Collections:

- Name: deeplabv3

  Metadata:

    Training Data:

    - Cityscapes

    - ADE20K

    - Pascal VOC 2012 + Aug

    - Pascal Context

    - Pascal Context 59

    - COCO-Stuff 10k

    - COCO-Stuff 164k

  Paper:

    URL: https://arxiv.org/abs/1706.05587

    Title: Rethinking atrous convolution for semantic image segmentation

  README: configs/deeplabv3/README.md

  Code:

    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54

    Version: v0.17.0

  Converted From:

    Code: https://github.com/tensorflow/models/tree/master/research/deeplab

Models:

- Name: deeplabv3_r50-d8_512x1024_40k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-50-D8

    crop size: (512,1024)

    lr schd: 40000

    inference time (ms/im):

    - value: 389.11

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,1024)

    Training Memory (GB): 6.1

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 79.09

      mIoU(ms+flip): 80.45

  Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth

- Name: deeplabv3_r101-d8_512x1024_40k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (512,1024)

    lr schd: 40000

    inference time (ms/im):

    - value: 520.83

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,1024)

    Training Memory (GB): 9.6

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 77.12

      mIoU(ms+flip): 79.61

  Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth

- Name: deeplabv3_r50-d8_769x769_40k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-50-D8

    crop size: (769,769)

    lr schd: 40000

    inference time (ms/im):

    - value: 900.9

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (769,769)

    Training Memory (GB): 6.9

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 78.58

      mIoU(ms+flip): 79.89

  Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth

- Name: deeplabv3_r101-d8_769x769_40k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (769,769)

    lr schd: 40000

    inference time (ms/im):

    - value: 1204.82

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (769,769)

    Training Memory (GB): 10.9

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 79.27

      mIoU(ms+flip): 80.11

  Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth

- Name: deeplabv3_r18-d8_512x1024_80k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-18-D8

    crop size: (512,1024)

    lr schd: 80000

    inference time (ms/im):

    - value: 72.57

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,1024)

    Training Memory (GB): 1.7

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 76.7

      mIoU(ms+flip): 78.27

  Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth

- Name: deeplabv3_r50-d8_512x1024_80k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-50-D8

    crop size: (512,1024)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 79.32

      mIoU(ms+flip): 80.57

  Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth

- Name: deeplabv3_r101-d8_512x1024_80k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (512,1024)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 80.2

      mIoU(ms+flip): 81.21

  Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth

- Name: deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (512,1024)

    lr schd: 80000

    inference time (ms/im):

    - value: 259.07

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP16

      resolution: (512,1024)

    Training Memory (GB): 5.75

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 80.48

  Config: configs/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-774d9cec.pth

- Name: deeplabv3_r18-d8_769x769_80k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-18-D8

    crop size: (769,769)

    lr schd: 80000

    inference time (ms/im):

    - value: 180.18

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (769,769)

    Training Memory (GB): 1.9

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 76.6

      mIoU(ms+flip): 78.26

  Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth

- Name: deeplabv3_r50-d8_769x769_80k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-50-D8

    crop size: (769,769)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 79.89

      mIoU(ms+flip): 81.06

  Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth

- Name: deeplabv3_r101-d8_769x769_80k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (769,769)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 79.67

      mIoU(ms+flip): 80.81

  Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth

- Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D16-MG124

    crop size: (512,1024)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 78.36

      mIoU(ms+flip): 79.84

  Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth

- Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-18b-D8

    crop size: (512,1024)

    lr schd: 80000

    inference time (ms/im):

    - value: 71.79

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,1024)

    Training Memory (GB): 1.6

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 76.26

      mIoU(ms+flip): 77.88

  Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth

- Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-50b-D8

    crop size: (512,1024)

    lr schd: 80000

    inference time (ms/im):

    - value: 364.96

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,1024)

    Training Memory (GB): 6.0

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 79.63

      mIoU(ms+flip): 80.98

  Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth

- Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-101b-D8

    crop size: (512,1024)

    lr schd: 80000

    inference time (ms/im):

