mmsegmentation/configs/ann/metafile.yml

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

Models:

  - Name: ann_r50-d8_512x1024_40k_cityscapes
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 269.54
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 77.40
    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
    Config: configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py



  - Name: ann_r101-d8_512x1024_40k_cityscapes
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 392.16
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 76.55
    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
    Config: configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py



  - Name: ann_r50-d8_769x769_40k_cityscapes
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 588.24
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 78.89
    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
    Config: configs/ann/ann_r50-d8_769x769_40k_cityscapes.py



  - Name: ann_r101-d8_769x769_40k_cityscapes
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 869.57
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 79.32
    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
    Config: configs/ann/ann_r101-d8_769x769_40k_cityscapes.py



  - Name: ann_r50-d8_512x1024_80k_cityscapes
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 269.54
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 77.34
    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
    Config: configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py



  - Name: ann_r101-d8_512x1024_80k_cityscapes
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 392.16
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 77.14
    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
    Config: configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py



  - Name: ann_r50-d8_769x769_80k_cityscapes
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 588.24
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 78.88
    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
    Config: configs/ann/ann_r50-d8_769x769_80k_cityscapes.py



  - Name: ann_r101-d8_769x769_80k_cityscapes
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 869.57
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 78.80
    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
    Config: configs/ann/ann_r101-d8_769x769_80k_cityscapes.py



  - Name: ann_r50-d8_512x512_80k_ade20k
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 47.6
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 41.01
    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
    Config: configs/ann/ann_r50-d8_512x512_80k_ade20k.py



  - Name: ann_r101-d8_512x512_80k_ade20k
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 70.82
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 42.94
    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
    Config: configs/ann/ann_r101-d8_512x512_80k_ade20k.py



  - Name: ann_r50-d8_512x512_160k_ade20k
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 47.6
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 41.74
    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
    Config: configs/ann/ann_r50-d8_512x512_160k_ade20k.py



  - Name: ann_r101-d8_512x512_160k_ade20k
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 70.82
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 42.94
    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
    Config: configs/ann/ann_r101-d8_512x512_160k_ade20k.py



  - Name: ann_r50-d8_512x512_20k_voc12aug
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 47.8
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 74.86
    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
    Config: configs/ann/ann_r50-d8_512x512_20k_voc12aug.py



  - Name: ann_r101-d8_512x512_20k_voc12aug
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 71.74
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 77.47
    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
    Config: configs/ann/ann_r101-d8_512x512_20k_voc12aug.py



  - Name: ann_r50-d8_512x512_40k_voc12aug
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 47.8
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 76.56
    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
    Config: configs/ann/ann_r50-d8_512x512_40k_voc12aug.py



  - Name: ann_r101-d8_512x512_40k_voc12aug
    In Collection: ANN
    Metadata:
      inference time (ms/im):
        - value: 71.74
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 76.70
    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
    Config: configs/ann/ann_r101-d8_512x512_40k_voc12aug.py