Collections:
- Metadata:
    Training Data:
    - Cityscapes
    - ADE20K
    - ' Pascal VOC 2012 + Aug'
    - ' Pascal Context'
    - ' Pascal Context 59'
  Name: deeplabv3plus
Models:
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py
  In Collection: deeplabv3plus
  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: 253.81
    lr schd: 40000
    memory (GB): 7.5
  Name: deeplabv3plus_r50-d8_512x1024_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.61
      mIoU(ms+flip): 81.01
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py
  In Collection: deeplabv3plus
  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: 384.62
    lr schd: 40000
    memory (GB): 11.0
  Name: deeplabv3plus_r101-d8_512x1024_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 80.21
      mIoU(ms+flip): 81.82
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py
  In Collection: deeplabv3plus
  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: 581.4
    lr schd: 40000
    memory (GB): 8.5
  Name: deeplabv3plus_r50-d8_769x769_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.97
      mIoU(ms+flip): 80.46
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py
  In Collection: deeplabv3plus
  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): 12.5
  Name: deeplabv3plus_r101-d8_769x769_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.46
      mIoU(ms+flip): 80.5
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-18-D8
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 70.08
    lr schd: 80000
    memory (GB): 2.2
  Name: deeplabv3plus_r18-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 76.89
      mIoU(ms+flip): 78.76
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-50-D8
    crop size: (512,1024)
    lr schd: 80000
  Name: deeplabv3plus_r50-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 80.09
      mIoU(ms+flip): 81.13
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-101-D8
    crop size: (512,1024)
    lr schd: 80000
  Name: deeplabv3plus_r101-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 80.97
      mIoU(ms+flip): 82.03
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-18-D8
    crop size: (769,769)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (769,769)
      value: 174.22
    lr schd: 80000
    memory (GB): 2.5
  Name: deeplabv3plus_r18-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 76.26
      mIoU(ms+flip): 77.91
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-50-D8
    crop size: (769,769)
    lr schd: 80000
  Name: deeplabv3plus_r50-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.83
      mIoU(ms+flip): 81.48
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-101-D8
    crop size: (769,769)
    lr schd: 80000
  Name: deeplabv3plus_r101-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 80.98
      mIoU(ms+flip): 82.18
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405-a7573d20.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-101-D16-MG124
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 133.69
    lr schd: 40000
    memory (GB): 5.8
  Name: deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.09
      mIoU(ms+flip): 80.36
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-101-D16-MG124
    crop size: (512,1024)
    lr schd: 80000
    memory (GB): 9.9
  Name: deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.9
      mIoU(ms+flip): 81.33
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-18b-D8
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 66.89
    lr schd: 80000
    memory (GB): 2.1
  Name: deeplabv3plus_r18b-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 75.87
      mIoU(ms+flip): 77.52
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-50b-D8
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 253.81
    lr schd: 80000
    memory (GB): 7.4
  Name: deeplabv3plus_r50b-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 80.28
      mIoU(ms+flip): 81.44
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-101b-D8
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 384.62
    lr schd: 80000
    memory (GB): 10.9
  Name: deeplabv3plus_r101b-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 80.16
      mIoU(ms+flip): 81.41
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-18b-D8
    crop size: (769,769)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (769,769)
      value: 167.79
    lr schd: 80000
    memory (GB): 2.4
  Name: deeplabv3plus_r18b-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 76.36
      mIoU(ms+flip): 78.24
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-50b-D8
    crop size: (769,769)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (769,769)
      value: 581.4
    lr schd: 80000
    memory (GB): 8.4
  Name: deeplabv3plus_r50b-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.41
      mIoU(ms+flip): 80.56
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-101b-D8
    crop size: (769,769)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (769,769)
      value: 909.09
    lr schd: 80000
    memory (GB): 12.3
  Name: deeplabv3plus_r101b-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.88
      mIoU(ms+flip): 81.46
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.py
  In Collection: deeplabv3plus
  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): 10.6
  Name: deeplabv3plus_r50-d8_512x512_80k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 42.72
      mIoU(ms+flip): 43.75
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py
  In Collection: deeplabv3plus
  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: 70.62
    lr schd: 80000
    memory (GB): 14.1
  Name: deeplabv3plus_r101-d8_512x512_80k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 44.6
      mIoU(ms+flip): 46.06
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 160000
  Name: deeplabv3plus_r50-d8_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 43.95
      mIoU(ms+flip): 44.93
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 160000
  Name: deeplabv3plus_r101-d8_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 45.47
      mIoU(ms+flip): 46.35
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py
  In Collection: deeplabv3plus
  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: 47.62
    lr schd: 20000
    memory (GB): 7.6
  Name: deeplabv3plus_r50-d8_512x512_20k_voc12aug
  Results:
    Dataset: ' Pascal VOC 2012 + Aug'
    Metrics:
      mIoU: 75.93
      mIoU(ms+flip): 77.5
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py
  In Collection: deeplabv3plus
  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: 72.05
    lr schd: 20000
    memory (GB): 11.0
  Name: deeplabv3plus_r101-d8_512x512_20k_voc12aug
  Results:
    Dataset: ' Pascal VOC 2012 + Aug'
    Metrics:
      mIoU: 77.22
      mIoU(ms+flip): 78.59
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 40000
  Name: deeplabv3plus_r50-d8_512x512_40k_voc12aug
  Results:
    Dataset: ' Pascal VOC 2012 + Aug'
    Metrics:
      mIoU: 76.81
      mIoU(ms+flip): 77.57
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 40000
  Name: deeplabv3plus_r101-d8_512x512_40k_voc12aug
  Results:
    Dataset: ' Pascal VOC 2012 + Aug'
    Metrics:
      mIoU: 78.62
      mIoU(ms+flip): 79.53
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-101-D8
    crop size: (480,480)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (480,480)
      value: 110.01
    lr schd: 40000
  Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context
  Results:
    Dataset: ' Pascal Context'
    Metrics:
      mIoU: 47.3
      mIoU(ms+flip): 48.47
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-101-D8
    crop size: (480,480)
    lr schd: 80000
  Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
  Results:
    Dataset: ' Pascal Context'
    Metrics:
      mIoU: 47.23
      mIoU(ms+flip): 48.26
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-101-D8
    crop size: (480,480)
    lr schd: 40000
  Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context_59
  Results:
    Dataset: ' Pascal Context 59'
    Metrics:
      mIoU: 52.86
      mIoU(ms+flip): 54.54
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py
  In Collection: deeplabv3plus
  Metadata:
    backbone: R-101-D8
    crop size: (480,480)
    lr schd: 80000
  Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context_59
  Results:
    Dataset: ' Pascal Context 59'
    Metrics:
      mIoU: 53.2
      mIoU(ms+flip): 54.67
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth