Collections:

- Name: encnet

  Metadata:

    Training Data:

    - Cityscapes

    - ADE20K

  Paper:

    URL: https://arxiv.org/abs/1803.08904

    Title: Context Encoding for Semantic Segmentation

  README: configs/encnet/README.md

  Code:

    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63

    Version: v0.17.0

  Converted From:

    Code: https://github.com/zhanghang1989/PyTorch-Encoding

Models:

- Name: encnet_r50-d8_512x1024_40k_cityscapes

  In Collection: encnet

  Metadata:

    backbone: R-50-D8

    crop size: (512,1024)

    lr schd: 40000

    inference time (ms/im):

    - value: 218.34

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,1024)

    Training Memory (GB): 8.6

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 75.67

      mIoU(ms+flip): 77.08

  Config: configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth

- Name: encnet_r101-d8_512x1024_40k_cityscapes

  In Collection: encnet

  Metadata:

    backbone: R-101-D8

    crop size: (512,1024)

    lr schd: 40000

    inference time (ms/im):

    - value: 375.94

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,1024)

    Training Memory (GB): 12.1

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 75.81

      mIoU(ms+flip): 77.21

  Config: configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth

- Name: encnet_r50-d8_769x769_40k_cityscapes

  In Collection: encnet

  Metadata:

    backbone: R-50-D8

    crop size: (769,769)

    lr schd: 40000

    inference time (ms/im):

    - value: 549.45

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (769,769)

    Training Memory (GB): 9.8

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 76.24

      mIoU(ms+flip): 77.85

  Config: configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth

- Name: encnet_r101-d8_769x769_40k_cityscapes

  In Collection: encnet

  Metadata:

    backbone: R-101-D8

    crop size: (769,769)

    lr schd: 40000

    inference time (ms/im):

    - value: 793.65

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (769,769)

    Training Memory (GB): 13.7

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 74.25

      mIoU(ms+flip): 76.25

  Config: configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth

- Name: encnet_r50-d8_512x1024_80k_cityscapes

  In Collection: encnet

  Metadata:

    backbone: R-50-D8

    crop size: (512,1024)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 77.94

      mIoU(ms+flip): 79.13

  Config: configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth

- Name: encnet_r101-d8_512x1024_80k_cityscapes

  In Collection: encnet

  Metadata:

    backbone: R-101-D8

    crop size: (512,1024)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 78.55

      mIoU(ms+flip): 79.47

  Config: configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth

- Name: encnet_r50-d8_769x769_80k_cityscapes

  In Collection: encnet

  Metadata:

    backbone: R-50-D8

    crop size: (769,769)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 77.44

      mIoU(ms+flip): 78.72

  Config: configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth

- Name: encnet_r101-d8_769x769_80k_cityscapes

  In Collection: encnet

  Metadata:

    backbone: R-101-D8

    crop size: (769,769)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 76.1

      mIoU(ms+flip): 76.97

  Config: configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth

- Name: encnet_r50-d8_512x512_80k_ade20k

  In Collection: encnet

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 80000

    inference time (ms/im):

    - value: 43.84

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 10.1

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 39.53

      mIoU(ms+flip): 41.17

  Config: configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth

- Name: encnet_r101-d8_512x512_80k_ade20k

  In Collection: encnet

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 80000

    inference time (ms/im):

    - value: 67.25

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 13.6

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 42.11

      mIoU(ms+flip): 43.61

  Config: configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth

- Name: encnet_r50-d8_512x512_160k_ade20k

  In Collection: encnet

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 160000

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 40.1

      mIoU(ms+flip): 41.71

  Config: configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth

- Name: encnet_r101-d8_512x512_160k_ade20k

  In Collection: encnet

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 160000

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 42.61

      mIoU(ms+flip): 44.01

  Config: configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth