392 lines
18 KiB
YAML
392 lines
18 KiB
YAML
Collections:
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- Name: PSANet
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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: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
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URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
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README: configs/psanet/README.md
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Frameworks:
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- PyTorch
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Models:
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- Name: psanet_r50-d8_4xb2-40k_cityscapes-512x1024
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In Collection: PSANet
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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: 77.63
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mIoU(ms+flip): 79.04
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Config: configs/psanet/psanet_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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- PSANet
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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/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117-99fac37c.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117.log.json
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Paper:
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Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
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URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
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Framework: PyTorch
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- Name: psanet_r101-d8_4xb2-40k_cityscapes-512x1024
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In Collection: PSANet
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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: 79.14
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mIoU(ms+flip): 80.19
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Config: configs/psanet/psanet_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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- PSANet
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Training Resources: 4x V100 GPUS
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Memory (GB): 10.5
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418-27b9cfa7.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418.log.json
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Paper:
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Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
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URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
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Framework: PyTorch
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- Name: psanet_r50-d8_4xb2-40k_cityscapes-769x769
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In Collection: PSANet
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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: 77.99
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mIoU(ms+flip): 79.64
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Config: configs/psanet/psanet_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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- PSANet
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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/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717-d5365506.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717.log.json
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Paper:
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Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
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URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
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Framework: PyTorch
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- Name: psanet_r101-d8_4xb2-40k_cityscapes-769x769
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In Collection: PSANet
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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: 78.43
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mIoU(ms+flip): 80.26
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Config: configs/psanet/psanet_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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- PSANet
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Training Resources: 4x V100 GPUS
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Memory (GB): 11.9
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107-997da1e6.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107.log.json
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Paper:
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Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
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URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
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Framework: PyTorch
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- Name: psanet_r50-d8_4xb2-80k_cityscapes-512x1024
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In Collection: PSANet
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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: 77.24
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mIoU(ms+flip): 78.69
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Config: configs/psanet/psanet_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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- PSANet
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Training Resources: 4x V100 GPUS
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842-ab60a24f.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842.log.json
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Paper:
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Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
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URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
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Framework: PyTorch
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- Name: psanet_r101-d8_4xb2-80k_cityscapes-512x1024
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In Collection: PSANet
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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: 79.31
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mIoU(ms+flip): 80.53
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Config: configs/psanet/psanet_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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- PSANet
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Training Resources: 4x V100 GPUS
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823-0f73a169.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823.log.json
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Paper:
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Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
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URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
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Framework: PyTorch
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- Name: psanet_r50-d8_4xb2-80k_cityscapes-769x769
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In Collection: PSANet
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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: 79.31
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mIoU(ms+flip): 80.91
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Config: configs/psanet/psanet_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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- PSANet
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Training Resources: 4x V100 GPUS
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134-fe42f49e.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134.log.json
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Paper:
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Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
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URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
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Framework: PyTorch
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- Name: psanet_r101-d8_4xb2-80k_cityscapes-769x769
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In Collection: PSANet
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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: 79.69
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mIoU(ms+flip): 80.89
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Config: configs/psanet/psanet_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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- PSANet
