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Collections: |
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- Name: DeepLabV3 |
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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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- Pascal Context |
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- Pascal Context 59 |
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- COCO-Stuff 10k |
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- COCO-Stuff 164k |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
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README: configs/deeplabv3/README.md |
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Frameworks: |
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- PyTorch |
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Models: |
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- Name: deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024 |
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In Collection: DeepLabV3 |
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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: 79.09 |
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mIoU(ms+flip): 80.45 |
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Config: configs/deeplabv3/deeplabv3_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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- DeepLabV3 |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 6.1 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449.log.json |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
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Framework: PyTorch |
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- Name: deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024 |
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In Collection: DeepLabV3 |
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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: 77.12 |
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mIoU(ms+flip): 79.61 |
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Config: configs/deeplabv3/deeplabv3_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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- DeepLabV3 |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 9.6 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241.log.json |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
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Framework: PyTorch |
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- Name: deeplabv3_r50-d8_4xb2-40k_cityscapes-769x769 |
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In Collection: DeepLabV3 |
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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: 78.58 |
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mIoU(ms+flip): 79.89 |
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Config: configs/deeplabv3/deeplabv3_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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- DeepLabV3 |
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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/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723.log.json |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
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Framework: PyTorch |
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- Name: deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769 |
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In Collection: DeepLabV3 |
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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: 79.27 |
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mIoU(ms+flip): 80.11 |
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Config: configs/deeplabv3/deeplabv3_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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- DeepLabV3 |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 10.9 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809.log.json |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
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Framework: PyTorch |
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- Name: deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024 |
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In Collection: DeepLabV3 |
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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: 76.7 |
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mIoU(ms+flip): 78.27 |
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Config: configs/deeplabv3/deeplabv3_r18-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-18-D8 |
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- DeepLabV3 |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 1.7 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes-20201225_021506.log.json |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
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Framework: PyTorch |
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- Name: deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024 |
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In Collection: DeepLabV3 |
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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: 79.32 |
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mIoU(ms+flip): 80.57 |
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Config: configs/deeplabv3/deeplabv3_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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- DeepLabV3 |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404.log.json |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
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Framework: PyTorch |
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- Name: deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024 |
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In Collection: DeepLabV3 |
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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: 80.2 |
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mIoU(ms+flip): 81.21 |
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Config: configs/deeplabv3/deeplabv3_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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- DeepLabV3 |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503.log.json |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
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Framework: PyTorch |
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- Name: deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024 |
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In Collection: DeepLabV3 |
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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: 80.48 |
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Config: configs/deeplabv3/deeplabv3_r101-d8_4xb2-amp-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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- DeepLabV3 |
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- (FP16) |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 5.75 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-774d9cec.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920.log.json |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
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Framework: PyTorch |
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- Name: deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769 |
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In Collection: DeepLabV3 |
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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: 76.6 |
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mIoU(ms+flip): 78.26 |
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Config: configs/deeplabv3/deeplabv3_r18-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-18-D8 |
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- DeepLabV3 |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 1.9 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes-20201225_021506.log.json |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