    - value: 552.49

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,1024)

    Training Memory (GB): 9.5

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 80.01

      mIoU(ms+flip): 81.21

  Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth

- Name: deeplabv3_r18b-d8_769x769_80k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-18b-D8

    crop size: (769,769)

    lr schd: 80000

    inference time (ms/im):

    - value: 172.71

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (769,769)

    Training Memory (GB): 1.8

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 76.63

      mIoU(ms+flip): 77.51

  Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth

- Name: deeplabv3_r50b-d8_769x769_80k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-50b-D8

    crop size: (769,769)

    lr schd: 80000

    inference time (ms/im):

    - value: 862.07

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (769,769)

    Training Memory (GB): 6.8

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 78.8

      mIoU(ms+flip): 80.27

  Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth

- Name: deeplabv3_r101b-d8_769x769_80k_cityscapes

  In Collection: deeplabv3

  Metadata:

    backbone: R-101b-D8

    crop size: (769,769)

    lr schd: 80000

    inference time (ms/im):

    - value: 1219.51

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (769,769)

    Training Memory (GB): 10.7

  Results:

  - Task: Semantic Segmentation

    Dataset: Cityscapes

    Metrics:

      mIoU: 79.41

      mIoU(ms+flip): 80.73

  Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth

- Name: deeplabv3_r50-d8_512x512_80k_ade20k

  In Collection: deeplabv3

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 80000

    inference time (ms/im):

    - value: 67.75

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 8.9

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 42.42

      mIoU(ms+flip): 43.28

  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth

- Name: deeplabv3_r101-d8_512x512_80k_ade20k

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 80000

    inference time (ms/im):

    - value: 98.62

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 12.4

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 44.08

      mIoU(ms+flip): 45.19

  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth

- Name: deeplabv3_r50-d8_512x512_160k_ade20k

  In Collection: deeplabv3

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 160000

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 42.66

      mIoU(ms+flip): 44.09

  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth

- Name: deeplabv3_r101-d8_512x512_160k_ade20k

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 160000

  Results:

  - Task: Semantic Segmentation

    Dataset: ADE20K

    Metrics:

      mIoU: 45.0

      mIoU(ms+flip): 46.66

  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth

- Name: deeplabv3_r50-d8_512x512_20k_voc12aug

  In Collection: deeplabv3

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 20000

    inference time (ms/im):

    - value: 72.05

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 6.1

  Results:

  - Task: Semantic Segmentation

    Dataset: Pascal VOC 2012 + Aug

    Metrics:

      mIoU: 76.17

      mIoU(ms+flip): 77.42

  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth

- Name: deeplabv3_r101-d8_512x512_20k_voc12aug

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 20000

    inference time (ms/im):

    - value: 101.94

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 9.6

  Results:

  - Task: Semantic Segmentation

    Dataset: Pascal VOC 2012 + Aug

    Metrics:

      mIoU: 78.7

      mIoU(ms+flip): 79.95

  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth

- Name: deeplabv3_r50-d8_512x512_40k_voc12aug

  In Collection: deeplabv3

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 40000

  Results:

  - Task: Semantic Segmentation

    Dataset: Pascal VOC 2012 + Aug

    Metrics:

      mIoU: 77.68

      mIoU(ms+flip): 78.78

  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth

- Name: deeplabv3_r101-d8_512x512_40k_voc12aug

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 40000

  Results:

  - Task: Semantic Segmentation

    Dataset: Pascal VOC 2012 + Aug

    Metrics:

      mIoU: 77.92

      mIoU(ms+flip): 79.18

  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth

- Name: deeplabv3_r101-d8_480x480_40k_pascal_context

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (480,480)

    lr schd: 40000

    inference time (ms/im):

    - value: 141.04

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (480,480)

    Training Memory (GB): 9.2

  Results:

  - Task: Semantic Segmentation

    Dataset: Pascal Context

    Metrics:

      mIoU: 46.55

      mIoU(ms+flip): 47.81

  Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth

- Name: deeplabv3_r101-d8_480x480_80k_pascal_context

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (480,480)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Pascal Context

