297 lines
13 KiB
YAML
297 lines
13 KiB
YAML
Collections:
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- Name: DMNet
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License: Apache License 2.0
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Metadata:
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Training Data:
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- Cityscapes
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- ADE20K
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Paper:
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Title: Dynamic Multi-scale Filters for Semantic Segmentation
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URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
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README: configs/dmnet/README.md
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Frameworks:
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- PyTorch
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Models:
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- Name: dmnet_r50-d8_4xb2-40k_cityscapes-512x1024
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In Collection: DMNet
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Results:
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Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 77.78
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mIoU(ms+flip): 79.14
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Config: configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
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Metadata:
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Training Data: Cityscapes
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Batch Size: 8
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Architecture:
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- R-50-D8
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- DMNet
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Training Resources: 4x V100 GPUS
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Memory (GB): 7.0
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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
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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
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Paper:
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Title: Dynamic Multi-scale Filters for Semantic Segmentation
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URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
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Framework: PyTorch
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- Name: dmnet_r101-d8_4xb2-40k_cityscapes-512x1024
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In Collection: DMNet
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Results:
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Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 78.37
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mIoU(ms+flip): 79.72
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Config: configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
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Metadata:
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Training Data: Cityscapes
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Batch Size: 8
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Architecture:
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- R-101-D8
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- DMNet
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Training Resources: 4x V100 GPUS
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Memory (GB): 10.6
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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
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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
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Paper:
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Title: Dynamic Multi-scale Filters for Semantic Segmentation
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URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
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Framework: PyTorch
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- Name: dmnet_r50-d8_4xb2-40k_cityscapes-769x769
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In Collection: DMNet
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Results:
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Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 78.49
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mIoU(ms+flip): 80.27
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Config: configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-769x769.py
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Metadata:
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Training Data: Cityscapes
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Batch Size: 8
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Architecture:
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- R-50-D8
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- DMNet
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Training Resources: 4x V100 GPUS
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Memory (GB): 7.9
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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
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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
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Paper:
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Title: Dynamic Multi-scale Filters for Semantic Segmentation
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URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
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Framework: PyTorch
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- Name: dmnet_r101-d8_4xb2-40k_cityscapes-769x769
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In Collection: DMNet
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Results:
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Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 77.62
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mIoU(ms+flip): 78.94
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Config: configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-769x769.py
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Metadata:
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Training Data: Cityscapes
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Batch Size: 8
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Architecture:
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- R-101-D8
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- DMNet
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Training Resources: 4x V100 GPUS
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Memory (GB): 12.0
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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
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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
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Paper:
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Title: Dynamic Multi-scale Filters for Semantic Segmentation
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URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
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Framework: PyTorch
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- Name: dmnet_r50-d8_4xb2-80k_cityscapes-512x1024
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In Collection: DMNet
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Results:
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Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.07
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mIoU(ms+flip): 80.22
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Config: configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
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Metadata:
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Training Data: Cityscapes
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Batch Size: 8
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Architecture:
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- R-50-D8
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- DMNet
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Training Resources: 4x V100 GPUS
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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
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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
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Paper:
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Title: Dynamic Multi-scale Filters for Semantic Segmentation
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URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
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Framework: PyTorch
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- Name: dmnet_r101-d8_4xb2-80k_cityscapes-512x1024
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In Collection: DMNet
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Results:
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Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.64
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mIoU(ms+flip): 80.67
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Config: configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
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Metadata:
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Training Data: Cityscapes
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Batch Size: 8
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Architecture:
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- R-101-D8
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- DMNet
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Training Resources: 4x V100 GPUS
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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
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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
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Paper:
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Title: Dynamic Multi-scale Filters for Semantic Segmentation
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URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
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Framework: PyTorch
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- Name: dmnet_r50-d8_4xb2-80k_cityscapes-769x769
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In Collection: DMNet
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Results:
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Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.22
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mIoU(ms+flip): 80.55
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Config: configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-769x769.py
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Metadata:
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Training Data: Cityscapes
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Batch Size: 8
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Architecture:
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- R-50-D8
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- DMNet
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Training Resources: 4x V100 GPUS
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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
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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
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Paper:
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Title: Dynamic Multi-scale Filters for Semantic Segmentation
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URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
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Framework: PyTorch
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- Name: dmnet_r101-d8_4xb2-80k_cityscapes-769x769
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In Collection: DMNet
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Results:
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Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.19
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mIoU(ms+flip): 80.65
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Config: configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-769x769.py
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Metadata:
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Training Data: Cityscapes
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Batch Size: 8
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Architecture:
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- R-101-D8
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- DMNet
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Training Resources: 4x V100 GPUS
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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
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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
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Paper:
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Title: Dynamic Multi-scale Filters for Semantic Segmentation
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URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
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Framework: PyTorch
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- Name: dmnet_r50-d8_4xb4-80k_ade20k-512x512
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In Collection: DMNet
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Results:
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Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 42.37
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mIoU(ms+flip): 43.62
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Config: configs/dmnet/dmnet_r50-d8_4xb4-80k_ade20k-512x512.py
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Metadata:
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Training Data: ADE20K
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Batch Size: 16
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Architecture:
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- R-50-D8
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- DMNet
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Training Resources: 4x V100 GPUS
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Memory (GB): 9.4
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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
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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
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Paper:
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Title: Dynamic Multi-scale Filters for Semantic Segmentation
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URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
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Framework: PyTorch
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- Name: dmnet_r101-d8_4xb4-80k_ade20k-512x512
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In Collection: DMNet
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Results:
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Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 45.34
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mIoU(ms+flip): 46.13
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Config: configs/dmnet/dmnet_r101-d8_4xb4-80k_ade20k-512x512.py
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Metadata:
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Training Data: ADE20K
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Batch Size: 16
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Architecture:
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- R-101-D8
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- DMNet
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Training Resources: 4x V100 GPUS
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Memory (GB): 13.0
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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
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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
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Paper:
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Title: Dynamic Multi-scale Filters for Semantic Segmentation
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URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
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Framework: PyTorch
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- Name: dmnet_r50-d8_4xb4-160k_ade20k-512x512
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In Collection: DMNet
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Results:
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Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 43.15
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mIoU(ms+flip): 44.17
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Config: configs/dmnet/dmnet_r50-d8_4xb4-160k_ade20k-512x512.py
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Metadata:
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Training Data: ADE20K
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Batch Size: 16
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Architecture:
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- R-50-D8
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- DMNet
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Training Resources: 4x V100 GPUS
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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
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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
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Paper:
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Title: Dynamic Multi-scale Filters for Semantic Segmentation
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URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
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Framework: PyTorch
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- Name: dmnet_r101-d8_4xb4-160k_ade20k-512x512
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In Collection: DMNet
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Results:
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Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 45.42
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mIoU(ms+flip): 46.76
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Config: configs/dmnet/dmnet_r101-d8_4xb4-160k_ade20k-512x512.py
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Metadata:
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Training Data: ADE20K
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Batch Size: 16
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Architecture:
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- R-101-D8
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- DMNet
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Training Resources: 4x V100 GPUS
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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
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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
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Paper:
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Title: Dynamic Multi-scale Filters for Semantic Segmentation
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URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
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Framework: PyTorch
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