Collections:
- Name: ERFNet
  License: Apache License 2.0
  Metadata:
    Training Data:
    - Cityscapes
  Paper:
    Title: 'ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation'
    URL: http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf
  README: configs/erfnet/README.md
  Frameworks:
  - PyTorch
Models:
- Name: erfnet_fcn_4xb4-160k_cityscapes-512x1024
  In Collection: ERFNet
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 72.5
      mIoU(ms+flip): 74.75
  Config: configs/erfnet/erfnet_fcn_4xb4-160k_cityscapes-512x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 16
    Architecture:
    - ERFNet
    - ERFNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 6.04
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20220704_162145-dc90157a.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20220704_162145.log.json
  Paper:
    Title: 'ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation'
    URL: http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/erfnet.py#L321
  Framework: PyTorch