mmsegmentation/configs/ccnet/ccnet.yml

Collections:
- Metadata:
    Training Data:
    - Cityscapes
    - ADE20K
    - Pascal VOC 2012 + Aug
  Name: ccnet
Models:
- Config: configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py
  In Collection: ccnet
  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: 301.2
    lr schd: 40000
    memory (GB): 6.0
  Name: ccnet_r50-d8_512x1024_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.76
      mIoU(ms+flip): 78.87
    Task: Semantic Segmentation
  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_r101-d8_512x1024_40k_cityscapes.py
  In Collection: ccnet
  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: 432.9
    lr schd: 40000
    memory (GB): 9.5
  Name: ccnet_r101-d8_512x1024_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 76.35
      mIoU(ms+flip): 78.19
    Task: Semantic Segmentation
  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_r50-d8_769x769_40k_cityscapes.py
  In Collection: ccnet
  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: 699.3
    lr schd: 40000
    memory (GB): 6.8
  Name: ccnet_r50-d8_769x769_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.46
      mIoU(ms+flip): 79.93
    Task: Semantic Segmentation
  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_r101-d8_769x769_40k_cityscapes.py
  In Collection: ccnet
  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: 990.1
    lr schd: 40000
    memory (GB): 10.7
  Name: ccnet_r101-d8_769x769_40k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 76.94
      mIoU(ms+flip): 78.62
    Task: Semantic Segmentation
  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_r50-d8_512x1024_80k_cityscapes.py
  In Collection: ccnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,1024)
    lr schd: 80000
  Name: ccnet_r50-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.03
      mIoU(ms+flip): 80.16
    Task: Semantic Segmentation
  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_r101-d8_512x1024_80k_cityscapes.py
  In Collection: ccnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,1024)
    lr schd: 80000
  Name: ccnet_r101-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.87
      mIoU(ms+flip): 79.9
    Task: Semantic Segmentation
  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_r50-d8_769x769_80k_cityscapes.py
  In Collection: ccnet
  Metadata:
    backbone: R-50-D8
    crop size: (769,769)
    lr schd: 80000
  Name: ccnet_r50-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.29
      mIoU(ms+flip): 81.08
    Task: Semantic Segmentation
  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_r101-d8_769x769_80k_cityscapes.py
  In Collection: ccnet
  Metadata:
    backbone: R-101-D8
    crop size: (769,769)
    lr schd: 80000
  Name: ccnet_r101-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.45
      mIoU(ms+flip): 80.66
    Task: Semantic Segmentation
  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_r50-d8_512x512_80k_ade20k.py
  In Collection: ccnet
  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: 47.87
    lr schd: 80000
    memory (GB): 8.8
  Name: ccnet_r50-d8_512x512_80k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 41.78
      mIoU(ms+flip): 42.98
    Task: Semantic Segmentation
  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_r101-d8_512x512_80k_ade20k.py
  In Collection: ccnet
  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: 70.87
    lr schd: 80000
    memory (GB): 12.2
  Name: ccnet_r101-d8_512x512_80k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 43.97
      mIoU(ms+flip): 45.13
    Task: Semantic Segmentation
  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_r50-d8_512x512_160k_ade20k.py
  In Collection: ccnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 160000
  Name: ccnet_r50-d8_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 42.08
      mIoU(ms+flip): 43.13
    Task: Semantic Segmentation
  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_r101-d8_512x512_160k_ade20k.py
  In Collection: ccnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 160000
  Name: ccnet_r101-d8_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 43.71
      mIoU(ms+flip): 45.04
    Task: Semantic Segmentation
  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_r50-d8_512x512_20k_voc12aug.py
  In Collection: ccnet
  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: 48.9
    lr schd: 20000
    memory (GB): 6.0
  Name: ccnet_r50-d8_512x512_20k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 76.17
      mIoU(ms+flip): 77.51
    Task: Semantic Segmentation
  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_r101-d8_512x512_20k_voc12aug.py
  In Collection: ccnet
  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: 73.31
    lr schd: 20000
    memory (GB): 9.5
  Name: ccnet_r101-d8_512x512_20k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 77.27
      mIoU(ms+flip): 79.02
    Task: Semantic Segmentation
  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_r50-d8_512x512_40k_voc12aug.py
  In Collection: ccnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 40000
  Name: ccnet_r50-d8_512x512_40k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 75.96
      mIoU(ms+flip): 77.04
    Task: Semantic Segmentation
  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_r101-d8_512x512_40k_voc12aug.py
  In Collection: ccnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 40000
  Name: ccnet_r101-d8_512x512_40k_voc12aug
  Results:
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 77.87
      mIoU(ms+flip): 78.9
    Task: Semantic Segmentation
  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