Collections:
- Metadata:
    Training Data:
    - Cityscapes
  Name: bisenetv2
Models:
- Config: configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py
  In Collection: bisenetv2
  Metadata:
    backbone: BiSeNetV2
    crop size: (1024,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (1024,1024)
      value: 31.48
    lr schd: 160000
    memory (GB): 7.64
  Name: bisenetv2_fcn_4x4_1024x1024_160k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 73.21
      mIoU(ms+flip): 75.74
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551-bcf10f09.pth
- Config: configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py
  In Collection: bisenetv2
  Metadata:
    backbone: BiSeNetV2
    crop size: (1024,1024)
    lr schd: 160000
    memory (GB): 7.64
  Name: bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 73.57
      mIoU(ms+flip): 75.8
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947-5f8103b4.pth
- Config: configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py
  In Collection: bisenetv2
  Metadata:
    backbone: BiSeNetV2
    crop size: (1024,1024)
    lr schd: 160000
    memory (GB): 15.05
  Name: bisenetv2_fcn_4x8_1024x1024_160k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 75.76
      mIoU(ms+flip): 77.79
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032-e1a2eed6.pth
- Config: configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py
  In Collection: bisenetv2
  Metadata:
    backbone: BiSeNetV2
    crop size: (1024,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (1024,1024)
      value: 27.29
    lr schd: 160000
    memory (GB): 5.77
  Name: bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 73.07
      mIoU(ms+flip): 75.13
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942-b979777b.pth