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| Collections: | |
| - Name: FastFCN | |
| License: Apache License 2.0 | |
| Metadata: | |
| Training Data: | |
| - Cityscapes | |
| - ADE20K | |
| Paper: | |
| Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1903.11816 | |
| README: configs/fastfcn/README.md | |
| Frameworks: | |
| - PyTorch | |
| Models: | |
| - Name: fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FastFCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.12 | |
| mIoU(ms+flip): 80.58 | |
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D32 | |
| - FastFCN | |
| - DeepLabV3 | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 5.67 | |
| 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 | |
| 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 | |
| Paper: | |
| 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 | |
| Framework: PyTorch | |
| - Name: fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FastFCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.52 | |
| mIoU(ms+flip): 80.91 | |
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D32 | |
| - FastFCN | |
| - DeepLabV3 | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.79 | |
| 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 | |
| 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 | |
| Paper: | |
| 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 | |
| Framework: PyTorch | |
| - Name: fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FastFCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.26 | |
| mIoU(ms+flip): 80.86 | |
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D32 | |
| - FastFCN | |
| - PSPNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 5.67 | |
| 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 | |
| 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 | |
| Paper: | |
| 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 | |
| Framework: PyTorch | |
| - Name: fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FastFCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.76 | |
| mIoU(ms+flip): 80.03 | |
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D32 | |
| - FastFCN | |
| - PSPNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.94 | |
| 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 | |
| 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 | |
| Paper: | |
| 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 | |
| Framework: PyTorch | |
| - Name: fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FastFCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.97 | |
| mIoU(ms+flip): 79.92 | |
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D32 | |
| - FastFCN | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 8.15 | |
| 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 | |
| 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 | |
| Paper: | |
| 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 | |
| Framework: PyTorch | |
| - Name: fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FastFCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.6 | |
| mIoU(ms+flip): 80.25 | |
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D32 | |
| - FastFCN | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 15.45 | |
| 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 | |
| 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 | |
| Paper: | |
| 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 | |
| Framework: PyTorch | |
| - Name: fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512 | |
| In Collection: FastFCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 41.88 | |
| mIoU(ms+flip): 42.91 | |
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D32 | |
| - FastFCN | |
| - DeepLabV3 | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 8.46 | |
| 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 | |
| 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 | |
| Paper: | |
| 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 | |
| Framework: PyTorch | |
| - Name: fastfcn_r50-d32_jpu_aspp_4xb4-160k_ade20k-512x512 | |
| In Collection: FastFCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 43.58 | |
| mIoU(ms+flip): 44.92 | |
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D32 | |
| - FastFCN | |
| - DeepLabV3 | |
| Training Resources: 4x V100 GPUS | |
| 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 | |
| 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 | |
| Paper: | |
| 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 | |
| Framework: PyTorch | |
| - Name: fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512 | |
| In Collection: FastFCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 41.4 | |
| mIoU(ms+flip): 42.12 | |
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D32 | |
| - FastFCN | |
| - PSPNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 8.02 | |
| 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 | |
| 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 | |
| Paper: | |
| 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 | |
| Framework: PyTorch | |
| - Name: fastfcn_r50-d32_jpu_psp_4xb4-160k_ade20k-512x512 | |
| In Collection: FastFCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 42.63 | |
| mIoU(ms+flip): 43.71 | |
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D32 | |
| - FastFCN | |
| - PSPNet | |
| Training Resources: 4x V100 GPUS | |
| 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 | |
| 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 | |
| Paper: | |
| 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 | |
| Framework: PyTorch | |
| - Name: fastfcn_r50-d32_jpu_enc_4xb4-80k_ade20k-512x512 | |
| In Collection: FastFCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 40.88 | |
| mIoU(ms+flip): 42.36 | |
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D32 | |
| - FastFCN | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.67 | |
| 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 | |
| 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 | |
| Paper: | |
| 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 | |
| Framework: PyTorch | |
| - Name: fastfcn_r50-d32_jpu_enc_4xb4-160k_ade20k-512x512 | |
| In Collection: FastFCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 42.5 | |
| mIoU(ms+flip): 44.21 | |
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D32 | |
| - FastFCN | |
| - EncNet | |
| Training Resources: 4x V100 GPUS | |
| 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 | |
| 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 | |
| Paper: | |
| 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 | |
| Framework: PyTorch | |