Collections:
- Name: upernet
  Metadata:
    Training Data:
    - Cityscapes
    - ADE20K
    - Pascal VOC 2012 + Aug
  Paper:
    URL: https://arxiv.org/pdf/1807.10221.pdf
    Title: Unified Perceptual Parsing for Scene Understanding
  README: configs/upernet/README.md
  Code:
    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
    Version: v0.17.0
  Converted From:
    Code: https://github.com/CSAILVision/unifiedparsing
Models:
- Name: upernet_r50_512x1024_40k_cityscapes
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (512,1024)
    lr schd: 40000
    inference time (ms/im):
    - value: 235.29
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,1024)
    Training Memory (GB): 6.4
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.1
      mIoU(ms+flip): 78.37
  Config: configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth
- Name: upernet_r101_512x1024_40k_cityscapes
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (512,1024)
    lr schd: 40000
    inference time (ms/im):
    - value: 263.85
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,1024)
    Training Memory (GB): 7.4
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.69
      mIoU(ms+flip): 80.11
  Config: configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth
- Name: upernet_r50_769x769_40k_cityscapes
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (769,769)
    lr schd: 40000
    inference time (ms/im):
    - value: 568.18
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (769,769)
    Training Memory (GB): 7.2
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.98
      mIoU(ms+flip): 79.7
  Config: configs/upernet/upernet_r50_769x769_40k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth
- Name: upernet_r101_769x769_40k_cityscapes
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (769,769)
    lr schd: 40000
    inference time (ms/im):
    - value: 641.03
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (769,769)
    Training Memory (GB): 8.4
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.03
      mIoU(ms+flip): 80.77
  Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth
- Name: upernet_r50_512x1024_80k_cityscapes
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (512,1024)
    lr schd: 80000
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.19
      mIoU(ms+flip): 79.19
  Config: configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth
- Name: upernet_r101_512x1024_80k_cityscapes
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (512,1024)
    lr schd: 80000
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.4
      mIoU(ms+flip): 80.46
  Config: configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth
- Name: upernet_r50_769x769_80k_cityscapes
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (769,769)
    lr schd: 80000
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.39
      mIoU(ms+flip): 80.92
  Config: configs/upernet/upernet_r50_769x769_80k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth
- Name: upernet_r101_769x769_80k_cityscapes
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (769,769)
    lr schd: 80000
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 80.1
      mIoU(ms+flip): 81.49
  Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth
- Name: upernet_r50_512x512_80k_ade20k
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (512,512)
    lr schd: 80000
    inference time (ms/im):
    - value: 42.74
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 8.1
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 40.7
      mIoU(ms+flip): 41.81
  Config: configs/upernet/upernet_r50_512x512_80k_ade20k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth
- Name: upernet_r101_512x512_80k_ade20k
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (512,512)
    lr schd: 80000
    inference time (ms/im):
    - value: 49.16
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 9.1
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 42.91
      mIoU(ms+flip): 43.96
  Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth
- Name: upernet_r50_512x512_160k_ade20k
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (512,512)
    lr schd: 160000
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 42.05
      mIoU(ms+flip): 42.78
  Config: configs/upernet/upernet_r50_512x512_160k_ade20k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth
- Name: upernet_r101_512x512_160k_ade20k
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (512,512)
    lr schd: 160000
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 43.82
      mIoU(ms+flip): 44.85
  Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth
- Name: upernet_r50_512x512_20k_voc12aug
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (512,512)
    lr schd: 20000
    inference time (ms/im):
    - value: 43.16
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 6.4
  Results:
  - Task: Semantic Segmentation
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 74.82
      mIoU(ms+flip): 76.35
  Config: configs/upernet/upernet_r50_512x512_20k_voc12aug.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth
- Name: upernet_r101_512x512_20k_voc12aug
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (512,512)
    lr schd: 20000
    inference time (ms/im):
    - value: 50.05
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 7.5
  Results:
  - Task: Semantic Segmentation
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 77.1
      mIoU(ms+flip): 78.29
  Config: configs/upernet/upernet_r101_512x512_20k_voc12aug.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth
- Name: upernet_r50_512x512_40k_voc12aug
  In Collection: upernet
  Metadata:
    backbone: R-50
    crop size: (512,512)
    lr schd: 40000
  Results:
  - Task: Semantic Segmentation
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 75.92
      mIoU(ms+flip): 77.44
  Config: configs/upernet/upernet_r50_512x512_40k_voc12aug.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth
- Name: upernet_r101_512x512_40k_voc12aug
  In Collection: upernet
  Metadata:
    backbone: R-101
    crop size: (512,512)
    lr schd: 40000
  Results:
  - Task: Semantic Segmentation
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 77.43
      mIoU(ms+flip): 78.56
  Config: configs/upernet/upernet_r101_512x512_40k_voc12aug.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth
