Collections:

- Name: apcnet

  Metadata:

    Training Data:

    - Cityscapes

    - ADE20K

  Paper:

    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html

    Title: Adaptive Pyramid Context Network for Semantic Segmentation

  README: configs/apcnet/README.md

  Code:

    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111

    Version: v0.17.0

  Converted From:

    Code: https://github.com/Junjun2016/APCNet

Models:

- Name: apcnet_r50-d8_512x1024_40k_cityscapes

  In Collection: apcnet

  Metadata:

    backbone: R-50-D8

    crop size: (512,1024)

    lr schd: 40000

    inference time (ms/im):

    - value: 280.11

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,1024)

    Training Memory (GB): 7.7

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 78.02

      mIoU(ms+flip): 79.26

  Config: configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth

- Name: apcnet_r101-d8_512x1024_40k_cityscapes

  In Collection: apcnet

  Metadata:

    backbone: R-101-D8

    crop size: (512,1024)

    lr schd: 40000

    inference time (ms/im):

    - value: 465.12

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,1024)

    Training Memory (GB): 11.2

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 79.08

      mIoU(ms+flip): 80.34

  Config: configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth

- Name: apcnet_r50-d8_769x769_40k_cityscapes

  In Collection: apcnet

  Metadata:

    backbone: R-50-D8

    crop size: (769,769)

    lr schd: 40000

    inference time (ms/im):

    - value: 657.89

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (769,769)

    Training Memory (GB): 8.7

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 77.89

      mIoU(ms+flip): 79.75

  Config: configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth

- Name: apcnet_r101-d8_769x769_40k_cityscapes

  In Collection: apcnet

  Metadata:

    backbone: R-101-D8

    crop size: (769,769)

    lr schd: 40000

    inference time (ms/im):

    - value: 970.87

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (769,769)

    Training Memory (GB): 12.7

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 77.96

      mIoU(ms+flip): 79.24

  Config: configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth

- Name: apcnet_r50-d8_512x1024_80k_cityscapes

  In Collection: apcnet

  Metadata:

    backbone: R-50-D8

    crop size: (512,1024)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 78.96

      mIoU(ms+flip): 79.94

  Config: configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth

- Name: apcnet_r101-d8_512x1024_80k_cityscapes

  In Collection: apcnet

  Metadata:

    backbone: R-101-D8

    crop size: (512,1024)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 79.64

      mIoU(ms+flip): 80.61

  Config: configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth

- Name: apcnet_r50-d8_769x769_80k_cityscapes

  In Collection: apcnet

  Metadata:

    backbone: R-50-D8

    crop size: (769,769)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 78.79

      mIoU(ms+flip): 80.35

  Config: configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth

- Name: apcnet_r101-d8_769x769_80k_cityscapes

  In Collection: apcnet

  Metadata:

    backbone: R-101-D8

    crop size: (769,769)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 78.45

      mIoU(ms+flip): 79.91

  Config: configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth

- Name: apcnet_r50-d8_512x512_80k_ade20k

  In Collection: apcnet

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 80000

    inference time (ms/im):

    - value: 50.99

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 10.1

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 42.2

      mIoU(ms+flip): 43.3

  Config: configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth

- Name: apcnet_r101-d8_512x512_80k_ade20k

  In Collection: apcnet

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 80000

    inference time (ms/im):

    - value: 76.34

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 13.6

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 45.54

      mIoU(ms+flip): 46.65

  Config: configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth

- Name: apcnet_r50-d8_512x512_160k_ade20k

  In Collection: apcnet

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 160000

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 43.4

      mIoU(ms+flip): 43.94

  Config: configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth

- Name: apcnet_r101-d8_512x512_160k_ade20k

  In Collection: apcnet

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 160000

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 45.41

      mIoU(ms+flip): 46.63

  Config: configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth