mmsegmentation/configs/mobilenet_v2/metafile.yml

Models:

  - Name: fcn_m-v2-d8_512x1024_80k_cityscapes
    In Collection: FCN
    Metadata:
      inference time (ms/im):
        - value: 70.42
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 61.54
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth
    Config: configs/fcn/fcn_m-v2-d8_512x1024_80k_cityscapes.py



  - Name: pspnet_m-v2-d8_512x1024_80k_cityscapes
    In Collection: PSPNet
    Metadata:
      inference time (ms/im):
        - value: 89.29
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 70.23
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth
    Config: configs/pspnet/pspnet_m-v2-d8_512x1024_80k_cityscapes.py



  - Name: deeplabv3_m-v2-d8_512x1024_80k_cityscapes
    In Collection: DeepLabV3
    Metadata:
      inference time (ms/im):
        - value: 119.05
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 73.84
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth
    Config: configs/deeplabv3/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py



  - Name: deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes
    In Collection: DeepLabV3+
    Metadata:
      inference time (ms/im):
        - value: 119.05
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: Cityscapes
        Metrics:
          mIoU: 75.20
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth
    Config: configs/deeplabv3+/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py



  - Name: fcn_m-v2-d8_512x512_160k_ade20k
    In Collection: FCN
    Metadata:
      inference time (ms/im):
        - value: 15.53
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 19.71
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth
    Config: configs/fcn/fcn_m-v2-d8_512x512_160k_ade20k.py



  - Name: pspnet_m-v2-d8_512x512_160k_ade20k
    In Collection: PSPNet
    Metadata:
      inference time (ms/im):
        - value: 17.33
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 29.68
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth
    Config: configs/pspnet/pspnet_m-v2-d8_512x512_160k_ade20k.py



  - Name: deeplabv3_m-v2-d8_512x512_160k_ade20k
    In Collection: DeepLabV3
    Metadata:
      inference time (ms/im):
        - value: 25.06
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 34.08
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth
    Config: configs/deeplabv3/deeplabv3_m-v2-d8_512x512_160k_ade20k.py



  - Name: deeplabv3plus_m-v2-d8_512x512_160k_ade20k
    In Collection: DeepLabV3+
    Metadata:
      inference time (ms/im):
        - value: 23.2
          hardware: V100
          backend: PyTorch
          batch size: 1
          mode: FP32
    Results:
      - Task: Semantic Segmentation
        Dataset: ADE20K
        Metrics:
          mIoU: 34.02
    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth
    Config: configs/deeplabv3+/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py