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snnetv2-semantic-segmentation
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Collections:
- Name: EMANet
  License: Apache License 2.0
  Metadata:
    Training Data:
    - Cityscapes
  Paper:
    Title: Expectation-Maximization Attention Networks for Semantic Segmentation
    URL: https://arxiv.org/abs/1907.13426
  README: configs/emanet/README.md
  Frameworks:
  - PyTorch
Models:
- Name: eemanet_r50-d8_4xb2-80k_cityscapes-512x1024
  In Collection: EMANet
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.59
      mIoU(ms+flip): 79.44
  Config: configs/emanet/eemanet_r50-d8_4xb2-80k_cityscapes-512x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - R-50-D8
    - EMANet
    Training Resources: 4x V100 GPUS
    Memory (GB): 5.4
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes-20200901_100301.log.json
  Paper:
    Title: Expectation-Maximization Attention Networks for Semantic Segmentation
    URL: https://arxiv.org/abs/1907.13426
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80
  Framework: PyTorch
- Name: emanet_r101-d8_4xb2-80k_cityscapes-512x1024
  In Collection: EMANet
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.1
      mIoU(ms+flip): 81.21
  Config: configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-512x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - R-101-D8
    - EMANet
    Training Resources: 4x V100 GPUS
    Memory (GB): 6.2
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes-20200901_100301.log.json
  Paper:
    Title: Expectation-Maximization Attention Networks for Semantic Segmentation
    URL: https://arxiv.org/abs/1907.13426
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80
  Framework: PyTorch
- Name: emanet_r50-d8_4xb2-80k_cityscapes-769x769
  In Collection: EMANet
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.33
      mIoU(ms+flip): 80.49
  Config: configs/emanet/emanet_r50-d8_4xb2-80k_cityscapes-769x769.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - R-50-D8
    - EMANet
    Training Resources: 4x V100 GPUS
    Memory (GB): 8.9
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes-20200901_100301.log.json
  Paper:
    Title: Expectation-Maximization Attention Networks for Semantic Segmentation
    URL: https://arxiv.org/abs/1907.13426
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80
  Framework: PyTorch
- Name: emanet_r101-d8_4xb2-80k_cityscapes-769x769
  In Collection: EMANet
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.62
      mIoU(ms+flip): 81.0
  Config: configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-769x769.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - R-101-D8
    - EMANet
    Training Resources: 4x V100 GPUS
    Memory (GB): 10.1
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes-20200901_100301.log.json
  Paper:
    Title: Expectation-Maximization Attention Networks for Semantic Segmentation
    URL: https://arxiv.org/abs/1907.13426
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80
  Framework: PyTorch