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Collections: |
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- Name: FCN |
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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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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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README: configs/fcn/README.md |
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Frameworks: |
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- PyTorch |
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Models: |
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- Name: fcn_r50-d8_4xb2-40k_cityscapes-512x1024 |
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In Collection: FCN |
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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: 72.25 |
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mIoU(ms+flip): 73.36 |
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Config: configs/fcn/fcn_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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- FCN |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 5.7 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r101-d8_4xb2-40k_cityscapes-512x1024 |
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In Collection: FCN |
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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: 75.45 |
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mIoU(ms+flip): 76.58 |
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Config: configs/fcn/fcn_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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- FCN |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 9.2 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r50-d8_4xb2-40k_cityscapes-769x769 |
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In Collection: FCN |
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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: 71.47 |
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mIoU(ms+flip): 72.54 |
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Config: configs/fcn/fcn_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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- FCN |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 6.5 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r101-d8_4xb2-40k_cityscapes-769x769 |
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In Collection: FCN |
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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: 73.93 |
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mIoU(ms+flip): 75.14 |
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Config: configs/fcn/fcn_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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- FCN |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 10.4 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r18-d8_4xb2-80k_cityscapes-512x1024 |
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In Collection: FCN |
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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: 71.11 |
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mIoU(ms+flip): 72.91 |
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Config: configs/fcn/fcn_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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- FCN |
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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/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes-20201225_021327.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r50-d8_4xb2-80k_cityscapes-512x1024 |
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In Collection: FCN |
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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: 73.61 |
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mIoU(ms+flip): 74.24 |
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Config: configs/fcn/fcn_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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- FCN |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r101-d8_4xb2-80k_cityscapes-512x1024 |
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In Collection: FCN |
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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: 75.13 |
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mIoU(ms+flip): 75.94 |
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Config: configs/fcn/fcn_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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- FCN |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024 |
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In Collection: FCN |
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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: 76.8 |
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Config: configs/fcn/fcn_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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- FCN |
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- (FP16) |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 5.37 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes/fcn_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230921-fb13e883.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes/fcn_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230921.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r18-d8_4xb2-80k_cityscapes-769x769 |
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In Collection: FCN |
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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: 70.8 |
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mIoU(ms+flip): 73.16 |
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Config: configs/fcn/fcn_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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- FCN |
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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/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes-20201225_021451.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r50-d8_4xb2-80k_cityscapes-769x769 |
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In Collection: FCN |
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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: 72.64 |
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mIoU(ms+flip): 73.32 |
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Config: configs/fcn/fcn_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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- FCN |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r101-d8_4xb2-80k_cityscapes-769x769 |
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In Collection: FCN |
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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: 75.52 |
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mIoU(ms+flip): 76.61 |
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Config: configs/fcn/fcn_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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- FCN |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r18b-d8_4xb2-80k_cityscapes-512x1024 |
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In Collection: FCN |
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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: 70.24 |
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mIoU(ms+flip): 72.77 |
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Config: configs/fcn/fcn_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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- FCN |
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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/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes-20201225_230143.log.json |
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Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r50b-d8_4xb2-80k_cityscapes-512x1024 |
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In Collection: FCN |
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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: 75.65 |
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mIoU(ms+flip): 77.59 |
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Config: configs/fcn/fcn_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 |
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- FCN |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 5.6 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes-20201225_094221.log.json |
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Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn_r101b-d8_4xb2-80k_cityscapes-512x1024 |
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In Collection: FCN |
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Results: |
