Collections:
- Metadata:
    Training Data:
    - Cityscapes
    - ADE20K
  Name: point_rend
Models:
- Config: configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py
  In Collection: point_rend
  Metadata:
    backbone: R-50
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 117.92
    lr schd: 80000
    memory (GB): 3.1
  Name: pointrend_r50_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 76.47
      mIoU(ms+flip): 78.13
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth
- Config: configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py
  In Collection: point_rend
  Metadata:
    backbone: R-101
    crop size: (512,1024)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,1024)
      value: 142.86
    lr schd: 80000
    memory (GB): 4.2
  Name: pointrend_r101_512x1024_80k_cityscapes
  Results:
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.3
      mIoU(ms+flip): 79.97
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth
- Config: configs/point_rend/pointrend_r50_512x512_160k_ade20k.py
  In Collection: point_rend
  Metadata:
    backbone: R-50
    crop size: (512,512)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,512)
      value: 57.77
    lr schd: 160000
    memory (GB): 5.1
  Name: pointrend_r50_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 37.64
      mIoU(ms+flip): 39.17
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth
- Config: configs/point_rend/pointrend_r101_512x512_160k_ade20k.py
  In Collection: point_rend
  Metadata:
    backbone: R-101
    crop size: (512,512)
    inference time (ms/im):
    - backend: PyTorch
      batch size: 1
      hardware: V100
      mode: FP32
      resolution: (512,512)
      value: 64.52
    lr schd: 160000
    memory (GB): 6.1
  Name: pointrend_r101_512x512_160k_ade20k
  Results:
    Dataset: ADE20K
    Metrics:
      mIoU: 40.02
      mIoU(ms+flip): 41.6
    Task: Semantic Segmentation
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth