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Collections: |
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- Name: FastFCN |
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License: Apache License 2.0 |
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Metadata: |
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Training Data: |
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- Cityscapes |
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- ADE20K |
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Paper: |
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Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1903.11816 |
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README: configs/fastfcn/README.md |
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Frameworks: |
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- PyTorch |
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Models: |
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- Name: fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024 |
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In Collection: FastFCN |
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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: 79.12 |
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mIoU(ms+flip): 80.58 |
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_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-D32 |
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- FastFCN |
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- DeepLabV3 |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 5.67 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722.log.json |
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Paper: |
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Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1903.11816 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12 |
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Framework: PyTorch |
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- Name: fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024 |
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In Collection: FastFCN |
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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: 79.52 |
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mIoU(ms+flip): 80.91 |
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_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-D32 |
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- FastFCN |
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- DeepLabV3 |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 9.79 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357.log.json |
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Paper: |
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Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1903.11816 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12 |
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Framework: PyTorch |
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- Name: fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024 |
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In Collection: FastFCN |
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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: 79.26 |
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mIoU(ms+flip): 80.86 |
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_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-D32 |
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- FastFCN |
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- PSPNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 5.67 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722.log.json |
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Paper: |
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Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1903.11816 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12 |
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Framework: PyTorch |
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- Name: fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024 |
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In Collection: FastFCN |
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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: 78.76 |
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mIoU(ms+flip): 80.03 |
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_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-D32 |
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- FastFCN |
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- PSPNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 9.94 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841.log.json |
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Paper: |
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Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1903.11816 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12 |
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Framework: PyTorch |
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- Name: fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024 |
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In Collection: FastFCN |
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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: 77.97 |
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mIoU(ms+flip): 79.92 |
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_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-D32 |
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- FastFCN |
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- EncNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 8.15 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036.log.json |
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Paper: |
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Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1903.11816 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12 |
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Framework: PyTorch |
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- Name: fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024 |
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In Collection: FastFCN |
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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: 78.6 |
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mIoU(ms+flip): 80.25 |
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_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-D32 |
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- FastFCN |
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- EncNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 15.45 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217.log.json |
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Paper: |
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Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1903.11816 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12 |
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Framework: PyTorch |
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- Name: fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512 |
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In Collection: FastFCN |
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Results: |
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Task: Semantic Segmentation |
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Dataset: ADE20K |
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Metrics: |
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mIoU: 41.88 |
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mIoU(ms+flip): 42.91 |
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512.py |
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Metadata: |
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Training Data: ADE20K |
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Batch Size: 16 |
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Architecture: |
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- R-50-D32 |
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- FastFCN |
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- DeepLabV3 |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 8.46 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619-3aa40f2d.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619.log.json |
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Paper: |
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Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1903.11816 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12 |
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Framework: PyTorch |
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- Name: fastfcn_r50-d32_jpu_aspp_4xb4-160k_ade20k-512x512 |
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In Collection: FastFCN |
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Results: |
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Task: Semantic Segmentation |
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Dataset: ADE20K |
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Metrics: |
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mIoU: 43.58 |
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mIoU(ms+flip): 44.92 |
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_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-D32 |
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- FastFCN |
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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/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246-27036aee.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246.log.json |
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Paper: |
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Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1903.11816 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12 |
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Framework: PyTorch |
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- Name: fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512 |
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In Collection: FastFCN |
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Results: |
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Task: Semantic Segmentation |
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Dataset: ADE20K |
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Metrics: |
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mIoU: 41.4 |
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mIoU(ms+flip): 42.12 |
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512.py |
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Metadata: |
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Training Data: ADE20K |
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Batch Size: 16 |
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Architecture: |
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- R-50-D32 |
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- FastFCN |
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- PSPNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 8.02 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137-993d07c8.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137.log.json |
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Paper: |
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Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1903.11816 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12 |
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Framework: PyTorch |
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- Name: fastfcn_r50-d32_jpu_psp_4xb4-160k_ade20k-512x512 |
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In Collection: FastFCN |
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Results: |
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Task: Semantic Segmentation |
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Dataset: ADE20K |
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Metrics: |
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mIoU: 42.63 |
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mIoU(ms+flip): 43.71 |
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_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-D32 |
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- FastFCN |
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- PSPNet |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455-e8f5a2fd.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455.log.json |
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Paper: |
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Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation' |
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URL: https://arxiv.org/abs/1903.11816 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12 |
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Framework: PyTorch |
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- Name: fastfcn_r50-d32_jpu_enc_4xb4-80k_ade20k-512x512 |
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In Collection: FastFCN |
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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: 40.88 |
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mIoU(ms+flip): 42.36 |
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_ade20k-512x512.py |
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Metadata: |
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Training Data: ADE20K |
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Batch Size: 16 |
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Architecture: |
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- R-50-D32 |
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- FastFCN |
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- EncNet |
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Training Resources: 4x V100 GPUS |
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Memory (GB): 9.67 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214-65aef6dd.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214.log.json |
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Paper: |
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Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation' |
|
URL: https://arxiv.org/abs/1903.11816 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12 |
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Framework: PyTorch |
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- Name: fastfcn_r50-d32_jpu_enc_4xb4-160k_ade20k-512x512 |
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In Collection: FastFCN |
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Results: |
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Task: Semantic Segmentation |
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Dataset: ADE20K |
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Metrics: |
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mIoU: 42.5 |
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mIoU(ms+flip): 44.21 |
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Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_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-D32 |
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- FastFCN |
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- EncNet |
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Training Resources: 4x V100 GPUS |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456-d875ce3c.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456.log.json |
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Paper: |
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Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation' |
|
URL: https://arxiv.org/abs/1903.11816 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12 |
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Framework: PyTorch |
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