Models:
- Name: twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512
  In Collection: FPN
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 43.26
      mIoU(ms+flip): 44.11
  Config: configs/twins/twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 32
    Architecture:
    - Twins-PCPVT-S
    - FPN
    Training Resources: 8x V100 GPUS
    Memory (GB): 6.6
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_204132-41acd132.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_204132.log.json
  Paper:
    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
    URL: https://arxiv.org/pdf/2104.13840.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
  Framework: PyTorch
- Name: twins_pcpvt-s_uperhead_8xb4-160k_ade20k-512x512
  In Collection: UPerNet
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 46.04
      mIoU(ms+flip): 46.92
  Config: configs/twins/twins_pcpvt-s_uperhead_8xb4-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 32
    Architecture:
    - Twins-PCPVT-S
    - UPerNet
    Training Resources: 8x V100 GPUS
    Memory (GB): 9.67
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k_20211201_233537-8e99c07a.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k_20211201_233537.log.json
  Paper:
    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
    URL: https://arxiv.org/pdf/2104.13840.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
  Framework: PyTorch
- Name: twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512
  In Collection: FPN
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 45.66
      mIoU(ms+flip): 46.48
  Config: configs/twins/twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 32
    Architecture:
    - Twins-PCPVT-B
    - FPN
    Training Resources: 8x V100 GPUS
    Memory (GB): 8.41
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141019-d396db72.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141019.log.json
  Paper:
    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
    URL: https://arxiv.org/pdf/2104.13840.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
  Framework: PyTorch
- Name: twins_pcpvt-b_uperhead_8xb2-160k_ade20k-512x512
  In Collection: UPerNet
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 47.91
      mIoU(ms+flip): 48.64
  Config: configs/twins/twins_pcpvt-b_uperhead_8xb2-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - Twins-PCPVT-B
    - UPerNet
    Training Resources: 8x V100 GPUS
    Memory (GB): 6.46
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k_20211130_141020-02094ea5.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k_20211130_141020.log.json
  Paper:
    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
    URL: https://arxiv.org/pdf/2104.13840.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
  Framework: PyTorch
- Name: twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512
  In Collection: FPN
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 45.94
      mIoU(ms+flip): 46.7
  Config: configs/twins/twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 32
    Architecture:
    - Twins-PCPVT-L
    - FPN
    Training Resources: 8x V100 GPUS
    Memory (GB): 10.78
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_105226-bc6d61dc.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_105226.log.json
  Paper:
    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
    URL: https://arxiv.org/pdf/2104.13840.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
  Framework: PyTorch
- Name: twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512
  In Collection: UPerNet
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 49.35
      mIoU(ms+flip): 50.08
  Config: configs/twins/twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - Twins-PCPVT-L
    - UPerNet
    Training Resources: 8x V100 GPUS
    Memory (GB): 7.82
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k_20211201_075053-c6095c07.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k_20211201_075053.log.json
  Paper:
    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
    URL: https://arxiv.org/pdf/2104.13840.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
  Framework: PyTorch
- Name: twins_svt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512
  In Collection: FPN
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 44.47
      mIoU(ms+flip): 45.42
  Config: configs/twins/twins_svt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 32
    Architecture:
    - Twins-SVT-S
    - FPN
    Training Resources: 8x V100 GPUS
    Memory (GB): 5.8
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141006-0a0d3317.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141006.log.json
  Paper:
    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
    URL: https://arxiv.org/pdf/2104.13840.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
  Framework: PyTorch
- Name: twins_svt-s_uperhead_8xb2-160k_ade20k-512x512
  In Collection: UPerNet
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 46.08
      mIoU(ms+flip): 46.96
  Config: configs/twins/twins_svt-s_uperhead_8xb2-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - SVT-S
    - UPerNet
    Training Resources: 8x V100 GPUS
    Memory (GB): 4.93
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k/twins_svt-s_uperhead_8x2_512x512_160k_ade20k_20211130_141005-e48a2d94.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k/twins_svt-s_uperhead_8x2_512x512_160k_ade20k_20211130_141005.log.json
  Paper:
    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
    URL: https://arxiv.org/pdf/2104.13840.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
  Framework: PyTorch
- Name: twins_svt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512
  In Collection: FPN
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 46.77
      mIoU(ms+flip): 47.47
  Config: configs/twins/twins_svt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 32
    Architecture:
    - Twins-SVT-B
    - FPN
    Training Resources: 8x V100 GPUS
    Memory (GB): 8.75
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_113849-88b2907c.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_113849.log.json
  Paper:
    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
    URL: https://arxiv.org/pdf/2104.13840.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
  Framework: PyTorch
- Name: twins_svt-b_uperhead_8xb2-160k_ade20k-512x512
  In Collection: UPerNet
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 48.04
      mIoU(ms+flip): 48.87
  Config: configs/twins/twins_svt-b_uperhead_8xb2-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - Twins-SVT-B
    - UPerNet
    Training Resources: 8x V100 GPUS
    Memory (GB): 6.77
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k/twins_svt-b_uperhead_8x2_512x512_160k_ade20k_20211202_040826-0943a1f1.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k/twins_svt-b_uperhead_8x2_512x512_160k_ade20k_20211202_040826.log.json
  Paper:
    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
    URL: https://arxiv.org/pdf/2104.13840.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
  Framework: PyTorch
- Name: twins_svt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512
  In Collection: FPN
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 46.55
      mIoU(ms+flip): 47.74
  Config: configs/twins/twins_svt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 32
    Architecture:
    - Twins-SVT-L
    - FPN
    Training Resources: 8x V100 GPUS
    Memory (GB): 11.2
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141005-1d59bee2.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141005.log.json
  Paper:
    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
    URL: https://arxiv.org/pdf/2104.13840.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
  Framework: PyTorch
- Name: twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512
  In Collection: UPerNet
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 49.65
      mIoU(ms+flip): 50.63
  Config: configs/twins/twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - Twins-SVT-L
    - UPerNet
    Training Resources: 8x V100 GPUS
    Memory (GB): 8.41
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k/twins_svt-l_uperhead_8x2_512x512_160k_ade20k_20211130_141005-3e2cae61.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k/twins_svt-l_uperhead_8x2_512x512_160k_ade20k_20211130_141005.log.json
  Paper:
    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
    URL: https://arxiv.org/pdf/2104.13840.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
  Framework: PyTorch