    Metrics:

      mIoU: 46.42

      mIoU(ms+flip): 47.53

  Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth

- Name: deeplabv3_r101-d8_480x480_40k_pascal_context_59

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (480,480)

    lr schd: 40000

  Results:

  - Task: Semantic Segmentation

    Dataset: Pascal Context 59

    Metrics:

      mIoU: 52.61

      mIoU(ms+flip): 54.28

  Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth

- Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (480,480)

    lr schd: 80000

  Results:

  - Task: Semantic Segmentation

    Dataset: Pascal Context 59

    Metrics:

      mIoU: 52.46

      mIoU(ms+flip): 54.09

  Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth

- Name: deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k

  In Collection: deeplabv3

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 20000

    inference time (ms/im):

    - value: 92.59

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 9.6

  Results:

  - Task: Semantic Segmentation

    Dataset: COCO-Stuff 10k

    Metrics:

      mIoU: 34.66

      mIoU(ms+flip): 36.08

  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-b35f789d.pth

- Name: deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 20000

    inference time (ms/im):

    - value: 114.94

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 13.2

  Results:

  - Task: Semantic Segmentation

    Dataset: COCO-Stuff 10k

    Metrics:

      mIoU: 37.3

      mIoU(ms+flip): 38.42

  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-c49752cb.pth

- Name: deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k

  In Collection: deeplabv3

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 40000

  Results:

  - Task: Semantic Segmentation

    Dataset: COCO-Stuff 10k

    Metrics:

      mIoU: 35.73

      mIoU(ms+flip): 37.09

  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-dc76f3ff.pth

- Name: deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 40000

  Results:

  - Task: Semantic Segmentation

    Dataset: COCO-Stuff 10k

    Metrics:

      mIoU: 37.81

      mIoU(ms+flip): 38.8

  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-636cb433.pth

- Name: deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k

  In Collection: deeplabv3

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 80000

    inference time (ms/im):

    - value: 92.59

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 9.6

  Results:

  - Task: Semantic Segmentation

    Dataset: COCO-Stuff 164k

    Metrics:

      mIoU: 39.38

      mIoU(ms+flip): 40.03

  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016-88675c24.pth

- Name: deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 80000

    inference time (ms/im):

    - value: 114.94

      hardware: V100

      backend: PyTorch

      batch size: 1

      mode: FP32

      resolution: (512,512)

    Training Memory (GB): 13.2

  Results:

  - Task: Semantic Segmentation

    Dataset: COCO-Stuff 164k

    Metrics:

      mIoU: 40.87

      mIoU(ms+flip): 41.5

  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252-13600dc2.pth

- Name: deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k

  In Collection: deeplabv3

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 160000

  Results:

  - Task: Semantic Segmentation

    Dataset: COCO-Stuff 164k

    Metrics:

      mIoU: 41.09

      mIoU(ms+flip): 41.69

  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016-49f2812b.pth

- Name: deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 160000

  Results:

  - Task: Semantic Segmentation

    Dataset: COCO-Stuff 164k

    Metrics:

      mIoU: 41.82

      mIoU(ms+flip): 42.49

  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402-f035acfd.pth

- Name: deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k

  In Collection: deeplabv3

  Metadata:

    backbone: R-50-D8

    crop size: (512,512)

    lr schd: 320000

  Results:

  - Task: Semantic Segmentation

    Dataset: COCO-Stuff 164k

    Metrics:

      mIoU: 41.37

      mIoU(ms+flip): 42.22

  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403-51b21115.pth

- Name: deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k

  In Collection: deeplabv3

  Metadata:

    backbone: R-101-D8

    crop size: (512,512)

    lr schd: 320000

  Results:

  - Task: Semantic Segmentation

    Dataset: COCO-Stuff 164k

    Metrics:

      mIoU: 42.61

      mIoU(ms+flip): 43.42

  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.py

  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402-3cbca14d.pth