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Framework: PyTorch |
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- Name: deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769 |
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In Collection: DeepLabV3 |
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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: 79.89 |
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mIoU(ms+flip): 81.06 |
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Config: configs/deeplabv3/deeplabv3_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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- DeepLabV3 |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338.log.json |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
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Framework: PyTorch |
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- Name: deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769 |
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In Collection: DeepLabV3 |
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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: 79.67 |
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mIoU(ms+flip): 80.81 |
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Config: configs/deeplabv3/deeplabv3_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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- DeepLabV3 |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353.log.json |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
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Framework: PyTorch |
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- Name: deeplabv3_r101-d16-mg124_4xb2-40k_cityscapes-512x1024 |
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In Collection: DeepLabV3 |
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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: 76.71 |
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mIoU(ms+flip): 78.63 |
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Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_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-D16-MG124 |
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- DeepLabV3 |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 4.7 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-67b0c992.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r101-d16-mg124_4xb2-80k_cityscapes-512x1024 |
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In Collection: DeepLabV3 |
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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: 78.36 |
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mIoU(ms+flip): 79.84 |
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Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py |
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Metadata: |
|
Training Data: Cityscapes |
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Batch Size: 8 |
|
Architecture: |
|
- R-101-D16-MG124 |
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- DeepLabV3 |
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Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
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Framework: PyTorch |
|
- Name: deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024 |
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In Collection: DeepLabV3 |
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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: 76.26 |
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mIoU(ms+flip): 77.88 |
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Config: configs/deeplabv3/deeplabv3_r18b-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-18b-D8 |
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- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
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Memory (GB): 1.6 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes-20201225_094144.log.json |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024 |
|
In Collection: DeepLabV3 |
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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: 79.63 |
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mIoU(ms+flip): 80.98 |
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Config: configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024.py |
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Metadata: |
|
Training Data: Cityscapes |
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Batch Size: 8 |
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Architecture: |
|
- R-50b-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 6.0 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes-20201225_155148.log.json |
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Paper: |
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Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
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Framework: PyTorch |
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- Name: deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024 |
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In Collection: DeepLabV3 |
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Results: |
|
Task: Semantic Segmentation |
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Dataset: Cityscapes |
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Metrics: |
|
mIoU: 80.01 |
|
mIoU(ms+flip): 81.21 |
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Config: configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024.py |
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Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-101b-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 9.5 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes-20201226_171821.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769 |
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In Collection: DeepLabV3 |
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Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 75.63 |
|
mIoU(ms+flip): 77.51 |
|
Config: configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769.py |
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Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-18b-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 1.8 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes-20201225_094144.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 78.8 |
|
mIoU(ms+flip): 80.27 |
|
Config: configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-50b-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 6.8 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes-20201225_155404.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 79.41 |
|
mIoU(ms+flip): 80.73 |
|
Config: configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-101b-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 10.7 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes-20201226_190843.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r50-d8_4xb4-80k_ade20k-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: ADE20K |
|
Metrics: |
|
mIoU: 42.42 |
|
mIoU(ms+flip): 43.28 |
|
Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_ade20k-512x512.py |
|
Metadata: |
|
Training Data: ADE20K |
|
Batch Size: 16 |
|
Architecture: |
|
- R-50-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 8.9 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r101-d8_4xb4-80k_ade20k-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: ADE20K |
|
Metrics: |
|
mIoU: 44.08 |
|
mIoU(ms+flip): 45.19 |
|
Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_ade20k-512x512.py |
|
Metadata: |
|
Training Data: ADE20K |
|
Batch Size: 16 |
|
Architecture: |
|
- R-101-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 12.4 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r50-d8_4xb4-160k_ade20k-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: ADE20K |
|
Metrics: |
|
mIoU: 42.66 |
|
mIoU(ms+flip): 44.09 |
|
Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-160k_ade20k-512x512.py |
|
Metadata: |
|
Training Data: ADE20K |
|
Batch Size: 16 |
|
Architecture: |
|
- R-50-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r101-d8_4xb4-160k_ade20k-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: ADE20K |
|