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Task: Semantic Segmentation |
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Dataset: Cityscapes |
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Metrics: |
|
mIoU: 77.37 |
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mIoU(ms+flip): 78.77 |
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Config: configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-512x1024.py |
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Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-101b-D8 |
|
- FCN |
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Training Resources: 4x V100 GPUS |
|
Memory (GB): 9.1 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes-20201226_160213.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
|
- Name: fcn_r18b-d8_4xb2-80k_cityscapes-769x769 |
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In Collection: FCN |
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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: 69.66 |
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mIoU(ms+flip): 72.07 |
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Config: configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-769x769.py |
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Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-18b-D8 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 1.7 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes-20201226_004430.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn_r50b-d8_4xb2-80k_cityscapes-769x769 |
|
In Collection: FCN |
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Results: |
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Task: Semantic Segmentation |
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Dataset: Cityscapes |
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Metrics: |
|
mIoU: 73.83 |
|
mIoU(ms+flip): 76.6 |
|
Config: configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-769x769.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-50b-D8 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 6.3 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes-20201225_094223.log.json |
|
Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn_r101b-d8_4xb2-80k_cityscapes-769x769 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 77.02 |
|
mIoU(ms+flip): 78.67 |
|
Config: configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-769x769.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-101b-D8 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 10.3 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes-20201226_170012.log.json |
|
Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 77.06 |
|
mIoU(ms+flip): 78.85 |
|
Config: configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-50-D16 |
|
- FCN |
|
- (D6) |
|
Training Resources: 4x TITAN Xp GPUS |
|
Memory (GB): 3.4 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes_20210305_130133-98d5d1bc.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-20210305_130133.log.json |
|
Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 77.27 |
|
mIoU(ms+flip): 78.88 |
|
Config: configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-50-D16 |
|
- FCN |
|
- (D6) |
|
Training Resources: 4x TITAN Xp GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes_20210306_115604-133c292f.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes-20210306_115604.log.json |
|
Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 76.82 |
|
mIoU(ms+flip): 78.22 |
|
Config: configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-50-D16 |
|
- FCN |
|
- (D6) |
|
Training Resources: 4x TITAN Xp GPUS |
|
Memory (GB): 3.7 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes_20210305_185744-1aab18ed.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-20210305_185744.log.json |
|
Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 77.04 |
|
mIoU(ms+flip): 78.4 |
|
Config: configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-50-D16 |
|
- FCN |
|
- (D6) |
|
Training Resources: 4x TITAN Xp GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes_20210305_200413-109d88eb.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-20210305_200413.log.json |
|
Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 77.36 |
|
mIoU(ms+flip): 79.18 |
|
Config: configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-101-D16 |
|
- FCN |
|
- (D6) |
|
Training Resources: 4x TITAN Xp GPUS |
|
Memory (GB): 4.5 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes_20210305_130337-9cf2b450.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-20210305_130337.log.json |
|
Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 78.46 |
|
mIoU(ms+flip): 80.42 |
|
Config: configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-101-D16 |
|
- FCN |
|
- (D6) |
|
Training Resources: 4x TITAN Xp GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes_20210308_102747-cb336445.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-20210308_102747.log.json |
|
Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 77.28 |
|
mIoU(ms+flip): 78.95 |
|
Config: configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-101-D16 |
|
- FCN |
|
- (D6) |
|
Training Resources: 4x TITAN Xp GPUS |
|
Memory (GB): 5.0 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes_20210308_102453-60b114e9.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-20210308_102453.log.json |
|
Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 78.06 |
|
mIoU(ms+flip): 79.58 |
|
Config: configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-101-D16 |
|
- FCN |
|
- (D6) |
|
Training Resources: 4x TITAN Xp GPUS |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes_20210306_120016-e33adc4f.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-20210306_120016.log.json |
|
Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 76.99 |
|
mIoU(ms+flip): 79.03 |
|
Config: configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-50b-D16 |
|
- FCN |
|
- (D6) |
|
Training Resources: 4x TITAN Xp GPUS |
|
Memory (GB): 3.2 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes/fcn_d6_r50b-d16_512x1024_80k_cityscapes_20210311_125550-6a0b62e9.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-20210311_125550.log.json |
|
Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 76.86 |
|
mIoU(ms+flip): 78.52 |
|
Config: configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-50b-D16 |
|
- FCN |
|
- (D6) |
|
Training Resources: 4x TITAN Xp GPUS |
|
Memory (GB): 3.6 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes/fcn_d6_r50b-d16_769x769_80k_cityscapes_20210311_131012-d665f231.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-20210311_131012.log.json |
|
Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 77.72 |
|
mIoU(ms+flip): 79.53 |
|
Config: configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-101b-D16 |
|
- FCN |
|
- (D6) |
|
Training Resources: 4x TITAN Xp GPUS |
|
Memory (GB): 4.3 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes/fcn_d6_r101b-d16_512x1024_80k_cityscapes_20210311_144305-3f2eb5b4.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-20210311_144305.log.json |
|
Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: Cityscapes |
|
Metrics: |
|
mIoU: 77.34 |
|
mIoU(ms+flip): 78.91 |
|
Config: configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769.py |
|
Metadata: |
|
Training Data: Cityscapes |
|
Batch Size: 8 |
|
Architecture: |
|
- R-101b-D16 |
|
- FCN |
|
- (D6) |
|
Training Resources: 4x TITAN Xp GPUS |
|
Memory (GB): 4.8 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes/fcn_d6_r101b-d16_769x769_80k_cityscapes_20210311_154527-c4d8bfbc.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-20210311_154527.log.json |
|
Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn_r50-d8_4xb4-80k_ade20k-512x512 |
|
In Collection: FCN |
|
Results: |
|
Task: Semantic Segmentation |
|
Dataset: ADE20K |
|
Metrics: |
|
mIoU: 35.94 |
|
mIoU(ms+flip): 37.94 |
|
Config: configs/fcn/fcn_r50-d8_4xb4-80k_ade20k-512x512.py |
|
Metadata: |
|
Training Data: ADE20K |
|
Batch Size: 16 |
|
Architecture: |
|
- R-50-D8 |
|
- FCN |
|
Training Resources: 4x V100 GPUS |
|
Memory (GB): 8.5 |
|
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth |
|
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016.log.json |
|
Paper: |
|
Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
|
Framework: PyTorch |
|
- Name: fcn_r101-d8_4xb4-80k_ade20k-512x512 |
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In Collection: FCN |
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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: 39.61 |
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mIoU(ms+flip): 40.83 |
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Config: configs/fcn/fcn_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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- FCN |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 12.0 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r50-d8_4xb4-160k_ade20k-512x512 |
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In Collection: FCN |
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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: 36.1 |
