Collections:
- Name: gcnet
  Metadata:
    Training Data:
    - Cityscapes
    - ADE20K
    - Pascal VOC 2012 + Aug
  Paper:
    URL: https://arxiv.org/abs/1904.11492
    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
  README: configs/gcnet/README.md
  Code:
    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
    Version: v0.17.0
  Converted From:
    Code: https://github.com/xvjiarui/GCNet
Models:
- Name: gcnet_r50-d8_512x1024_40k_cityscapes
  In Collection: gcnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,1024)
    lr schd: 40000
    inference time (ms/im):
    - value: 254.45
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,1024)
    Training Memory (GB): 5.8
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.69
      mIoU(ms+flip): 78.56
  Config: configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py
  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
- Name: gcnet_r101-d8_512x1024_40k_cityscapes
  In Collection: gcnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,1024)
    lr schd: 40000
    inference time (ms/im):
    - value: 383.14
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,1024)
    Training Memory (GB): 9.2
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.28
      mIoU(ms+flip): 79.34
  Config: configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py
  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
- Name: gcnet_r50-d8_769x769_40k_cityscapes
  In Collection: gcnet
  Metadata:
    backbone: R-50-D8
    crop size: (769,769)
    lr schd: 40000
    inference time (ms/im):
    - value: 598.8
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (769,769)
    Training Memory (GB): 6.5
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.12
      mIoU(ms+flip): 80.09
  Config: configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py
  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
- Name: gcnet_r101-d8_769x769_40k_cityscapes
  In Collection: gcnet
  Metadata:
    backbone: R-101-D8
    crop size: (769,769)
    lr schd: 40000
    inference time (ms/im):
    - value: 884.96
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (769,769)
    Training Memory (GB): 10.5
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.95
      mIoU(ms+flip): 80.71
  Config: configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py
  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
- Name: gcnet_r50-d8_512x1024_80k_cityscapes
  In Collection: gcnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,1024)
    lr schd: 80000
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.48
      mIoU(ms+flip): 80.01
  Config: configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py
  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
- Name: gcnet_r101-d8_512x1024_80k_cityscapes
  In Collection: gcnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,1024)
    lr schd: 80000
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.03
      mIoU(ms+flip): 79.84
  Config: configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py
  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
- Name: gcnet_r50-d8_769x769_80k_cityscapes
  In Collection: gcnet
  Metadata:
    backbone: R-50-D8
    crop size: (769,769)
    lr schd: 80000
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.68
      mIoU(ms+flip): 80.66
  Config: configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py
  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
- Name: gcnet_r101-d8_769x769_80k_cityscapes
  In Collection: gcnet
  Metadata:
    backbone: R-101-D8
    crop size: (769,769)
    lr schd: 80000
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.18
      mIoU(ms+flip): 80.71
  Config: configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py
  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
- Name: gcnet_r50-d8_512x512_80k_ade20k
  In Collection: gcnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 80000
    inference time (ms/im):
    - value: 42.77
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 8.5
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 41.47
      mIoU(ms+flip): 42.85
  Config: configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
  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
- Name: gcnet_r101-d8_512x512_80k_ade20k
  In Collection: gcnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 80000
    inference time (ms/im):
    - value: 65.79
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 12.0
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 42.82
      mIoU(ms+flip): 44.54
  Config: configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py
  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
- Name: gcnet_r50-d8_512x512_160k_ade20k
  In Collection: gcnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 160000
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 42.37
      mIoU(ms+flip): 43.52
  Config: configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py
  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
- Name: gcnet_r101-d8_512x512_160k_ade20k
  In Collection: gcnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 160000
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 43.69
      mIoU(ms+flip): 45.21
  Config: configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py
  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
- Name: gcnet_r50-d8_512x512_20k_voc12aug
  In Collection: gcnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 20000
    inference time (ms/im):
    - value: 42.83
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 5.8
  Results:
  - Task: Semantic Segmentation
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 76.42
      mIoU(ms+flip): 77.51
  Config: configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py
  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
- Name: gcnet_r101-d8_512x512_20k_voc12aug
  In Collection: gcnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 20000
    inference time (ms/im):
    - value: 67.57
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 9.2
  Results:
  - Task: Semantic Segmentation
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 77.41
      mIoU(ms+flip): 78.56
  Config: configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py
  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
- Name: gcnet_r50-d8_512x512_40k_voc12aug
  In Collection: gcnet
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 40000
  Results:
  - Task: Semantic Segmentation
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 76.24
      mIoU(ms+flip): 77.63
  Config: configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py
  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
- Name: gcnet_r101-d8_512x512_40k_voc12aug
  In Collection: gcnet
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 40000
  Results:
  - Task: Semantic Segmentation
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 77.84
      mIoU(ms+flip): 78.59
  Config: configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py
  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
