Collections:

- Name: erfnet

  Metadata:

    Training Data:

    - Cityscapes

  Paper:

    URL: http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf

    Title: 'ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation'

  README: configs/erfnet/README.md

  Code:

    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/erfnet.py#L321

    Version: v0.20.0

  Converted From:

    Code: https://github.com/Eromera/erfnet_pytorch

Models:

- Name: erfnet_fcn_4x4_512x1024_160k_cityscapes

  In Collection: erfnet

  Metadata:

    backbone: ERFNet

    crop size: (512,1024)

    lr schd: 160000

    inference time (ms/im):

    - value: 65.53

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,1024)

    Training Memory (GB): 6.04

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 71.08

      mIoU(ms+flip): 72.6

  Config: configs/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20211126_082056-03d333ed.pth