mmsegmentation/configs/unet/metafile.yaml

Collections:
- Name: UNet
  License: Apache License 2.0
  Metadata:
    Training Data:
    - Cityscapes
    - DRIVE
    - STARE
    - CHASE_DB1
    - HRF
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  README: configs/unet/README.md
  Frameworks:
  - PyTorch
Models:
- Name: unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 69.1
      mIoU(ms+flip): 71.05
  Config: configs/unet/unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - FCN
    Training Resources: 4x V100 GPUS
    Memory (GB): 17.91
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_fcn_4xb4-40k_drive-64x64
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: DRIVE
    Metrics:
      mDice: 88.38
      Dice: 78.67
  Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_drive-64x64.py
  Metadata:
    Training Data: DRIVE
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - FCN
    Training Resources: 4x V100 GPUS
    Memory (GB): 0.68
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/unet_s5-d16_64x64_40k_drive-20201223_191051.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: DRIVE
    Metrics:
      mDice: 88.71
      Dice: 79.32
  Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py
  Metadata:
    Training Data: DRIVE
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - FCN
    Training Resources: 4x V100 GPUS
    Memory (GB): 0.582
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820-785de5c2.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_pspnet_4xb4-40k_drive-64x64
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: DRIVE
    Metrics:
      mDice: 88.35
      Dice: 78.62
  Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_drive-64x64.py
  Metadata:
    Training Data: DRIVE
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - PSPNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 0.599
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: DRIVE
    Metrics:
      mDice: 88.76
      Dice: 79.42
  Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py
  Metadata:
    Training Data: DRIVE
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - PSPNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 0.585
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821-22b3e3ba.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: DRIVE
    Metrics:
      mDice: 88.38
      Dice: 78.69
  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64.py
  Metadata:
    Training Data: DRIVE
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - DeepLabV3
    Training Resources: 4x V100 GPUS
    Memory (GB): 0.596
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: DRIVE
    Metrics:
      mDice: 88.84
      Dice: 79.56
  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py
  Metadata:
    Training Data: DRIVE
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - DeepLabV3
    Training Resources: 4x V100 GPUS
    Memory (GB): 0.582
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825-6bf0efd7.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_fcn_4xb4-40k_stare-128x128
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: STARE
    Metrics:
      mDice: 89.78
      Dice: 81.02
  Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_stare-128x128.py
  Metadata:
    Training Data: STARE
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - FCN
    Training Resources: 4x V100 GPUS
    Memory (GB): 0.968
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare-20201223_191051.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: STARE
    Metrics:
      mDice: 90.65
      Dice: 82.7
  Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py
  Metadata:
    Training Data: STARE
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - FCN
    Training Resources: 4x V100 GPUS
    Memory (GB): 0.986
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821-f75705a9.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_pspnet_4xb4-40k_stare-128x128
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: STARE
    Metrics:
      mDice: 89.89
      Dice: 81.22
  Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_stare-128x128.py
  Metadata:
    Training Data: STARE
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - PSPNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 0.982
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: STARE
    Metrics:
      mDice: 90.72
      Dice: 82.84
  Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py
  Metadata:
    Training Data: STARE
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - PSPNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 1.028
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823-f1063ef7.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: STARE
    Metrics:
      mDice: 89.73
      Dice: 80.93
  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128.py
  Metadata:
    Training Data: STARE
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - DeepLabV3
    Training Resources: 4x V100 GPUS
    Memory (GB): 0.999
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: STARE
    Metrics:
      mDice: 90.65
      Dice: 82.71
  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py
  Metadata:
    Training Data: STARE
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - DeepLabV3
    Training Resources: 4x V100 GPUS
    Memory (GB): 1.01
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825-21db614c.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: CHASE_DB1
    Metrics:
      mDice: 89.46
      Dice: 80.24
  Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py
  Metadata:
    Training Data: CHASE_DB1
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - FCN
    Training Resources: 4x V100 GPUS
    Memory (GB): 0.968
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: CHASE_DB1
    Metrics:
      mDice: 89.52
      Dice: 80.4
  Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py
  Metadata:
    Training Data: CHASE_DB1
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - FCN
    Training Resources: 4x V100 GPUS
    Memory (GB): 0.986
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821-1c4eb7cf.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: CHASE_DB1
    Metrics:
      mDice: 89.52
      Dice: 80.36
  Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128.py
  Metadata:
    Training Data: CHASE_DB1
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - PSPNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 0.982
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1-20201227_181818.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: CHASE_DB1
    Metrics:
      mDice: 89.45
      Dice: 80.28
  Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py
  Metadata:
    Training Data: CHASE_DB1
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - PSPNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 1.028
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823-c0802c4d.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: CHASE_DB1
    Metrics:
      mDice: 89.57
      Dice: 80.47
  Config: configs/unet/unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128.py
  Metadata:
    Training Data: CHASE_DB1
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - DeepLabV3
    Training Resources: 4x V100 GPUS
    Memory (GB): 0.999
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1-20201226_094047.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: CHASE_DB1
    Metrics:
      mDice: 89.49
      Dice: 80.37
  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py
  Metadata:
    Training Data: CHASE_DB1
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - DeepLabV3
    Training Resources: 4x V100 GPUS
    Memory (GB): 1.01
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825-4ef29df5.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_fcn_4xb4-40k_hrf-256x256
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: HRF
    Metrics:
      mDice: 88.92
      Dice: 79.45
  Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py
  Metadata:
    Training Data: HRF
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - FCN
    Training Resources: 4x V100 GPUS
    Memory (GB): 2.525
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: HRF
    Metrics:
      mDice: 89.64
      Dice: 80.87
  Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py
  Metadata:
    Training Data: HRF
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - FCN
    Training Resources: 4x V100 GPUS
    Memory (GB): 2.623
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821-c314da8a.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_pspnet_4xb4-40k_hrf-256x256
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: HRF
    Metrics:
      mDice: 89.24
      Dice: 80.07
  Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_hrf-256x256.py
  Metadata:
    Training Data: HRF
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - PSPNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 2.588
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: HRF
    Metrics:
      mDice: 89.69
      Dice: 80.96
  Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py
  Metadata:
    Training Data: HRF
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - PSPNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 2.798
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823-53d492fa.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: HRF
    Metrics:
      mDice: 89.32
      Dice: 80.21
  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256.py
  Metadata:
    Training Data: HRF
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - DeepLabV3
    Training Resources: 4x V100 GPUS
    Memory (GB): 2.604
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch
- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256
  In Collection: UNet
  Results:
    Task: Semantic Segmentation
    Dataset: HRF
    Metrics:
      mDice: 89.56
      Dice: 80.71
  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py
  Metadata:
    Training Data: HRF
    Batch Size: 16
    Architecture:
    - UNet-S5-D16
    - UNet
    - DeepLabV3
    Training Resources: 4x V100 GPUS
    Memory (GB): 2.607
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032-59daf7a4.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032.log.json
  Paper:
    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
    URL: https://arxiv.org/abs/1505.04597
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
  Framework: PyTorch