Models:
- Name: resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024
  In Collection: FCN
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.56
      mIoU(ms+flip): 78.98
  Config: configs/resnest/resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - S-101-D8
    - FCN
    Training Resources: 4x V100 GPUS
    Memory (GB): 11.4
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes-20200807_140631.log.json
  Paper:
    Title: 'ResNeSt: Split-Attention Networks'
    URL: https://arxiv.org/abs/2004.08955
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
  Framework: PyTorch
- Name: resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024
  In Collection: PSPNet
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.57
      mIoU(ms+flip): 79.19
  Config: configs/resnest/resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - S-101-D8
    - PSPNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 11.8
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes-20200807_140631.log.json
  Paper:
    Title: 'ResNeSt: Split-Attention Networks'
    URL: https://arxiv.org/abs/2004.08955
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
  Framework: PyTorch
- Name: resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024
  In Collection: DeepLabV3
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.67
      mIoU(ms+flip): 80.51
  Config: configs/resnest/resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - S-101-D8
    - DeepLabV3
    Training Resources: 4x V100 GPUS
    Memory (GB): 11.9
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json
  Paper:
    Title: 'ResNeSt: Split-Attention Networks'
    URL: https://arxiv.org/abs/2004.08955
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
  Framework: PyTorch
- Name: resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024
  In Collection: DeepLabV3+
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.62
      mIoU(ms+flip): 80.27
  Config: configs/resnest/resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - S-101-D8
    - DeepLabV3+
    Training Resources: 4x V100 GPUS
    Memory (GB): 13.2
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json
  Paper:
    Title: 'ResNeSt: Split-Attention Networks'
    URL: https://arxiv.org/abs/2004.08955
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
  Framework: PyTorch
- Name: resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512
  In Collection: FCN
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 45.62
      mIoU(ms+flip): 46.16
  Config: configs/resnest/resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - S-101-D8
    - FCN
    Training Resources: 4x V100 GPUS
    Memory (GB): 14.2
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k-20200807_145416.log.json
  Paper:
    Title: 'ResNeSt: Split-Attention Networks'
    URL: https://arxiv.org/abs/2004.08955
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
  Framework: PyTorch
- Name: resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512
  In Collection: PSPNet
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 45.44
      mIoU(ms+flip): 46.28
  Config: configs/resnest/resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - S-101-D8
    - PSPNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 14.2
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k-20200807_145416.log.json
  Paper:
    Title: 'ResNeSt: Split-Attention Networks'
    URL: https://arxiv.org/abs/2004.08955
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
  Framework: PyTorch
- Name: resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512
  In Collection: DeepLabV3
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 45.71
      mIoU(ms+flip): 46.59
  Config: configs/resnest/resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - S-101-D8
    - DeepLabV3
    Training Resources: 4x V100 GPUS
    Memory (GB): 14.6
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k-20200807_144503.log.json
  Paper:
    Title: 'ResNeSt: Split-Attention Networks'
    URL: https://arxiv.org/abs/2004.08955
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
  Framework: PyTorch
- Name: resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512
  In Collection: DeepLabV3+
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 46.47
      mIoU(ms+flip): 47.27
  Config: configs/resnest/resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - S-101-D8
    - DeepLabV3+
    Training Resources: 4x V100 GPUS
    Memory (GB): 16.2
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k-20200807_144503.log.json
  Paper:
    Title: 'ResNeSt: Split-Attention Networks'
    URL: https://arxiv.org/abs/2004.08955
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
  Framework: PyTorch