Collections:
- Metadata:
    Training Data:
    - Cityscapes
    - ADE20K
    - Pascal VOC 2012 + Aug
  Name: ann
Models:
- Config: configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py
  In Collection: ann
  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: 269.54
    lr schd: 40000
    memory (GB): 6.0
  Name: ann_r50-d8_512x1024_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.4
      mIoU(ms+flip): 78.57
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth
- Config: configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py
  In Collection: ann
  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: 392.16
    lr schd: 40000
    memory (GB): 9.5
  Name: ann_r101-d8_512x1024_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 76.55
      mIoU(ms+flip): 78.85
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth
- Config: configs/ann/ann_r50-d8_769x769_40k_cityscapes.py
  In Collection: ann
  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: 588.24
    lr schd: 40000
    memory (GB): 6.8
  Name: ann_r50-d8_769x769_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.89
      mIoU(ms+flip): 80.46
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth
- Config: configs/ann/ann_r101-d8_769x769_40k_cityscapes.py
  In Collection: ann
  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: 869.57
    lr schd: 40000
    memory (GB): 10.7
  Name: ann_r101-d8_769x769_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.32
      mIoU(ms+flip): 80.94
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth
- Config: configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py
  In Collection: ann
  Metadata:
    backbone: R-50-D8
    crop size: (512,1024)
    lr schd: 80000
  Name: ann_r50-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.34
      mIoU(ms+flip): 78.65
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth
- Config: configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py
  In Collection: ann
  Metadata:
    backbone: R-101-D8
    crop size: (512,1024)
    lr schd: 80000
  Name: ann_r101-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.14
      mIoU(ms+flip): 78.81
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth
- Config: configs/ann/ann_r50-d8_769x769_80k_cityscapes.py
  In Collection: ann
  Metadata:
    backbone: R-50-D8
    crop size: (769,769)
    lr schd: 80000
  Name: ann_r50-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.88
      mIoU(ms+flip): 80.57
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth
- Config: configs/ann/ann_r101-d8_769x769_80k_cityscapes.py
  In Collection: ann
  Metadata:
    backbone: R-101-D8
    crop size: (769,769)
    lr schd: 80000
  Name: ann_r101-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.8
      mIoU(ms+flip): 80.34
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth
- Config: configs/ann/ann_r50-d8_512x512_80k_ade20k.py
  In Collection: ann
  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: 47.6
    lr schd: 80000
    memory (GB): 9.1
  Name: ann_r50-d8_512x512_80k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 41.01
      mIoU(ms+flip): 42.3
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth
- Config: configs/ann/ann_r101-d8_512x512_80k_ade20k.py
  In Collection: ann
  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: 70.82
    lr schd: 80000
    memory (GB): 12.5
  Name: ann_r101-d8_512x512_80k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 42.94
      mIoU(ms+flip): 44.18
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth
- Config: configs/ann/ann_r50-d8_512x512_160k_ade20k.py
  In Collection: ann
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 160000
  Name: ann_r50-d8_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 41.74
      mIoU(ms+flip): 42.62
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth
- Config: configs/ann/ann_r101-d8_512x512_160k_ade20k.py
  In Collection: ann
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 160000
  Name: ann_r101-d8_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 42.94
      mIoU(ms+flip): 44.06
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth
- Config: configs/ann/ann_r50-d8_512x512_20k_voc12aug.py
  In Collection: ann
  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: 47.8
    lr schd: 20000
    memory (GB): 6.0
  Name: ann_r50-d8_512x512_20k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 74.86
      mIoU(ms+flip): 76.13
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth
- Config: configs/ann/ann_r101-d8_512x512_20k_voc12aug.py
  In Collection: ann
  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: 71.74
    lr schd: 20000
    memory (GB): 9.5
  Name: ann_r101-d8_512x512_20k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 77.47
      mIoU(ms+flip): 78.7
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth
- Config: configs/ann/ann_r50-d8_512x512_40k_voc12aug.py
  In Collection: ann
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 40000
  Name: ann_r50-d8_512x512_40k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 76.56
      mIoU(ms+flip): 77.51
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth
- Config: configs/ann/ann_r101-d8_512x512_40k_voc12aug.py
  In Collection: ann
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 40000
  Name: ann_r101-d8_512x512_40k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 76.7
      mIoU(ms+flip): 78.06
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth