Collections:
- Metadata:
    Training Data:
    - Cityscapes
    - ADE20K
    - Pascal VOC 2012 + Aug
  Name: gcnet
Models:
- Config: configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py
  In Collection: gcnet
  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: 254.45
    lr schd: 40000
    memory (GB): 5.8
  Name: gcnet_r50-d8_512x1024_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.69
      mIoU(ms+flip): 78.56
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436-4b0fd17b.pth
- Config: configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py
  In Collection: gcnet
  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: 383.14
    lr schd: 40000
    memory (GB): 9.2
  Name: gcnet_r101-d8_512x1024_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.28
      mIoU(ms+flip): 79.34
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436-5e62567f.pth
- Config: configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py
  In Collection: gcnet
  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: 598.8
    lr schd: 40000
    memory (GB): 6.5
  Name: gcnet_r50-d8_769x769_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.12
      mIoU(ms+flip): 80.09
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814-a26f4471.pth
- Config: configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py
  In Collection: gcnet
  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: 884.96
    lr schd: 40000
    memory (GB): 10.5
  Name: gcnet_r101-d8_769x769_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.95
      mIoU(ms+flip): 80.71
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550-ca4f0a84.pth
- Config: configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py
  In Collection: gcnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,1024)
    lr schd: 80000
  Name: gcnet_r50-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.48
      mIoU(ms+flip): 80.01
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450-ef8f069b.pth
- Config: configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py
  In Collection: gcnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,1024)
    lr schd: 80000
  Name: gcnet_r101-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.03
      mIoU(ms+flip): 79.84
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450-778ebf69.pth
- Config: configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py
  In Collection: gcnet
  Metadata:
    backbone: R-50-D8
    crop size: (769,769)
    lr schd: 80000
  Name: gcnet_r50-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.68
      mIoU(ms+flip): 80.66
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516-4839565b.pth
- Config: configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py
  In Collection: gcnet
  Metadata:
    backbone: R-101-D8
    crop size: (769,769)
    lr schd: 80000
  Name: gcnet_r101-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.18
      mIoU(ms+flip): 80.71
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628-8e043423.pth
- Config: configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
  In Collection: gcnet
  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: 42.77
    lr schd: 80000
    memory (GB): 8.5
  Name: gcnet_r50-d8_512x512_80k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 41.47
      mIoU(ms+flip): 42.85
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146-91a6da41.pth
- Config: configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py
  In Collection: gcnet
  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: 65.79
    lr schd: 80000
    memory (GB): 12.0
  Name: gcnet_r101-d8_512x512_80k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 42.82
      mIoU(ms+flip): 44.54
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811-c3fcb6dd.pth
- Config: configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py
  In Collection: gcnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 160000
  Name: gcnet_r50-d8_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 42.37
      mIoU(ms+flip): 43.52
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122-d95f3e1f.pth
- Config: configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py
  In Collection: gcnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 160000
  Name: gcnet_r101-d8_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 43.69
      mIoU(ms+flip): 45.21
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406-615528d7.pth
- Config: configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py
  In Collection: gcnet
  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: 42.83
    lr schd: 20000
    memory (GB): 5.8
  Name: gcnet_r50-d8_512x512_20k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 76.42
      mIoU(ms+flip): 77.51
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701-3cbfdab1.pth
- Config: configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py
  In Collection: gcnet
  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: 67.57
    lr schd: 20000
    memory (GB): 9.2
  Name: gcnet_r101-d8_512x512_20k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 77.41
      mIoU(ms+flip): 78.56
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713-6c720aa9.pth
- Config: configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py
  In Collection: gcnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 40000
  Name: gcnet_r50-d8_512x512_40k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 76.24
      mIoU(ms+flip): 77.63
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105-9797336d.pth
- Config: configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py
  In Collection: gcnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 40000
  Name: gcnet_r101-d8_512x512_40k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 77.84
      mIoU(ms+flip): 78.59
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806-1e38208d.pth