Models:
- Name: vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512
  In Collection: UPerNet
  Metadata:
    backbone: ViT-B + MLN
    crop size: (512,512)
    lr schd: 80000
    inference time (ms/im):
    - value: 144.09
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 9.2
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 47.71
      mIoU(ms+flip): 49.51
  Config: configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_80k_ade20k/upernet_vit-b16_mln_512x512_80k_ade20k_20210624_130547-0403cee1.pth
- Name: vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512
  In Collection: UPerNet
  Metadata:
    backbone: ViT-B + MLN
    crop size: (512,512)
    lr schd: 160000
    inference time (ms/im):
    - value: 131.93
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 9.2
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 46.75
      mIoU(ms+flip): 48.46
  Config: configs/vit/vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_160k_ade20k/upernet_vit-b16_mln_512x512_160k_ade20k_20210624_130547-852fa768.pth
- Name: vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512
  In Collection: UPerNet
  Metadata:
    backbone: ViT-B + LN + MLN
    crop size: (512,512)
    lr schd: 160000
    inference time (ms/im):
    - value: 146.63
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 9.21
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 47.73
      mIoU(ms+flip): 49.95
  Config: configs/vit/vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k/upernet_vit-b16_ln_mln_512x512_160k_ade20k_20210621_172828-f444c077.pth
- Name: vit_deit-s16_upernet_8xb2-80k_ade20k-512x512
  In Collection: UPerNet
  Metadata:
    backbone: DeiT-S
    crop size: (512,512)
    lr schd: 80000
    inference time (ms/im):
    - value: 33.5
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 4.68
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 42.96
      mIoU(ms+flip): 43.79
  Config: configs/vit/vit_deit-s16_upernet_8xb2-80k_ade20k-512x512.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_80k_ade20k/upernet_deit-s16_512x512_80k_ade20k_20210624_095228-afc93ec2.pth
- Name: vit_deit-s16_upernet_8xb2-160k_ade20k-512x512
  In Collection: UPerNet
  Metadata:
    backbone: DeiT-S
    crop size: (512,512)
    lr schd: 160000
    inference time (ms/im):
    - value: 34.26
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 4.68
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 42.87
      mIoU(ms+flip): 43.79
  Config: configs/vit/vit_deit-s16_upernet_8xb2-160k_ade20k-512x512.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_160k_ade20k/upernet_deit-s16_512x512_160k_ade20k_20210621_160903-5110d916.pth
- Name: vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512
  In Collection: UPerNet
  Metadata:
    backbone: DeiT-S + MLN
    crop size: (512,512)
    lr schd: 160000
    inference time (ms/im):
    - value: 89.45
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 5.69
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 43.82
      mIoU(ms+flip): 45.07
  Config: configs/vit/vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_mln_512x512_160k_ade20k/upernet_deit-s16_mln_512x512_160k_ade20k_20210621_161021-fb9a5dfb.pth
- Name: vit_deit-s16-ln_mln_upernet_512x512_160k_ade20k-512x512
  In Collection: UPerNet
  Metadata:
    backbone: DeiT-S + LN + MLN
    crop size: (512,512)
    lr schd: 160000
    inference time (ms/im):
    - value: 80.71
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 5.69
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 43.52
      mIoU(ms+flip): 45.01
  Config: configs/vit/vit_deit-s16-ln_mln_upernet_512x512_160k_ade20k-512x512.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k/upernet_deit-s16_ln_mln_512x512_160k_ade20k_20210621_161021-c0cd652f.pth
- Name: vit_deit-b16_upernet_8xb2-80k_ade20k-512x512
  In Collection: UPerNet
  Metadata:
    backbone: DeiT-B
    crop size: (512,512)
    lr schd: 80000
    inference time (ms/im):
    - value: 103.2
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 7.75
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 45.24
      mIoU(ms+flip): 46.73
  Config: configs/vit/vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_80k_ade20k/upernet_deit-b16_512x512_80k_ade20k_20210624_130529-1e090789.pth
- Name: vit_deit-b16_upernet_8xb2-160k_ade20k-512x512
  In Collection: UPerNet
  Metadata:
    backbone: DeiT-B
    crop size: (512,512)
    lr schd: 160000
    inference time (ms/im):
    - value: 96.25
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 7.75
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 45.36
      mIoU(ms+flip): 47.16
  Config: configs/vit/vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_160k_ade20k/upernet_deit-b16_512x512_160k_ade20k_20210621_180100-828705d7.pth
- Name: vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512
  In Collection: UPerNet
  Metadata:
    backbone: DeiT-B + MLN
    crop size: (512,512)
    lr schd: 160000
    inference time (ms/im):
    - value: 128.53
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 9.21
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 45.46
      mIoU(ms+flip): 47.16
  Config: configs/vit/vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_mln_512x512_160k_ade20k/upernet_deit-b16_mln_512x512_160k_ade20k_20210621_191949-4e1450f3.pth
- Name: vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512
  In Collection: UPerNet
  Metadata:
    backbone: DeiT-B + LN + MLN
    crop size: (512,512)
    lr schd: 160000
    inference time (ms/im):
    - value: 129.03
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP32
      resolution: (512,512)
    Training Memory (GB): 9.21
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 45.37
      mIoU(ms+flip): 47.23
  Config: configs/vit/vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k/upernet_deit-b16_ln_mln_512x512_160k_ade20k_20210623_153535-8a959c14.pth