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mIoU(ms+flip): 38.08 |
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Config: configs/fcn/fcn_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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- FCN |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r101-d8_4xb4-160k_ade20k-512x512 |
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In Collection: FCN |
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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: 39.91 |
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mIoU(ms+flip): 41.4 |
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Config: configs/fcn/fcn_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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- FCN |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r50-d8_4xb4-20k_voc12aug-512x512 |
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In Collection: FCN |
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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: 67.08 |
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mIoU(ms+flip): 69.94 |
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Config: configs/fcn/fcn_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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- FCN |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 5.7 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r101-d8_4xb4-20k_voc12aug-512x512 |
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In Collection: FCN |
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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: 71.16 |
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mIoU(ms+flip): 73.57 |
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Config: configs/fcn/fcn_r101-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-101-D8 |
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- FCN |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 9.2 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r50-d8_4xb4-40k_voc12aug-512x512 |
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In Collection: FCN |
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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: 66.97 |
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mIoU(ms+flip): 69.04 |
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Config: configs/fcn/fcn_r50-d8_4xb4-40k_voc12aug-512x512.py |
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Metadata: |
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Training Data: Pascal VOC 2012 + Aug |
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Batch Size: 16 |
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Architecture: |
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- R-50-D8 |
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- FCN |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r101-d8_4xb4-40k_voc12aug-512x512 |
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In Collection: FCN |
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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: 69.91 |
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mIoU(ms+flip): 72.38 |
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Config: configs/fcn/fcn_r101-d8_4xb4-40k_voc12aug-512x512.py |
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Metadata: |
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Training Data: Pascal VOC 2012 + Aug |
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Batch Size: 16 |
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Architecture: |
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- R-101-D8 |
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- FCN |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r101-d8_4xb4-40k_pascal-context-480x480 |
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In Collection: FCN |
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Results: |
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Task: Semantic Segmentation |
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Dataset: Pascal Context |
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Metrics: |
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mIoU: 44.43 |
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mIoU(ms+flip): 45.63 |
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Config: configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-480x480.py |
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Metadata: |
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Training Data: Pascal Context |
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Batch Size: 16 |
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Architecture: |
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- R-101-D8 |
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- FCN |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context_20210421_154757-b5e97937.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r101-d8_4xb4-80k_pascal-context-480x480 |
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In Collection: FCN |
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Results: |
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Task: Semantic Segmentation |
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Dataset: Pascal Context |
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Metrics: |
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mIoU: 44.13 |
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mIoU(ms+flip): 45.26 |
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Config: configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-480x480.py |
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Metadata: |
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Training Data: Pascal Context |
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Batch Size: 16 |
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Architecture: |
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- R-101-D8 |
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- FCN |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context_20210421_163310-4711813f.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r101-d8_4xb4-40k_pascal-context-59-480x480 |
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In Collection: FCN |
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Results: |
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Task: Semantic Segmentation |
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Dataset: Pascal Context 59 |
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Metrics: |
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mIoU: 48.42 |
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mIoU(ms+flip): 50.4 |
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Config: configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-59-480x480.py |
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Metadata: |
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Training Data: Pascal Context 59 |
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Batch Size: 16 |
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Architecture: |
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- R-101-D8 |
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- FCN |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59-20210415_230724.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
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URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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- Name: fcn_r101-d8_4xb4-80k_pascal-context-59-480x480 |
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In Collection: FCN |
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Results: |
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Task: Semantic Segmentation |
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Dataset: Pascal Context 59 |
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Metrics: |
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mIoU: 49.35 |
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mIoU(ms+flip): 51.38 |
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Config: configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-59-480x480.py |
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Metadata: |
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Training Data: Pascal Context 59 |
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Batch Size: 16 |
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Architecture: |
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- R-101-D8 |
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- FCN |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59-20210416_110804.log.json |
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Paper: |
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Title: Fully Convolutional Networks for Semantic Segmentation |
|
URL: https://arxiv.org/abs/1411.4038 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 |
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Framework: PyTorch |
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