Collections:
- Metadata:
    Training Data:
    - Cityscapes
    - ' HRNet backbone'
    - ' ResNet backbone'
    - ADE20K
    - Pascal VOC 2012 + Aug
  Name: ocrnet
Models:
- Config: configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W18-Small
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 95.69
    lr schd: 40000
    memory (GB): 3.5
  Name: ocrnet_hr18s_512x1024_40k_cityscapes
  Results:
    Dataset: ' HRNet backbone'
    Metrics:
      mIoU: 74.3
      mIoU(ms+flip): 75.95
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304-fa2436c2.pth
- Config: configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W18
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 133.33
    lr schd: 40000
    memory (GB): 4.7
  Name: ocrnet_hr18_512x1024_40k_cityscapes
  Results:
    Dataset: ' HRNet backbone'
    Metrics:
      mIoU: 77.72
      mIoU(ms+flip): 79.49
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth
- Config: configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W48
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 236.97
    lr schd: 40000
    memory (GB): 8.0
  Name: ocrnet_hr48_512x1024_40k_cityscapes
  Results:
    Dataset: ' HRNet backbone'
    Metrics:
      mIoU: 80.58
      mIoU(ms+flip): 81.79
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth
- Config: configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W18-Small
    crop size: (512,1024)
    lr schd: 80000
  Name: ocrnet_hr18s_512x1024_80k_cityscapes
  Results:
    Dataset: ' HRNet backbone'
    Metrics:
      mIoU: 77.16
      mIoU(ms+flip): 78.66
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth
- Config: configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W18
    crop size: (512,1024)
    lr schd: 80000
  Name: ocrnet_hr18_512x1024_80k_cityscapes
  Results:
    Dataset: ' HRNet backbone'
    Metrics:
      mIoU: 78.57
      mIoU(ms+flip): 80.46
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth
- Config: configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W48
    crop size: (512,1024)
    lr schd: 80000
  Name: ocrnet_hr48_512x1024_80k_cityscapes
  Results:
    Dataset: ' HRNet backbone'
    Metrics:
      mIoU: 80.7
      mIoU(ms+flip): 81.87
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth
- Config: configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W18-Small
    crop size: (512,1024)
    lr schd: 160000
  Name: ocrnet_hr18s_512x1024_160k_cityscapes
  Results:
    Dataset: ' HRNet backbone'
    Metrics:
      mIoU: 78.45
      mIoU(ms+flip): 79.97
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth
- Config: configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W18
    crop size: (512,1024)
    lr schd: 160000
  Name: ocrnet_hr18_512x1024_160k_cityscapes
  Results:
    Dataset: ' HRNet backbone'
    Metrics:
      mIoU: 79.47
      mIoU(ms+flip): 80.91
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth
- Config: configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W48
    crop size: (512,1024)
    lr schd: 160000
  Name: ocrnet_hr48_512x1024_160k_cityscapes
  Results:
    Dataset: ' HRNet backbone'
    Metrics:
      mIoU: 81.35
      mIoU(ms+flip): 82.7
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth
- Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py
  In Collection: ocrnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,1024)
    lr schd: 40000
  Name: ocrnet_r101-d8_512x1024_40k_b8_cityscapes
  Results:
    Dataset: ' ResNet backbone'
    Metrics:
      mIoU: 80.09
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes-02ac0f13.pth
- Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py
  In Collection: ocrnet
  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: 331.13
    lr schd: 40000
    memory (GB): 8.8
  Name: ocrnet_r101-d8_512x1024_40k_b16_cityscapes
  Results:
    Dataset: ' ResNet backbone'
    Metrics:
      mIoU: 80.3
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes-db500f80.pth
- Config: configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py
  In Collection: ocrnet
  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: 331.13
    lr schd: 80000
    memory (GB): 8.8
  Name: ocrnet_r101-d8_512x1024_80k_b16_cityscapes
  Results:
    Dataset: ' ResNet backbone'
    Metrics:
      mIoU: 80.81
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes-78688424.pth
- Config: configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W18-Small
    crop size: (512,512)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,512)
      value: 34.51
    lr schd: 80000
    memory (GB): 6.7
  Name: ocrnet_hr18s_512x512_80k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 35.06
      mIoU(ms+flip): 35.8
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth
- Config: configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W18
    crop size: (512,512)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,512)
      value: 52.83
    lr schd: 80000
    memory (GB): 7.9
  Name: ocrnet_hr18_512x512_80k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 37.79
      mIoU(ms+flip): 39.16
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth
- Config: configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W48
    crop size: (512,512)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,512)
      value: 58.86
    lr schd: 80000
    memory (GB): 11.2
  Name: ocrnet_hr48_512x512_80k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 43.0
      mIoU(ms+flip): 44.3
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth
- Config: configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W18-Small
    crop size: (512,512)
    lr schd: 160000
  Name: ocrnet_hr18s_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 37.19
      mIoU(ms+flip): 38.4
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth
- Config: configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W18
    crop size: (512,512)
    lr schd: 160000
  Name: ocrnet_hr18_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 39.32
      mIoU(ms+flip): 40.8
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth
- Config: configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W48
    crop size: (512,512)
    lr schd: 160000
  Name: ocrnet_hr48_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 43.25
      mIoU(ms+flip): 44.88
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth
- Config: configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W18-Small
    crop size: (512,512)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,512)
      value: 31.7
    lr schd: 20000
    memory (GB): 3.5
  Name: ocrnet_hr18s_512x512_20k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 71.7
      mIoU(ms+flip): 73.84
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth
- Config: configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W18
    crop size: (512,512)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,512)
      value: 50.23
    lr schd: 20000
    memory (GB): 4.7
  Name: ocrnet_hr18_512x512_20k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 74.75
      mIoU(ms+flip): 77.11
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth
- Config: configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W48
    crop size: (512,512)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,512)
      value: 56.09
    lr schd: 20000
    memory (GB): 8.1
  Name: ocrnet_hr48_512x512_20k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 77.72
      mIoU(ms+flip): 79.87
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth
- Config: configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W18-Small
    crop size: (512,512)
    lr schd: 40000
  Name: ocrnet_hr18s_512x512_40k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 72.76
      mIoU(ms+flip): 74.6
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth
- Config: configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W18
    crop size: (512,512)
    lr schd: 40000
  Name: ocrnet_hr18_512x512_40k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 74.98
      mIoU(ms+flip): 77.4
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth
- Config: configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py
  In Collection: ocrnet
  Metadata:
    backbone: HRNetV2p-W48
    crop size: (512,512)
    lr schd: 40000
  Name: ocrnet_hr48_512x512_40k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 77.14
      mIoU(ms+flip): 79.71
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth