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Collections: |
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- Name: APCNet |
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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: Adaptive Pyramid Context Network for Semantic Segmentation |
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URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html |
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README: configs/apcnet/README.md |
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Frameworks: |
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- PyTorch |
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Models: |
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- Name: apcnet_r50-d8_4xb2-40k_cityscapes-512x1024 |
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In Collection: APCNet |
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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: 78.02 |
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mIoU(ms+flip): 79.26 |
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Config: configs/apcnet/apcnet_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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- APCNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 7.7 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes-20201214_115717.log.json |
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Paper: |
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Title: Adaptive Pyramid Context Network for Semantic Segmentation |
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URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html |
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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: apcnet_r101-d8_4xb2-40k_cityscapes-512x1024 |
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In Collection: APCNet |
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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: 79.08 |
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mIoU(ms+flip): 80.34 |
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Config: configs/apcnet/apcnet_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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- APCNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 11.2 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes-20201214_115716.log.json |
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Paper: |
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Title: Adaptive Pyramid Context Network for Semantic Segmentation |
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URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html |
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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: apcnet_r50-d8_4xb2-40k_cityscapes-769x769 |
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In Collection: APCNet |
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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: 77.89 |
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mIoU(ms+flip): 79.75 |
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Config: configs/apcnet/apcnet_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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- APCNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 8.7 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes-20201214_115717.log.json |
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Paper: |
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Title: Adaptive Pyramid Context Network for Semantic Segmentation |
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URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html |
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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: apcnet_r101-d8_4xb2-40k_cityscapes-769x769 |
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In Collection: APCNet |
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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: 77.96 |
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mIoU(ms+flip): 79.24 |
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Config: configs/apcnet/apcnet_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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- APCNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 12.7 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes-20201214_115718.log.json |
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Paper: |
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Title: Adaptive Pyramid Context Network for Semantic Segmentation |
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URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html |
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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: apcnet_r50-d8_4xb2-80k_cityscapes-512x1024 |
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In Collection: APCNet |
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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: 78.96 |
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mIoU(ms+flip): 79.94 |
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Config: configs/apcnet/apcnet_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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- APCNet |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes-20201214_115716.log.json |
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Paper: |
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Title: Adaptive Pyramid Context Network for Semantic Segmentation |
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URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html |
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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: apcnet_r101-d8_4xb2-80k_cityscapes-512x1024 |
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In Collection: APCNet |
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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): 80.61 |
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Config: configs/apcnet/apcnet_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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- APCNet |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes-20201214_115705.log.json |
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Paper: |
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Title: Adaptive Pyramid Context Network for Semantic Segmentation |
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URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html |
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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: apcnet_r50-d8_4xb2-80k_cityscapes-769x769 |
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In Collection: APCNet |
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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: 78.79 |
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mIoU(ms+flip): 80.35 |
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Config: configs/apcnet/apcnet_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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- APCNet |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes-20201214_115718.log.json |
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Paper: |
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Title: Adaptive Pyramid Context Network for Semantic Segmentation |
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URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html |
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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: apcnet_r101-d8_4xb2-80k_cityscapes-769x769 |
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In Collection: APCNet |
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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: 78.45 |
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mIoU(ms+flip): 79.91 |
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Config: configs/apcnet/apcnet_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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- APCNet |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes-20201214_115716.log.json |
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Paper: |
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Title: Adaptive Pyramid Context Network for Semantic Segmentation |
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URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html |
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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: apcnet_r50-d8_4xb4-80k_ade20k-512x512 |
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In Collection: APCNet |
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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: 42.2 |
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mIoU(ms+flip): 43.3 |
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Config: configs/apcnet/apcnet_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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- APCNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 10.1 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k-20201214_115705.log.json |
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Paper: |
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Title: Adaptive Pyramid Context Network for Semantic Segmentation |
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URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html |
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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: apcnet_r101-d8_4xb4-80k_ade20k-512x512 |
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In Collection: APCNet |
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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: 45.54 |
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mIoU(ms+flip): 46.65 |
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Config: configs/apcnet/apcnet_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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- APCNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 13.6 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k-20201214_115704.log.json |
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Paper: |
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Title: Adaptive Pyramid Context Network for Semantic Segmentation |
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URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html |
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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: apcnet_r50-d8_4xb4-160k_ade20k-512x512 |
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In Collection: APCNet |
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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: 43.4 |
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mIoU(ms+flip): 43.94 |
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Config: configs/apcnet/apcnet_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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- APCNet |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k-20201214_115706.log.json |
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Paper: |
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Title: Adaptive Pyramid Context Network for Semantic Segmentation |
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URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html |
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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: apcnet_r101-d8_4xb4-160k_ade20k-512x512 |
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In Collection: APCNet |
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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: 45.41 |
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mIoU(ms+flip): 46.63 |
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Config: configs/apcnet/apcnet_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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- APCNet |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k-20201214_115705.log.json |
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Paper: |
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Title: Adaptive Pyramid Context Network for Semantic Segmentation |
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URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html |
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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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