mmsegmentation/configs/danet/metafile.yml

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

Models:

  - Name: danet_r50-d8_512x1024_40k_cityscapes
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 375.94
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 78.74
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth
    Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py



  - Name: danet_r101-d8_512x1024_40k_cityscapes
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 502.51
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 80.52
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth
    Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py



  - Name: danet_r50-d8_769x769_40k_cityscapes
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 641.03
          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/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth
    Config: configs/danet/danet_r50-d8_769x769_40k_cityscapes.py



  - Name: danet_r101-d8_769x769_40k_cityscapes
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 934.58
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 79.88
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth
    Config: configs/danet/danet_r101-d8_769x769_40k_cityscapes.py



  - Name: danet_r50-d8_512x1024_80k_cityscapes
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 375.94
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 79.34
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth
    Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py



  - Name: danet_r101-d8_512x1024_80k_cityscapes
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 502.51
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 80.41
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth
    Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py



  - Name: danet_r50-d8_769x769_80k_cityscapes
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 641.03
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 79.27
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth
    Config: configs/danet/danet_r50-d8_769x769_80k_cityscapes.py



  - Name: danet_r101-d8_769x769_80k_cityscapes
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 934.58
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 80.47
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth
    Config: configs/danet/danet_r101-d8_769x769_80k_cityscapes.py



  - Name: danet_r50-d8_512x512_80k_ade20k
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 47.17
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 41.66
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth
    Config: configs/danet/danet_r50-d8_512x512_80k_ade20k.py



  - Name: danet_r101-d8_512x512_80k_ade20k
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 70.52
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 43.64
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth
    Config: configs/danet/danet_r101-d8_512x512_80k_ade20k.py



  - Name: danet_r50-d8_512x512_160k_ade20k
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 47.17
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 42.45
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth
    Config: configs/danet/danet_r50-d8_512x512_160k_ade20k.py



  - Name: danet_r101-d8_512x512_160k_ade20k
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 70.52
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 44.17
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth
    Config: configs/danet/danet_r101-d8_512x512_160k_ade20k.py



  - Name: danet_r50-d8_512x512_20k_voc12aug
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 47.76
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 74.45
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth
    Config: configs/danet/danet_r50-d8_512x512_20k_voc12aug.py



  - Name: danet_r101-d8_512x512_20k_voc12aug
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 72.67
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 76.02
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth
    Config: configs/danet/danet_r101-d8_512x512_20k_voc12aug.py



  - Name: danet_r50-d8_512x512_40k_voc12aug
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 47.76
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 76.37
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth
    Config: configs/danet/danet_r50-d8_512x512_40k_voc12aug.py



  - Name: danet_r101-d8_512x512_40k_voc12aug
    In Collection: DANet
    Metadata:
      inference time (ms/im):
        - value: 72.67
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Pascal VOC 2012 + Aug
        Metrics:
          mIoU: 76.51
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth
    Config: configs/danet/danet_r101-d8_512x512_40k_voc12aug.py