Collections:

- Name: cgnet

  Metadata:

    Training Data:

    - Cityscapes

  Paper:

    URL: https://arxiv.org/pdf/1811.08201.pdf

    Title: 'CGNet: A Light-weight Context Guided Network for Semantic Segmentation'

  README: configs/cgnet/README.md

  Code:

    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/cgnet.py#L187

    Version: v0.17.0

  Converted From:

    Code: https://github.com/wutianyiRosun/CGNet

Models:

- Name: cgnet_680x680_60k_cityscapes

  In Collection: cgnet

  Metadata:

    backbone: M3N21

    crop size: (680,680)

    lr schd: 60000

    inference time (ms/im):

    - value: 32.78

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (680,680)

    Training Memory (GB): 7.5

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 65.63

      mIoU(ms+flip): 68.04

  Config: configs/cgnet/cgnet_680x680_60k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth

- Name: cgnet_512x1024_60k_cityscapes

  In Collection: cgnet

  Metadata:

    backbone: M3N21

    crop size: (512,1024)

    lr schd: 60000

    inference time (ms/im):

    - value: 32.11

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,1024)

    Training Memory (GB): 8.3

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 68.27

      mIoU(ms+flip): 70.33

  Config: configs/cgnet/cgnet_512x1024_60k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth