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Collections: |
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- Name: CCNet |
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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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- Pascal VOC 2012 + Aug |
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Paper: |
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Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1811.11721 |
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README: configs/ccnet/README.md |
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Frameworks: |
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- PyTorch |
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Models: |
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- Name: ccnet_r50-d8_4xb2-40k_cityscapes-512x1024 |
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In Collection: CCNet |
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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: 77.76 |
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mIoU(ms+flip): 78.87 |
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Config: configs/ccnet/ccnet_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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- CCNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 6.0 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517.log.json |
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Paper: |
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Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1811.11721 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
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Framework: PyTorch |
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- Name: ccnet_r101-d8_4xb2-40k_cityscapes-512x1024 |
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In Collection: CCNet |
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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: 76.35 |
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mIoU(ms+flip): 78.19 |
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Config: configs/ccnet/ccnet_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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- CCNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 9.5 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540.log.json |
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Paper: |
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Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1811.11721 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
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Framework: PyTorch |
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- Name: ccnet_r50-d8_4xb2-40k_cityscapes-769x769 |
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In Collection: CCNet |
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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: 78.46 |
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mIoU(ms+flip): 79.93 |
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Config: configs/ccnet/ccnet_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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- CCNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 6.8 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125.log.json |
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Paper: |
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Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1811.11721 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
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Framework: PyTorch |
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- Name: ccnet_r101-d8_4xb2-40k_cityscapes-769x769 |
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In Collection: CCNet |
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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: 76.94 |
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mIoU(ms+flip): 78.62 |
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Config: configs/ccnet/ccnet_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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- CCNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 10.7 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428.log.json |
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Paper: |
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Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1811.11721 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
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Framework: PyTorch |
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- Name: ccnet_r50-d8_4xb2-80k_cityscapes-512x1024 |
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In Collection: CCNet |
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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: 79.03 |
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mIoU(ms+flip): 80.16 |
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Config: configs/ccnet/ccnet_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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- CCNet |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421.log.json |
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Paper: |
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Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1811.11721 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
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Framework: PyTorch |
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- Name: ccnet_r101-d8_4xb2-80k_cityscapes-512x1024 |
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In Collection: CCNet |
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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: 78.87 |
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mIoU(ms+flip): 79.9 |
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Config: configs/ccnet/ccnet_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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- CCNet |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935.log.json |
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Paper: |
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Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
|
URL: https://arxiv.org/abs/1811.11721 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
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Framework: PyTorch |
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- Name: ccnet_r50-d8_4xb2-80k_cityscapes-769x769 |
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In Collection: CCNet |
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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: 79.29 |
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mIoU(ms+flip): 81.08 |
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Config: configs/ccnet/ccnet_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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- CCNet |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421.log.json |
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Paper: |
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Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
|
URL: https://arxiv.org/abs/1811.11721 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
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Framework: PyTorch |
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- Name: ccnet_r101-d8_4xb2-80k_cityscapes-769x769 |
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In Collection: CCNet |
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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: 79.45 |
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mIoU(ms+flip): 80.66 |
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Config: configs/ccnet/ccnet_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: |
|
- R-101-D8 |
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- CCNet |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502.log.json |
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Paper: |
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Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1811.11721 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
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Framework: PyTorch |
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- Name: ccnet_r50-d8_4xb4-80k_ade20k-512x512 |
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In Collection: CCNet |
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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: 41.78 |
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mIoU(ms+flip): 42.98 |
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Config: configs/ccnet/ccnet_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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- CCNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 8.8 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848.log.json |
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Paper: |
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Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1811.11721 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
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Framework: PyTorch |
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- Name: ccnet_r101-d8_4xb4-80k_ade20k-512x512 |
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In Collection: CCNet |
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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: 43.97 |
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mIoU(ms+flip): 45.13 |
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Config: configs/ccnet/ccnet_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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- CCNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 12.2 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848.log.json |
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Paper: |
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Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
|
URL: https://arxiv.org/abs/1811.11721 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
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Framework: PyTorch |
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- Name: ccnet_r50-d8_4xb4-160k_ade20k-512x512 |
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In Collection: CCNet |
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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: 42.08 |
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mIoU(ms+flip): 43.13 |
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Config: configs/ccnet/ccnet_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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- CCNet |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435.log.json |
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Paper: |
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Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
|
URL: https://arxiv.org/abs/1811.11721 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
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Framework: PyTorch |
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- Name: ccnet_r101-d8_4xb4-160k_ade20k-512x512 |
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In Collection: CCNet |
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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: 43.71 |
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mIoU(ms+flip): 45.04 |
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Config: configs/ccnet/ccnet_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: |
|
- R-101-D8 |
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- CCNet |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644.log.json |
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Paper: |
|
Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
|
URL: https://arxiv.org/abs/1811.11721 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
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Framework: PyTorch |
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- Name: ccnet_r50-d8_4xb4-20k_voc12aug-512x512 |
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In Collection: CCNet |
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Results: |
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Task: Semantic Segmentation |
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Dataset: Pascal VOC 2012 + Aug |
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Metrics: |
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mIoU: 76.17 |
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mIoU(ms+flip): 77.51 |
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Config: configs/ccnet/ccnet_r50-d8_4xb4-20k_voc12aug-512x512.py |
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Metadata: |
|
Training Data: Pascal VOC 2012 + Aug |
|
Batch Size: 16 |
|
Architecture: |
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- R-50-D8 |
|
- CCNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 6.0 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212.log.json |
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Paper: |
|
Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
|
URL: https://arxiv.org/abs/1811.11721 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
|
Framework: PyTorch |
|
- Name: ccnet_r101-d8_4xb4-20k_voc12aug-512x512 |
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In Collection: CCNet |
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Results: |
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Task: Semantic Segmentation |
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Dataset: Pascal VOC 2012 + Aug |
|
Metrics: |
|
mIoU: 77.27 |
|
mIoU(ms+flip): 79.02 |
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Config: configs/ccnet/ccnet_r101-d8_4xb4-20k_voc12aug-512x512.py |
|
Metadata: |
|
Training Data: Pascal VOC 2012 + Aug |
|
Batch Size: 16 |
|
Architecture: |
|
- R-101-D8 |
|
- CCNet |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 9.5 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212.log.json |
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Paper: |
|
Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
|
URL: https://arxiv.org/abs/1811.11721 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
|
Framework: PyTorch |
|
- Name: ccnet_r50-d8_4xb4-40k_voc12aug-512x512 |
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In Collection: CCNet |
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Results: |
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Task: Semantic Segmentation |
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Dataset: Pascal VOC 2012 + Aug |
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Metrics: |
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mIoU: 75.96 |
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mIoU(ms+flip): 77.04 |
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Config: configs/ccnet/ccnet_r50-d8_4xb4-40k_voc12aug-512x512.py |
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Metadata: |
|
Training Data: Pascal VOC 2012 + Aug |
|
Batch Size: 16 |
|
Architecture: |
|
- R-50-D8 |
|
- CCNet |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127.log.json |
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Paper: |
|
Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
|
URL: https://arxiv.org/abs/1811.11721 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
|
Framework: PyTorch |
|
- Name: ccnet_r101-d8_4xb4-40k_voc12aug-512x512 |
|
In Collection: CCNet |
|
Results: |
|
Task: Semantic Segmentation |
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Dataset: Pascal VOC 2012 + Aug |
|
Metrics: |
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mIoU: 77.87 |
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mIoU(ms+flip): 78.9 |
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Config: configs/ccnet/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py |
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Metadata: |
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Training Data: Pascal VOC 2012 + Aug |
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Batch Size: 16 |
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Architecture: |
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- R-101-D8 |
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- CCNet |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127.log.json |
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Paper: |
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Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1811.11721 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 |
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Framework: PyTorch |
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