Metrics: |
|
mIoU: 45.0 |
|
mIoU(ms+flip): 46.66 |
|
Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py |
|
Metadata: |
|
Training Data: ADE20K |
|
Batch Size: 16 |
|
Architecture: |
|
- R-101-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal VOC 2012 + Aug |
|
Metrics: |
|
mIoU: 76.17 |
|
mIoU(ms+flip): 77.42 |
|
Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512.py |
|
Metadata: |
|
Training Data: Pascal VOC 2012 + Aug |
|
Batch Size: 16 |
|
Architecture: |
|
- R-50-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 6.1 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal VOC 2012 + Aug |
|
Metrics: |
|
mIoU: 78.7 |
|
mIoU(ms+flip): 79.95 |
|
Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py |
|
Metadata: |
|
Training Data: Pascal VOC 2012 + Aug |
|
Batch Size: 16 |
|
Architecture: |
|
- R-101-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 9.6 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal VOC 2012 + Aug |
|
Metrics: |
|
mIoU: 77.68 |
|
mIoU(ms+flip): 78.78 |
|
Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512.py |
|
Metadata: |
|
Training Data: Pascal VOC 2012 + Aug |
|
Batch Size: 16 |
|
Architecture: |
|
- R-50-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal VOC 2012 + Aug |
|
Metrics: |
|
mIoU: 77.92 |
|
mIoU(ms+flip): 79.18 |
|
Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512.py |
|
Metadata: |
|
Training Data: Pascal VOC 2012 + Aug |
|
Batch Size: 16 |
|
Architecture: |
|
- R-101-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal Context |
|
Metrics: |
|
mIoU: 46.55 |
|
mIoU(ms+flip): 47.81 |
|
Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480.py |
|
Metadata: |
|
Training Data: Pascal Context |
|
Batch Size: 16 |
|
Architecture: |
|
- R-101-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 9.2 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context-20200911_204118.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal Context |
|
Metrics: |
|
mIoU: 46.42 |
|
mIoU(ms+flip): 47.53 |
|
Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480.py |
|
Metadata: |
|
Training Data: Pascal Context |
|
Batch Size: 16 |
|
Architecture: |
|
- R-101-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context-20200911_170155.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480 |
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In Collection: DeepLabV3 |
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Results: |
|
Task: Semantic Segmentation |
|
Dataset: Pascal Context 59 |
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Metrics: |
|
mIoU: 52.61 |
|
mIoU(ms+flip): 54.28 |
|
Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py |
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Metadata: |
|
Training Data: Pascal Context 59 |
|
Batch Size: 16 |
|
Architecture: |
|
- R-101-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59-20210416_110332.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
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URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480 |
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In Collection: DeepLabV3 |
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Results: |
|
Task: Semantic Segmentation |
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Dataset: Pascal Context 59 |
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Metrics: |
|
mIoU: 52.46 |
|
mIoU(ms+flip): 54.09 |
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Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480.py |
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Metadata: |
|
Training Data: Pascal Context 59 |
|
Batch Size: 16 |
|
Architecture: |
|
- R-101-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59-20210416_113002.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512 |
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In Collection: DeepLabV3 |
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Results: |
|
Task: Semantic Segmentation |
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Dataset: COCO-Stuff 10k |
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Metrics: |
|
mIoU: 34.66 |
|
mIoU(ms+flip): 36.08 |
|
Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512.py |
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Metadata: |
|
Training Data: COCO-Stuff 10k |
|
Batch Size: 16 |
|
Architecture: |
|
- R-50-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 9.6 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-b35f789d.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: COCO-Stuff 10k |
|
Metrics: |
|
mIoU: 37.3 |
|
mIoU(ms+flip): 38.42 |
|
Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512.py |
|
Metadata: |
|
Training Data: COCO-Stuff 10k |
|
Batch Size: 16 |
|
Architecture: |
|
- R-101-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 13.2 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-c49752cb.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: COCO-Stuff 10k |
|
Metrics: |
|
mIoU: 35.73 |
|
mIoU(ms+flip): 37.09 |
|
Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py |
|
Metadata: |
|
Training Data: COCO-Stuff 10k |
|
Batch Size: 16 |
|
Architecture: |
|
- R-50-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-dc76f3ff.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: COCO-Stuff 10k |
|
Metrics: |
|
mIoU: 37.81 |
|
mIoU(ms+flip): 38.8 |
|
Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512.py |
|
Metadata: |
|
Training Data: COCO-Stuff 10k |
|
Batch Size: 16 |
|
Architecture: |
|
- R-101-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-636cb433.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r50-d8_4xb4-80k_coco-stuff164k-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: COCO-Stuff 164k |
|
Metrics: |
|
mIoU: 39.38 |
|
mIoU(ms+flip): 40.03 |
|
Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_coco-stuff164k-512x512.py |
|
Metadata: |
|
Training Data: COCO-Stuff 164k |
|
Batch Size: 16 |
|
Architecture: |
|
- R-50-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 9.6 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016-88675c24.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: COCO-Stuff 164k |
|
Metrics: |
|
mIoU: 40.87 |
|
mIoU(ms+flip): 41.5 |
|
Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512.py |
|
Metadata: |
|
Training Data: COCO-Stuff 164k |
|
Batch Size: 16 |
|
Architecture: |
|
- R-101-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 13.2 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252-13600dc2.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r50-d8_4xb4-160k_coco-stuff164k-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: COCO-Stuff 164k |
|
Metrics: |
|
mIoU: 41.09 |
|
mIoU(ms+flip): 41.69 |
|
Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-160k_coco-stuff164k-512x512.py |
|
Metadata: |
|
Training Data: COCO-Stuff 164k |
|
Batch Size: 16 |
|
Architecture: |
|
- R-50-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016-49f2812b.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: COCO-Stuff 164k |
|
Metrics: |
|
mIoU: 41.82 |
|
mIoU(ms+flip): 42.49 |
|
Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512.py |
|
Metadata: |
|
Training Data: COCO-Stuff 164k |
|
Batch Size: 16 |
|
Architecture: |
|
- R-101-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402-f035acfd.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: COCO-Stuff 164k |
|
Metrics: |
|
mIoU: 41.37 |
|
mIoU(ms+flip): 42.22 |
|
Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py |
|
Metadata: |
|
Training Data: COCO-Stuff 164k |
|
Batch Size: 16 |
|
Architecture: |
|
- R-50-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403-51b21115.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
- Name: deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512 |
|
In Collection: DeepLabV3 |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: COCO-Stuff 164k |
|
Metrics: |
|
mIoU: 42.61 |
|
mIoU(ms+flip): 43.42 |
|
Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512.py |
|
Metadata: |
|
Training Data: COCO-Stuff 164k |
|
Batch Size: 16 |
|
Architecture: |
|
- R-101-D8 |
|
- DeepLabV3 |
|
Training Resources: 4x V100 GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402-3cbca14d.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402.log.json |
|
Paper: |
|
Title: Rethinking atrous convolution for semantic image segmentation |
|
URL: https://arxiv.org/abs/1706.05587 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 |
|
Framework: PyTorch |
|
|