Collections:
- Name: BiSeNetV1
  Metadata:
    Training Data:
    - Cityscapes
    - COCO-Stuff 164k
  Paper:
    URL: https://arxiv.org/abs/1808.00897
    Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
  README: configs/bisenetv1/README.md
  Code:
    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
    Version: v0.18.0
  Converted From:
    Code: https://github.com/ycszen/TorchSeg/tree/master/model/bisenet
Models:
- Name: bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes
  In Collection: BiSeNetV1
  Metadata:
    backbone: R-18-D32
    crop size: (1024,1024)
    lr schd: 160000
    inference time (ms/im):
    - value: 31.48
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (1024,1024)
    Training Memory (GB): 5.69
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 74.44
      mIoU(ms+flip): 77.05
  Config: configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239-c55e78e2.pth
- Name: bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes
  In Collection: BiSeNetV1
  Metadata:
    backbone: R-18-D32
    crop size: (1024,1024)
    lr schd: 160000
    inference time (ms/im):
    - value: 31.48
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (1024,1024)
    Training Memory (GB): 5.69
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 74.37
      mIoU(ms+flip): 76.91
  Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251-8ba80eff.pth
- Name: bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes
  In Collection: BiSeNetV1
  Metadata:
    backbone: R-18-D32
    crop size: (1024,1024)
    lr schd: 160000
    inference time (ms/im):
    - value: 31.48
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (1024,1024)
    Training Memory (GB): 11.17
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 75.16
      mIoU(ms+flip): 77.24
  Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322-bb8db75f.pth
- Name: bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes
  In Collection: BiSeNetV1
  Metadata:
    backbone: R-50-D32
    crop size: (1024,1024)
    lr schd: 160000
    inference time (ms/im):
    - value: 129.7
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (1024,1024)
    Training Memory (GB): 15.39
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 76.92
      mIoU(ms+flip): 78.87
  Config: configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639-7b28a2a6.pth
- Name: bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes
  In Collection: BiSeNetV1
  Metadata:
    backbone: R-50-D32
    crop size: (1024,1024)
    lr schd: 160000
    inference time (ms/im):
    - value: 129.7
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (1024,1024)
    Training Memory (GB): 15.39
  Results:
  - Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.68
      mIoU(ms+flip): 79.57
  Config: configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628-8b304447.pth
- Name: bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k
  In Collection: BiSeNetV1
  Metadata:
    backbone: R-18-D32
    crop size: (512,512)
    lr schd: 160000
  Results:
  - Task: Semantic Segmentation
    Dataset: COCO-Stuff 164k
    Metrics:
      mIoU: 25.45
      mIoU(ms+flip): 26.15
  Config: configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328-046aa2f2.pth
- Name: bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k
  In Collection: BiSeNetV1
  Metadata:
    backbone: R-18-D32
    crop size: (512,512)
    lr schd: 160000
    inference time (ms/im):
    - value: 13.47
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 6.33
  Results:
  - Task: Semantic Segmentation
    Dataset: COCO-Stuff 164k
    Metrics:
      mIoU: 28.55
      mIoU(ms+flip): 29.26
  Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth
- Name: bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k
  In Collection: BiSeNetV1
  Metadata:
    backbone: R-50-D32
    crop size: (512,512)
    lr schd: 160000
  Results:
  - Task: Semantic Segmentation
    Dataset: COCO-Stuff 164k
    Metrics:
      mIoU: 29.82
      mIoU(ms+flip): 30.33
  Config: configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616-d2bb0df4.pth
- Name: bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k
  In Collection: BiSeNetV1
  Metadata:
    backbone: R-50-D32
    crop size: (512,512)
    lr schd: 160000
    inference time (ms/im):
    - value: 30.67
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 9.28
  Results:
  - Task: Semantic Segmentation
    Dataset: COCO-Stuff 164k
    Metrics:
      mIoU: 34.88
      mIoU(ms+flip): 35.37
  Config: configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932-66747911.pth
- Name: bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k
  In Collection: BiSeNetV1
  Metadata:
    backbone: R-101-D32
    crop size: (512,512)
    lr schd: 160000
  Results:
  - Task: Semantic Segmentation
    Dataset: COCO-Stuff 164k
    Metrics:
      mIoU: 31.14
      mIoU(ms+flip): 31.76
  Config: configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth
- Name: bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k
  In Collection: BiSeNetV1
  Metadata:
    backbone: R-101-D32
    crop size: (512,512)
    lr schd: 160000
    inference time (ms/im):
    - value: 39.6
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 10.36
  Results:
  - Task: Semantic Segmentation
    Dataset: COCO-Stuff 164k
    Metrics:
      mIoU: 37.38
      mIoU(ms+flip): 37.99
  Config: configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220-28c8f092.pth
