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+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