
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 41.76
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth
    Config: configs/dnl/dnl_r50-d8_512x512_80k_ade20k.py



  - Name: dnl_r101-d8_512x512_80k_ade20k
    In Collection: dnl
    Metadata:
      inference time (ms/im):
        - value: 79.74
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 43.76
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth
    Config: configs/dnl/dnl_r101-d8_512x512_80k_ade20k.py



  - Name: dnl_r50-d8_512x512_160k_ade20k
    In Collection: dnl
    Metadata:
      inference time (ms/im):
        - value: 48.4
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 41.87
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth
    Config: configs/dnl/dnl_r50-d8_512x512_160k_ade20k.py



  - Name: dnl_r101-d8_512x512_160k_ade20k
    In Collection: dnl
    Metadata:
      inference time (ms/im):
        - value: 79.74
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 44.25
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth
    Config: configs/dnl/dnl_r101-d8_512x512_160k_ade20k.py