Collections:
- Metadata:
    Training Data:
    - Cityscapes
    - ADE20K
    - Pascal VOC 2012 + Aug
  Name: upernet
Models:
- Config: configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 235.29
    lr schd: 40000
    memory (GB): 6.4
  Name: upernet_r50_512x1024_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.1
      mIoU(ms+flip): 78.37
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth
- Config: configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 263.85
    lr schd: 40000
    memory (GB): 7.4
  Name: upernet_r101_512x1024_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.69
      mIoU(ms+flip): 80.11
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth
- Config: configs/upernet/upernet_r50_769x769_40k_cityscapes.py
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (769,769)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (769,769)
      value: 568.18
    lr schd: 40000
    memory (GB): 7.2
  Name: upernet_r50_769x769_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.98
      mIoU(ms+flip): 79.7
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth
- Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (769,769)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (769,769)
      value: 641.03
    lr schd: 40000
    memory (GB): 8.4
  Name: upernet_r101_769x769_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.03
      mIoU(ms+flip): 80.77
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth
- Config: configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (512,1024)
    lr schd: 80000
  Name: upernet_r50_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.19
      mIoU(ms+flip): 79.19
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth
- Config: configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (512,1024)
    lr schd: 80000
  Name: upernet_r101_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.4
      mIoU(ms+flip): 80.46
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth
- Config: configs/upernet/upernet_r50_769x769_80k_cityscapes.py
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (769,769)
    lr schd: 80000
  Name: upernet_r50_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.39
      mIoU(ms+flip): 80.92
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth
- Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (769,769)
    lr schd: 80000
  Name: upernet_r101_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 80.1
      mIoU(ms+flip): 81.49
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth
- Config: configs/upernet/upernet_r50_512x512_80k_ade20k.py
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (512,512)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,512)
      value: 42.74
    lr schd: 80000
    memory (GB): 8.1
  Name: upernet_r50_512x512_80k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 40.7
      mIoU(ms+flip): 41.81
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth
- Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (512,512)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,512)
      value: 49.16
    lr schd: 80000
    memory (GB): 9.1
  Name: upernet_r101_512x512_80k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 42.91
      mIoU(ms+flip): 43.96
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth
- Config: configs/upernet/upernet_r50_512x512_160k_ade20k.py
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (512,512)
    lr schd: 160000
  Name: upernet_r50_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 42.05
      mIoU(ms+flip): 42.78
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth
- Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (512,512)
    lr schd: 160000
  Name: upernet_r101_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 43.82
      mIoU(ms+flip): 44.85
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth
- Config: configs/upernet/upernet_r50_512x512_20k_voc12aug.py
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (512,512)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,512)
      value: 43.16
    lr schd: 20000
    memory (GB): 6.4
  Name: upernet_r50_512x512_20k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 74.82
      mIoU(ms+flip): 76.35
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth
- Config: configs/upernet/upernet_r101_512x512_20k_voc12aug.py
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (512,512)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,512)
      value: 50.05
    lr schd: 20000
    memory (GB): 7.5
  Name: upernet_r101_512x512_20k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 77.1
      mIoU(ms+flip): 78.29
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth
- Config: configs/upernet/upernet_r50_512x512_40k_voc12aug.py
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (512,512)
    lr schd: 40000
  Name: upernet_r50_512x512_40k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 75.92
      mIoU(ms+flip): 77.44
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth
- Config: configs/upernet/upernet_r101_512x512_40k_voc12aug.py
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (512,512)
    lr schd: 40000
  Name: upernet_r101_512x512_40k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 77.43
      mIoU(ms+flip): 78.56
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth