Collections:
- Name: ann
  Metadata:
    Training Data:
    - Cityscapes
    - ADE20K
    - Pascal VOC 2012 + Aug
  Paper:
    URL: https://arxiv.org/abs/1908.07678
    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
  README: configs/ann/README.md
  Code:
    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
    Version: v0.17.0
  Converted From:
    Code: https://github.com/MendelXu/ANN
Models:
- Name: ann_r50-d8_512x1024_40k_cityscapes
  In Collection: ann
  Metadata:
    backbone: R-50-D8
    crop size: (512,1024)
    lr schd: 40000
    inference time (ms/im):
    - value: 269.54
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,1024)
    Training Memory (GB): 6.0
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.4
      mIoU(ms+flip): 78.57
  Config: configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth
- Name: ann_r101-d8_512x1024_40k_cityscapes
  In Collection: ann
  Metadata:
    backbone: R-101-D8
    crop size: (512,1024)
    lr schd: 40000
    inference time (ms/im):
    - value: 392.16
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,1024)
    Training Memory (GB): 9.5
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 76.55
      mIoU(ms+flip): 78.85
  Config: configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth
- Name: ann_r50-d8_769x769_40k_cityscapes
  In Collection: ann
  Metadata:
    backbone: R-50-D8
    crop size: (769,769)
    lr schd: 40000
    inference time (ms/im):
    - value: 588.24
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (769,769)
    Training Memory (GB): 6.8
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.89
      mIoU(ms+flip): 80.46
  Config: configs/ann/ann_r50-d8_769x769_40k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth
- Name: ann_r101-d8_769x769_40k_cityscapes
  In Collection: ann
  Metadata:
    backbone: R-101-D8
    crop size: (769,769)
    lr schd: 40000
    inference time (ms/im):
    - value: 869.57
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (769,769)
    Training Memory (GB): 10.7
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.32
      mIoU(ms+flip): 80.94
  Config: configs/ann/ann_r101-d8_769x769_40k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth
- Name: ann_r50-d8_512x1024_80k_cityscapes
  In Collection: ann
  Metadata:
    backbone: R-50-D8
    crop size: (512,1024)
    lr schd: 80000
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.34
      mIoU(ms+flip): 78.65
  Config: configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth
- Name: ann_r101-d8_512x1024_80k_cityscapes
  In Collection: ann
  Metadata:
    backbone: R-101-D8
    crop size: (512,1024)
    lr schd: 80000
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.14
      mIoU(ms+flip): 78.81
  Config: configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth
- Name: ann_r50-d8_769x769_80k_cityscapes
  In Collection: ann
  Metadata:
    backbone: R-50-D8
    crop size: (769,769)
    lr schd: 80000
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.88
      mIoU(ms+flip): 80.57
  Config: configs/ann/ann_r50-d8_769x769_80k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth
- Name: ann_r101-d8_769x769_80k_cityscapes
  In Collection: ann
  Metadata:
    backbone: R-101-D8
    crop size: (769,769)
    lr schd: 80000
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.8
      mIoU(ms+flip): 80.34
  Config: configs/ann/ann_r101-d8_769x769_80k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth
- Name: ann_r50-d8_512x512_80k_ade20k
  In Collection: ann
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 80000
    inference time (ms/im):
    - value: 47.6
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 9.1
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 41.01
      mIoU(ms+flip): 42.3
  Config: configs/ann/ann_r50-d8_512x512_80k_ade20k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth
- Name: ann_r101-d8_512x512_80k_ade20k
  In Collection: ann
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 80000
    inference time (ms/im):
    - value: 70.82
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 12.5
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 42.94
      mIoU(ms+flip): 44.18
  Config: configs/ann/ann_r101-d8_512x512_80k_ade20k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth
- Name: ann_r50-d8_512x512_160k_ade20k
  In Collection: ann
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 160000
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 41.74
      mIoU(ms+flip): 42.62
  Config: configs/ann/ann_r50-d8_512x512_160k_ade20k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth
- Name: ann_r101-d8_512x512_160k_ade20k
  In Collection: ann
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 160000
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 42.94
      mIoU(ms+flip): 44.06
  Config: configs/ann/ann_r101-d8_512x512_160k_ade20k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth
- Name: ann_r50-d8_512x512_20k_voc12aug
  In Collection: ann
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 20000
    inference time (ms/im):
    - value: 47.8
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 6.0
  Results:
  - Task: Semantic Segmentation
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 74.86
      mIoU(ms+flip): 76.13
  Config: configs/ann/ann_r50-d8_512x512_20k_voc12aug.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth
- Name: ann_r101-d8_512x512_20k_voc12aug
  In Collection: ann
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 20000
    inference time (ms/im):
    - value: 71.74
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 9.5
  Results:
  - Task: Semantic Segmentation
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 77.47
      mIoU(ms+flip): 78.7
  Config: configs/ann/ann_r101-d8_512x512_20k_voc12aug.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth
- Name: ann_r50-d8_512x512_40k_voc12aug
  In Collection: ann
  Metadata:
    backbone: R-50-D8
    crop size: (512,512)
    lr schd: 40000
  Results:
  - Task: Semantic Segmentation
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 76.56
      mIoU(ms+flip): 77.51
  Config: configs/ann/ann_r50-d8_512x512_40k_voc12aug.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth
- Name: ann_r101-d8_512x512_40k_voc12aug
  In Collection: ann
  Metadata:
    backbone: R-101-D8
    crop size: (512,512)
    lr schd: 40000
  Results:
  - Task: Semantic Segmentation
    Dataset: Pascal VOC 2012 + Aug
    Metrics:
      mIoU: 76.7
      mIoU(ms+flip): 78.06
  Config: configs/ann/ann_r101-d8_512x512_40k_voc12aug.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth
