Collections:
- Metadata:
    Training Data:
    - Cityscapes
  Name: emanet
Models:
- Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py
  In Collection: emanet
  Metadata:
    backbone: R-50-D8
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 218.34
    lr schd: 80000
    memory (GB): 5.4
  Name: emanet_r50-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.59
      mIoU(ms+flip): 79.44
    Task: Semantic Segmentation
  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
- Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py
  In Collection: emanet
  Metadata:
    backbone: R-101-D8
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 348.43
    lr schd: 80000
    memory (GB): 6.2
  Name: emanet_r101-d8_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.1
      mIoU(ms+flip): 81.21
    Task: Semantic Segmentation
  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
- Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py
  In Collection: emanet
  Metadata:
    backbone: R-50-D8
    crop size: (769,769)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (769,769)
      value: 507.61
    lr schd: 80000
    memory (GB): 8.9
  Name: emanet_r50-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.33
      mIoU(ms+flip): 80.49
    Task: Semantic Segmentation
  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
- Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py
  In Collection: emanet
  Metadata:
    backbone: R-101-D8
    crop size: (769,769)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (769,769)
      value: 819.67
    lr schd: 80000
    memory (GB): 10.1
  Name: emanet_r101-d8_769x769_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.62
      mIoU(ms+flip): 81.0
    Task: Semantic Segmentation
  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