mmsegmentation/configs/segformer/metafile.yaml

Collections:
- Name: Segformer
  License: Apache License 2.0
  Metadata:
    Training Data:
    - ADE20K
    - Cityscapes
  Paper:
    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
      Transformers'
    URL: https://arxiv.org/abs/2105.15203
  README: configs/segformer/README.md
  Frameworks:
  - PyTorch
Models:
- Name: segformer_mit-b0_8xb2-160k_ade20k-512x512
  In Collection: Segformer
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 37.41
      mIoU(ms+flip): 38.34
  Config: configs/segformer/segformer_mit-b0_8xb2-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - MIT-B0
    - Segformer
    Training Resources: 8x 1080 Ti GPUS
    Memory (GB): 2.1
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530-8ffa8fda.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530.log.json
  Paper:
    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
      Transformers'
    URL: https://arxiv.org/abs/2105.15203
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
  Framework: PyTorch
- Name: segformer_mit-b1_8xb2-160k_ade20k-512x512
  In Collection: Segformer
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 40.97
      mIoU(ms+flip): 42.54
  Config: configs/segformer/segformer_mit-b1_8xb2-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - MIT-B1
    - Segformer
    Training Resources: 8x TITAN Xp GPUS
    Memory (GB): 2.6
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106-d70e859d.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106.log.json
  Paper:
    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
      Transformers'
    URL: https://arxiv.org/abs/2105.15203
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
  Framework: PyTorch
- Name: segformer_mit-b2_8xb2-160k_ade20k-512x512
  In Collection: Segformer
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 45.58
      mIoU(ms+flip): 47.03
  Config: configs/segformer/segformer_mit-b2_8xb2-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - MIT-B2
    - Segformer
    Training Resources: 8x TITAN Xp GPUS
    Memory (GB): 3.6
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103-cbd414ac.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103.log.json
  Paper:
    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
      Transformers'
    URL: https://arxiv.org/abs/2105.15203
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
  Framework: PyTorch
- Name: segformer_mit-b3_8xb2-160k_ade20k-512x512
  In Collection: Segformer
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 47.82
      mIoU(ms+flip): 48.81
  Config: configs/segformer/segformer_mit-b3_8xb2-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - MIT-B3
    - Segformer
    Training Resources: 8x V100 GPUS
    Memory (GB): 4.8
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410-962b98d2.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410.log.json
  Paper:
    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
      Transformers'
    URL: https://arxiv.org/abs/2105.15203
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
  Framework: PyTorch
- Name: segformer_mit-b4_8xb2-160k_ade20k-512x512
  In Collection: Segformer
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 48.46
      mIoU(ms+flip): 49.76
  Config: configs/segformer/segformer_mit-b4_8xb2-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - MIT-B4
    - Segformer
    Training Resources: 8x V100 GPUS
    Memory (GB): 6.1
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055-7f509d7d.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055.log.json
  Paper:
    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
      Transformers'
    URL: https://arxiv.org/abs/2105.15203
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
  Framework: PyTorch
- Name: segformer_mit-b5_8xb2-160k_ade20k-512x512
  In Collection: Segformer
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 49.13
      mIoU(ms+flip): 50.22
  Config: configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - MIT-B5
    - Segformer
    Training Resources: 8x V100 GPUS
    Memory (GB): 7.2
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235-94cedf59.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235.log.json
  Paper:
    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
      Transformers'
    URL: https://arxiv.org/abs/2105.15203
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
  Framework: PyTorch
- Name: segformer_mit-b5_8xb2-160k_ade20k-640x640
  In Collection: Segformer
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 49.62
      mIoU(ms+flip): 50.36
  Config: configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-640x640.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - MIT-B5
    - Segformer
    Training Resources: 8x V100 GPUS
    Memory (GB): 11.5
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243-41d2845b.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243.log.json
  Paper:
    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
      Transformers'
    URL: https://arxiv.org/abs/2105.15203
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
  Framework: PyTorch
- Name: segformer_mit-b0_8xb1-160k_cityscapes-1024x1024
  In Collection: Segformer
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 76.54
      mIoU(ms+flip): 78.22
  Config: configs/segformer/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - MIT-B0
    - Segformer
    Training Resources: 8x V100 GPUS
    Memory (GB): 3.64
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857-e7f88502.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857.log.json
  Paper:
    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
      Transformers'
    URL: https://arxiv.org/abs/2105.15203
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
  Framework: PyTorch
- Name: segformer_mit-b1_8xb1-160k_cityscapes-1024x1024
  In Collection: Segformer
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.56
      mIoU(ms+flip): 79.73
  Config: configs/segformer/segformer_mit-b1_8xb1-160k_cityscapes-1024x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - MIT-B1
    - Segformer
    Training Resources: 8x V100 GPUS
    Memory (GB): 4.49
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213-655c7b3f.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213.log.json
  Paper:
    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
      Transformers'
    URL: https://arxiv.org/abs/2105.15203
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
  Framework: PyTorch
- Name: segformer_mit-b2_8xb1-160k_cityscapes-1024x1024
  In Collection: Segformer
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 81.08
      mIoU(ms+flip): 82.18
  Config: configs/segformer/segformer_mit-b2_8xb1-160k_cityscapes-1024x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - MIT-B2
    - Segformer
    Training Resources: 8x V100 GPUS
    Memory (GB): 7.42
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205-6096669a.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205.log.json
  Paper:
    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
      Transformers'
    URL: https://arxiv.org/abs/2105.15203
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
  Framework: PyTorch
- Name: segformer_mit-b3_8xb1-160k_cityscapes-1024x1024
  In Collection: Segformer
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 81.94
      mIoU(ms+flip): 83.14
  Config: configs/segformer/segformer_mit-b3_8xb1-160k_cityscapes-1024x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - MIT-B3
    - Segformer
    Training Resources: 8x V100 GPUS
    Memory (GB): 10.86
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823-a8f8a177.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823.log.json
  Paper:
    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
      Transformers'
    URL: https://arxiv.org/abs/2105.15203
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
  Framework: PyTorch
- Name: segformer_mit-b4_8xb1-160k_cityscapes-1024x1024
  In Collection: Segformer
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 81.89
      mIoU(ms+flip): 83.38
  Config: configs/segformer/segformer_mit-b4_8xb1-160k_cityscapes-1024x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - MIT-B4
    - Segformer
    Training Resources: 8x V100 GPUS
    Memory (GB): 15.07
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709-07f6c333.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709.log.json
  Paper:
    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
      Transformers'
    URL: https://arxiv.org/abs/2105.15203
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
  Framework: PyTorch
- Name: segformer_mit-b5_8xb1-160k_cityscapes-1024x1024
  In Collection: Segformer
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 82.25
      mIoU(ms+flip): 83.48
  Config: configs/segformer/segformer_mit-b5_8xb1-160k_cityscapes-1024x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - MIT-B5
    - Segformer
    Training Resources: 8x V100 GPUS
    Memory (GB): 18.0
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934-87a052ec.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934.log.json
  Paper:
    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
      Transformers'
    URL: https://arxiv.org/abs/2105.15203
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
  Framework: PyTorch