Collections:
- Metadata:
    Training Data:
    - Cityscapes
    - ADE20K
  Name: dnlnet
Models:
- Config: configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py
  In Collection: dnlnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 390.62
    lr schd: 40000
    memory (GB): 7.3
  Name: dnl_r50-d8_512x1024_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.61
    Task: Semantic Segmentation
  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/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py
  In Collection: dnlnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 510.2
    lr schd: 40000
    memory (GB): 10.9
  Name: dnl_r101-d8_512x1024_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.31
    Task: Semantic Segmentation
  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/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py
  In Collection: dnlnet
  Metadata:
    backbone: R-50-D8
    crop size: (769,769)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (769,769)
      value: 666.67
    lr schd: 40000
    memory (GB): 9.2
  Name: dnl_r50-d8_769x769_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.44
      mIoU(ms+flip): 80.27
    Task: Semantic Segmentation
  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/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py
  In Collection: dnlnet
  Metadata:
    backbone: R-101-D8
    crop size: (769,769)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (769,769)
      value: 980.39
    lr schd: 40000
    memory (GB): 12.6
  Name: dnl_r101-d8_769x769_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 76.39
      mIoU(ms+flip): 77.77
    Task: Semantic Segmentation
  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/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py
  In Collection: dnlnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,1024)
    lr schd: 80000
  Name: dnl_r50-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.33
    Task: Semantic Segmentation
  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/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py
  In Collection: dnlnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,1024)
    lr schd: 80000
  Name: dnl_r101-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 80.41
    Task: Semantic Segmentation
  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/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py
  In Collection: dnlnet
  Metadata:
    backbone: R-50-D8
    crop size: (769,769)
    lr schd: 80000
  Name: dnl_r50-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.36
      mIoU(ms+flip): 80.7
    Task: Semantic Segmentation
  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/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py
  In Collection: dnlnet
  Metadata:
    backbone: R-101-D8
    crop size: (769,769)
    lr schd: 80000
  Name: dnl_r101-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.41
      mIoU(ms+flip): 80.68
    Task: Semantic Segmentation
  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/dnlnet/dnl_r50-d8_512x512_80k_ade20k.py
  In Collection: dnlnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,512)
      value: 48.4
    lr schd: 80000
    memory (GB): 8.8
  Name: dnl_r50-d8_512x512_80k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 41.76
      mIoU(ms+flip): 42.99
    Task: Semantic Segmentation
  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/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py
  In Collection: dnlnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,512)
      value: 79.74
    lr schd: 80000
    memory (GB): 12.8
  Name: dnl_r101-d8_512x512_80k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 43.76
      mIoU(ms+flip): 44.91
    Task: Semantic Segmentation
  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/dnlnet/dnl_r50-d8_512x512_160k_ade20k.py
  In Collection: dnlnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 160000
  Name: dnl_r50-d8_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 41.87
      mIoU(ms+flip): 43.01
    Task: Semantic Segmentation
  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/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py
  In Collection: dnlnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 160000
  Name: dnl_r101-d8_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 44.25
      mIoU(ms+flip): 45.78
    Task: Semantic Segmentation
  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