Collections:
- Name: emanet
  Metadata:
    Training Data:
    - Cityscapes
  Paper:
    URL: https://arxiv.org/abs/1907.13426
    Title: Expectation-Maximization Attention Networks for Semantic Segmentation
  README: configs/emanet/README.md
  Code:
    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80
    Version: v0.17.0
  Converted From:
    Code: https://xialipku.github.io/EMANet
Models:
- Name: emanet_r50-d8_512x1024_80k_cityscapes
  In Collection: emanet
  Metadata:
    backbone: R-50-D8
    crop size: (512,1024)
    lr schd: 80000
    inference time (ms/im):
    - value: 218.34
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,1024)
    Training Memory (GB): 5.4
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.59
      mIoU(ms+flip): 79.44
  Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth
- Name: emanet_r101-d8_512x1024_80k_cityscapes
  In Collection: emanet
  Metadata:
    backbone: R-101-D8
    crop size: (512,1024)
    lr schd: 80000
    inference time (ms/im):
    - value: 348.43
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,1024)
    Training Memory (GB): 6.2
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.1
      mIoU(ms+flip): 81.21
  Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth
- Name: emanet_r50-d8_769x769_80k_cityscapes
  In Collection: emanet
  Metadata:
    backbone: R-50-D8
    crop size: (769,769)
    lr schd: 80000
    inference time (ms/im):
    - value: 507.61
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (769,769)
    Training Memory (GB): 8.9
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.33
      mIoU(ms+flip): 80.49
  Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth
- Name: emanet_r101-d8_769x769_80k_cityscapes
  In Collection: emanet
  Metadata:
    backbone: R-101-D8
    crop size: (769,769)
    lr schd: 80000
    inference time (ms/im):
    - value: 819.67
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (769,769)
    Training Memory (GB): 10.1
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.62
      mIoU(ms+flip): 81.0
  Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth
