Collections:
  - Name: GCNet
    Metadata:
      Training Data:
        - Cityscapes
        - Pascal VOC 2012 + Aug
        - ADE20K

Models:

  - Name: gcnet_r50-d8_512x1024_40k_cityscapes
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 254.45
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 77.69
    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_r50-d8_512x1024_40k_cityscapes.py



  - Name: gcnet_r101-d8_512x1024_40k_cityscapes
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 383.14
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 78.28
    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_r101-d8_512x1024_40k_cityscapes.py



  - Name: gcnet_r50-d8_769x769_40k_cityscapes
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 598.8
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 78.12
    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_r50-d8_769x769_40k_cityscapes.py



  - Name: gcnet_r101-d8_769x769_40k_cityscapes
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 884.96
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 78.95
    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_r101-d8_769x769_40k_cityscapes.py



  - Name: gcnet_r50-d8_512x1024_80k_cityscapes
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 254.45
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 78.48
    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_r50-d8_512x1024_80k_cityscapes.py



  - Name: gcnet_r101-d8_512x1024_80k_cityscapes
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 383.14
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 79.03
    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_r101-d8_512x1024_80k_cityscapes.py



  - Name: gcnet_r50-d8_769x769_80k_cityscapes
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 598.8
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 78.68
    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_r50-d8_769x769_80k_cityscapes.py



  - Name: gcnet_r101-d8_769x769_80k_cityscapes
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 884.96
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 79.18
    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_r101-d8_769x769_80k_cityscapes.py



  - Name: gcnet_r50-d8_512x512_80k_ade20k
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 42.77
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 41.47
    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_r50-d8_512x512_80k_ade20k.py



  - Name: gcnet_r101-d8_512x512_80k_ade20k
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 65.79
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 42.82
    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_r101-d8_512x512_80k_ade20k.py



  - Name: gcnet_r50-d8_512x512_160k_ade20k
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 42.77
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 42.37
    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_r50-d8_512x512_160k_ade20k.py



  - Name: gcnet_r101-d8_512x512_160k_ade20k
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 65.79
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 43.69
    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_r101-d8_512x512_160k_ade20k.py



  - Name: gcnet_r50-d8_512x512_20k_voc12aug
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 42.83
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 76.42
    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_r50-d8_512x512_20k_voc12aug.py



  - Name: gcnet_r101-d8_512x512_20k_voc12aug
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 67.57
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 77.41
    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_r101-d8_512x512_20k_voc12aug.py



  - Name: gcnet_r50-d8_512x512_40k_voc12aug
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 42.83
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 76.24
    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_r50-d8_512x512_40k_voc12aug.py



  - Name: gcnet_r101-d8_512x512_40k_voc12aug
    In Collection: GCNet
    Metadata:
      inference time (ms/im):
        - value: 67.57
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 77.84
    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
    Config: configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py