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