Collections:
- Name: DMNet
  License: Apache License 2.0
  Metadata:
    Training Data:
    - Cityscapes
    - ADE20K
  Paper:
    Title: Dynamic Multi-scale Filters for Semantic Segmentation
    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
  README: configs/dmnet/README.md
  Frameworks:
  - PyTorch
Models:
- Name: dmnet_r50-d8_4xb2-40k_cityscapes-512x1024
  In Collection: DMNet
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.78
      mIoU(ms+flip): 79.14
  Config: configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - R-50-D8
    - DMNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 7.0
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201215_042326-615373cf.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes-20201215_042326.log.json
  Paper:
    Title: Dynamic Multi-scale Filters for Semantic Segmentation
    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
  Framework: PyTorch
- Name: dmnet_r101-d8_4xb2-40k_cityscapes-512x1024
  In Collection: DMNet
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.37
      mIoU(ms+flip): 79.72
  Config: configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - R-101-D8
    - DMNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 10.6
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201215_043100-8291e976.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes-20201215_043100.log.json
  Paper:
    Title: Dynamic Multi-scale Filters for Semantic Segmentation
    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
  Framework: PyTorch
- Name: dmnet_r50-d8_4xb2-40k_cityscapes-769x769
  In Collection: DMNet
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 78.49
      mIoU(ms+flip): 80.27
  Config: configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-769x769.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - R-50-D8
    - DMNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 7.9
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201215_093706-e7f0e23e.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes-20201215_093706.log.json
  Paper:
    Title: Dynamic Multi-scale Filters for Semantic Segmentation
    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
  Framework: PyTorch
- Name: dmnet_r101-d8_4xb2-40k_cityscapes-769x769
  In Collection: DMNet
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 77.62
      mIoU(ms+flip): 78.94
  Config: configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-769x769.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - R-101-D8
    - DMNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 12.0
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201215_081348-a74261f6.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes-20201215_081348.log.json
  Paper:
    Title: Dynamic Multi-scale Filters for Semantic Segmentation
    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
  Framework: PyTorch
- Name: dmnet_r50-d8_4xb2-80k_cityscapes-512x1024
  In Collection: DMNet
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.07
      mIoU(ms+flip): 80.22
  Config: configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - R-50-D8
    - DMNet
    Training Resources: 4x V100 GPUS
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201215_053728-3c8893b9.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes-20201215_053728.log.json
  Paper:
    Title: Dynamic Multi-scale Filters for Semantic Segmentation
    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
  Framework: PyTorch
- Name: dmnet_r101-d8_4xb2-80k_cityscapes-512x1024
  In Collection: DMNet
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.64
      mIoU(ms+flip): 80.67
  Config: configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - R-101-D8
    - DMNet
    Training Resources: 4x V100 GPUS
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201215_031718-fa081cb8.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes-20201215_031718.log.json
  Paper:
    Title: Dynamic Multi-scale Filters for Semantic Segmentation
    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
  Framework: PyTorch
- Name: dmnet_r50-d8_4xb2-80k_cityscapes-769x769
  In Collection: DMNet
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.22
      mIoU(ms+flip): 80.55
  Config: configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-769x769.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - R-50-D8
    - DMNet
    Training Resources: 4x V100 GPUS
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201215_034006-6060840e.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes-20201215_034006.log.json
  Paper:
    Title: Dynamic Multi-scale Filters for Semantic Segmentation
    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
  Framework: PyTorch
- Name: dmnet_r101-d8_4xb2-80k_cityscapes-769x769
  In Collection: DMNet
  Results:
    Task: Semantic Segmentation
    Dataset: Cityscapes
    Metrics:
      mIoU: 79.19
      mIoU(ms+flip): 80.65
  Config: configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-769x769.py
  Metadata:
    Training Data: Cityscapes
    Batch Size: 8
    Architecture:
    - R-101-D8
    - DMNet
    Training Resources: 4x V100 GPUS
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201215_082810-7f0de59a.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes-20201215_082810.log.json
  Paper:
    Title: Dynamic Multi-scale Filters for Semantic Segmentation
    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
  Framework: PyTorch
- Name: dmnet_r50-d8_4xb4-80k_ade20k-512x512
  In Collection: DMNet
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 42.37
      mIoU(ms+flip): 43.62
  Config: configs/dmnet/dmnet_r50-d8_4xb4-80k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - R-50-D8
    - DMNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 9.4
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201215_144744-f89092a6.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k-20201215_144744.log.json
  Paper:
    Title: Dynamic Multi-scale Filters for Semantic Segmentation
    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
  Framework: PyTorch
- Name: dmnet_r101-d8_4xb4-80k_ade20k-512x512
  In Collection: DMNet
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 45.34
      mIoU(ms+flip): 46.13
  Config: configs/dmnet/dmnet_r101-d8_4xb4-80k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - R-101-D8
    - DMNet
    Training Resources: 4x V100 GPUS
    Memory (GB): 13.0
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201215_104812-bfa45311.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k-20201215_104812.log.json
  Paper:
    Title: Dynamic Multi-scale Filters for Semantic Segmentation
    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
  Framework: PyTorch
- Name: dmnet_r50-d8_4xb4-160k_ade20k-512x512
  In Collection: DMNet
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 43.15
      mIoU(ms+flip): 44.17
  Config: configs/dmnet/dmnet_r50-d8_4xb4-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - R-50-D8
    - DMNet
    Training Resources: 4x V100 GPUS
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201215_115313-025ab3f9.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k-20201215_115313.log.json
  Paper:
    Title: Dynamic Multi-scale Filters for Semantic Segmentation
    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
  Framework: PyTorch
- Name: dmnet_r101-d8_4xb4-160k_ade20k-512x512
  In Collection: DMNet
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 45.42
      mIoU(ms+flip): 46.76
  Config: configs/dmnet/dmnet_r101-d8_4xb4-160k_ade20k-512x512.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - R-101-D8
    - DMNet
    Training Resources: 4x V100 GPUS
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201215_111145-a0bc02ef.pth
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k-20201215_111145.log.json
  Paper:
    Title: Dynamic Multi-scale Filters for Semantic Segmentation
    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
  Framework: PyTorch