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

Models:

  - Name: ccnet_r50-d8_512x1024_40k_cityscapes
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 301.2
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 77.76
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth
    Config: configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py



  - Name: ccnet_r101-d8_512x1024_40k_cityscapes
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 432.9
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 76.35
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth
    Config: configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py



  - Name: ccnet_r50-d8_769x769_40k_cityscapes
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 699.3
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 78.46
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth
    Config: configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py



  - Name: ccnet_r101-d8_769x769_40k_cityscapes
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 990.1
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 76.94
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth
    Config: configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py



  - Name: ccnet_r50-d8_512x1024_80k_cityscapes
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 301.2
          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/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth
    Config: configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py



  - Name: ccnet_r101-d8_512x1024_80k_cityscapes
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 432.9
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 78.87
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth
    Config: configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py



  - Name: ccnet_r50-d8_769x769_80k_cityscapes
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 699.3
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 79.29
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth
    Config: configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py



  - Name: ccnet_r101-d8_769x769_80k_cityscapes
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 990.1
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 79.45
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth
    Config: configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py



  - Name: ccnet_r50-d8_512x512_80k_ade20k
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 47.87
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 41.78
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth
    Config: configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py



  - Name: ccnet_r101-d8_512x512_80k_ade20k
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 70.87
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 43.97
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth
    Config: configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py



  - Name: ccnet_r50-d8_512x512_160k_ade20k
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 47.87
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 42.08
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth
    Config: configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py



  - Name: ccnet_r101-d8_512x512_160k_ade20k
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 70.87
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 43.71
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth
    Config: configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py



  - Name: ccnet_r50-d8_512x512_20k_voc12aug
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 48.9
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 76.17
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth
    Config: configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py



  - Name: ccnet_r101-d8_512x512_20k_voc12aug
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 73.31
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 77.27
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth
    Config: configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py



  - Name: ccnet_r50-d8_512x512_40k_voc12aug
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 48.9
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 75.96
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth
    Config: configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py



  - Name: ccnet_r101-d8_512x512_40k_voc12aug
    In Collection: CCNet
    Metadata:
      inference time (ms/im):
        - value: 73.31
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 77.87
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth
    Config: configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py