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Training Resources: 4x V100 GPUS
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550-7665827b.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550.log.json
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Paper:
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Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
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URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
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Framework: PyTorch
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- Name: psanet_r50-d8_4xb4-80k_ade20k-512x512
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In Collection: PSANet
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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: 41.14
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mIoU(ms+flip): 41.91
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Config: configs/psanet/psanet_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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- PSANet
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Training Resources: 4x V100 GPUS
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Memory (GB): 9.0
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141-835e4b97.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141.log.json
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Paper:
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Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
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URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
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Framework: PyTorch
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- Name: psanet_r101-d8_4xb4-80k_ade20k-512x512
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In Collection: PSANet
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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: 43.8
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mIoU(ms+flip): 44.75
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Config: configs/psanet/psanet_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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- PSANet
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Training Resources: 4x V100 GPUS
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Memory (GB): 12.5
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117-1fab60d4.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117.log.json
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Paper:
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Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
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URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
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Framework: PyTorch
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- Name: psanet_r50-d8_4xb4-160k_ade20k-512x512
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In Collection: PSANet
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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: 41.67
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mIoU(ms+flip): 42.95
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Config: configs/psanet/psanet_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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- PSANet
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Training Resources: 4x V100 GPUS
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258-148077dd.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258.log.json
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Paper:
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Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
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URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
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Framework: PyTorch
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- Name: psanet_r101-d8_4xb4-160k_ade20k-512x512
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In Collection: PSANet
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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: 43.74
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mIoU(ms+flip): 45.38
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Config: configs/psanet/psanet_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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- PSANet
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Training Resources: 4x V100 GPUS
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537-dbfa564c.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537.log.json
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Paper:
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Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
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URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
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Framework: PyTorch
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- Name: psanet_r50-d8_4xb4-20k_voc12aug-512x512
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In Collection: PSANet
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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: 76.39
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mIoU(ms+flip): 77.34
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Config: configs/psanet/psanet_r50-d8_4xb4-20k_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-50-D8
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- PSANet
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Training Resources: 4x V100 GPUS
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Memory (GB): 6.9
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413-2f1bbaa1.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413.log.json
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Paper:
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Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
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|
URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
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Framework: PyTorch
|
|
- Name: psanet_r101-d8_4xb4-20k_voc12aug-512x512
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In Collection: PSANet
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|
Results:
|
|
Task: Semantic Segmentation
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Dataset: Pascal VOC 2012 + Aug
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|
Metrics:
|
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mIoU: 77.91
|
|
mIoU(ms+flip): 79.3
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|
Config: configs/psanet/psanet_r101-d8_4xb4-20k_voc12aug-512x512.py
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|
Metadata:
|
|
Training Data: Pascal VOC 2012 + Aug
|
|
Batch Size: 16
|
|
Architecture:
|
|
- R-101-D8
|
|
- PSANet
|
|
Training Resources: 4x V100 GPUS
|
|
Memory (GB): 10.4
|
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624-946fef11.pth
|
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624.log.json
|
|
Paper:
|
|
Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
|
|
URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
|
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
|
|
Framework: PyTorch
|
|
- Name: psanet_r50-d8_4xb4-40k_voc12aug-512x512
|
|
In Collection: PSANet
|
|
Results:
|
|
Task: Semantic Segmentation
|
|
Dataset: Pascal VOC 2012 + Aug
|
|
Metrics:
|
|
mIoU: 76.3
|
|
mIoU(ms+flip): 77.35
|
|
Config: configs/psanet/psanet_r50-d8_4xb4-40k_voc12aug-512x512.py
|
|
Metadata:
|
|
Training Data: Pascal VOC 2012 + Aug
|
|
Batch Size: 16
|
|
Architecture:
|
|
- R-50-D8
|
|
- PSANet
|
|
Training Resources: 4x V100 GPUS
|
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946-f596afb5.pth
|
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946.log.json
|
|
Paper:
|
|
Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
|
|
URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
|
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
|
|
Framework: PyTorch
|
|
- Name: psanet_r101-d8_4xb4-40k_voc12aug-512x512
|
|
In Collection: PSANet
|
|
Results:
|
|
Task: Semantic Segmentation
|
|
Dataset: Pascal VOC 2012 + Aug
|
|
Metrics:
|
|
mIoU: 77.73
|
|
mIoU(ms+flip): 79.05
|
|
Config: configs/psanet/psanet_r101-d8_4xb4-40k_voc12aug-512x512.py
|
|
Metadata:
|
|
Training Data: Pascal VOC 2012 + Aug
|
|
Batch Size: 16
|
|
Architecture:
|
|
- R-101-D8
|
|
- PSANet
|
|
Training Resources: 4x V100 GPUS
|
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946-1f560f9e.pth
|
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946.log.json
|
|
Paper:
|
|
Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
|
|
URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
|
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
|
|
Framework: PyTorch
|