Collections:

- Name: danet

  Metadata:

    Training Data:

    - Cityscapes

    - ADE20K

    - Pascal VOC 2012 + Aug

  Paper:

    URL: https://arxiv.org/abs/1809.02983

    Title: Dual Attention Network for Scene Segmentation

  README: configs/danet/README.md

  Code:

    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76

    Version: v0.17.0

  Converted From:

    Code: https://github.com/junfu1115/DANet/

Models:

- Name: danet_r50-d8_512x1024_40k_cityscapes

  In Collection: danet

  Metadata:

    backbone: R-50-D8

    crop size: (512,1024)

    lr schd: 40000

    inference time (ms/im):

    - value: 375.94

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,1024)

    Training Memory (GB): 7.4

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 78.74

  Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py

  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

- Name: danet_r101-d8_512x1024_40k_cityscapes

  In Collection: danet

  Metadata:

    backbone: R-101-D8

    crop size: (512,1024)

    lr schd: 40000

    inference time (ms/im):

    - value: 502.51

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,1024)

    Training Memory (GB): 10.9

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 80.52

  Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py

  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

- Name: danet_r50-d8_769x769_40k_cityscapes

  In Collection: danet

  Metadata:

    backbone: R-50-D8

    crop size: (769,769)

    lr schd: 40000

    inference time (ms/im):

    - value: 641.03

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (769,769)

    Training Memory (GB): 8.8

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 78.88

      mIoU(ms+flip): 80.62

  Config: configs/danet/danet_r50-d8_769x769_40k_cityscapes.py

  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

- Name: danet_r101-d8_769x769_40k_cityscapes

  In Collection: danet

  Metadata:

    backbone: R-101-D8

    crop size: (769,769)

    lr schd: 40000

    inference time (ms/im):

    - value: 934.58

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (769,769)

    Training Memory (GB): 12.8

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 79.88

      mIoU(ms+flip): 81.47

  Config: configs/danet/danet_r101-d8_769x769_40k_cityscapes.py

  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

- Name: danet_r50-d8_512x1024_80k_cityscapes

  In Collection: danet

  Metadata:

    backbone: R-50-D8

    crop size: (512,1024)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 79.34

  Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py

  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

- Name: danet_r101-d8_512x1024_80k_cityscapes

  In Collection: danet

  Metadata:

    backbone: R-101-D8

    crop size: (512,1024)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 80.41

  Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py

  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

- Name: danet_r50-d8_769x769_80k_cityscapes

  In Collection: danet

  Metadata:

    backbone: R-50-D8

    crop size: (769,769)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 79.27

      mIoU(ms+flip): 80.96

  Config: configs/danet/danet_r50-d8_769x769_80k_cityscapes.py

  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

- Name: danet_r101-d8_769x769_80k_cityscapes

  In Collection: danet

  Metadata:

    backbone: R-101-D8

    crop size: (769,769)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 80.47

      mIoU(ms+flip): 82.02

  Config: configs/danet/danet_r101-d8_769x769_80k_cityscapes.py

  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

- Name: danet_r50-d8_512x512_80k_ade20k

  In Collection: danet

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 80000

    inference time (ms/im):

    - value: 47.17

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 11.5

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 41.66

      mIoU(ms+flip): 42.9

  Config: configs/danet/danet_r50-d8_512x512_80k_ade20k.py

  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

- Name: danet_r101-d8_512x512_80k_ade20k

  In Collection: danet

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 80000

    inference time (ms/im):

    - value: 70.52

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 15.0

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 43.64

      mIoU(ms+flip): 45.19

  Config: configs/danet/danet_r101-d8_512x512_80k_ade20k.py

  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

- Name: danet_r50-d8_512x512_160k_ade20k

  In Collection: danet

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 160000

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 42.45

      mIoU(ms+flip): 43.25

  Config: configs/danet/danet_r50-d8_512x512_160k_ade20k.py

  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

- Name: danet_r101-d8_512x512_160k_ade20k

  In Collection: danet

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 160000

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 44.17

      mIoU(ms+flip): 45.02

  Config: configs/danet/danet_r101-d8_512x512_160k_ade20k.py

  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

- Name: danet_r50-d8_512x512_20k_voc12aug

  In Collection: danet

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 20000

    inference time (ms/im):

    - value: 47.76

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 6.5

  Results:

  - Task: Semantic Segmentation

    Dataset: Pascal VOC 2012 + Aug

    Metrics:

      mIoU: 74.45

      mIoU(ms+flip): 75.69

  Config: configs/danet/danet_r50-d8_512x512_20k_voc12aug.py

  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

- Name: danet_r101-d8_512x512_20k_voc12aug

  In Collection: danet

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 20000

    inference time (ms/im):

    - value: 72.67

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 9.9

  Results:

  - Task: Semantic Segmentation

    Dataset: Pascal VOC 2012 + Aug

    Metrics:

      mIoU: 76.02

      mIoU(ms+flip): 77.23

  Config: configs/danet/danet_r101-d8_512x512_20k_voc12aug.py

  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

- Name: danet_r50-d8_512x512_40k_voc12aug

  In Collection: danet

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 40000

  Results:

  - Task: Semantic Segmentation

    Dataset: Pascal VOC 2012 + Aug

    Metrics:

      mIoU: 76.37

      mIoU(ms+flip): 77.29

  Config: configs/danet/danet_r50-d8_512x512_40k_voc12aug.py

  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

- Name: danet_r101-d8_512x512_40k_voc12aug

  In Collection: danet

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 40000

  Results:

  - Task: Semantic Segmentation

    Dataset: Pascal VOC 2012 + Aug

    Metrics:

      mIoU: 76.51

      mIoU(ms+flip): 77.32

  Config: configs/danet/danet_r101-d8_512x512_40k_voc12aug.py

  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