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| Collections: | |
| - Name: DANet | |
| License: Apache License 2.0 | |
| Metadata: | |
| Training Data: | |
| - Cityscapes | |
| - ADE20K | |
| - Pascal VOC 2012 + Aug | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| README: configs/danet/README.md | |
| Frameworks: | |
| - PyTorch | |
| Models: | |
| - Name: danet_r50-d8_4xb2-40k_cityscapes-512x1024 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.74 | |
| Config: configs/danet/danet_r50-d8_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 7.4 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |
| - Name: danet_r101-d8_4xb2-40k_cityscapes-512x1024 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 80.52 | |
| Config: configs/danet/danet_r101-d8_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 10.9 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |
| - Name: danet_r50-d8_4xb2-40k_cityscapes-769x769 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.88 | |
| mIoU(ms+flip): 80.62 | |
| Config: configs/danet/danet_r50-d8_4xb2-40k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 8.8 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |
| - Name: danet_r101-d8_4xb2-40k_cityscapes-769x769 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.88 | |
| mIoU(ms+flip): 81.47 | |
| Config: configs/danet/danet_r101-d8_4xb2-40k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 12.8 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |
| - Name: danet_r50-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.34 | |
| Config: configs/danet/danet_r50-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |
| - Name: danet_r101-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 80.41 | |
| Config: configs/danet/danet_r101-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |
| - Name: danet_r50-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.27 | |
| mIoU(ms+flip): 80.96 | |
| Config: configs/danet/danet_r50-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |
| - Name: danet_r101-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 80.47 | |
| mIoU(ms+flip): 82.02 | |
| Config: configs/danet/danet_r101-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |
| - Name: danet_r50-d8_4xb4-80k_ade20k-512x512 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 41.66 | |
| mIoU(ms+flip): 42.9 | |
| Config: configs/danet/danet_r50-d8_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 11.5 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |
| - Name: danet_r101-d8_4xb4-80k_ade20k-512x512 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 43.64 | |
| mIoU(ms+flip): 45.19 | |
| Config: configs/danet/danet_r101-d8_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 15.0 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |
| - Name: danet_r50-d8_4xb4-160k_ade20k-512x512 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 42.45 | |
| mIoU(ms+flip): 43.25 | |
| Config: configs/danet/danet_r50-d8_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |
| - Name: danet_r101-d8_4xb4-160k_ade20k-512x512 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 44.17 | |
| mIoU(ms+flip): 45.02 | |
| Config: configs/danet/danet_r101-d8_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |
| - Name: danet_r50-d8_4xb4-20k_voc12aug-512x512 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 74.45 | |
| mIoU(ms+flip): 75.69 | |
| Config: configs/danet/danet_r50-d8_4xb4-20k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 6.5 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |
| - Name: danet_r101-d8_4xb4-20k_voc12aug-512x512 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 76.02 | |
| mIoU(ms+flip): 77.23 | |
| Config: configs/danet/danet_r101-d8_4xb4-20k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.9 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |
| - Name: danet_r50-d8_4xb4-40k_voc12aug-512x512 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 76.37 | |
| mIoU(ms+flip): 77.29 | |
| Config: configs/danet/danet_r50-d8_4xb4-40k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |
| - Name: danet_r101-d8_4xb4-40k_voc12aug-512x512 | |
| In Collection: DANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 76.51 | |
| mIoU(ms+flip): 77.32 | |
| Config: configs/danet/danet_r101-d8_4xb4-40k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - DANet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031.log.json | |
| Paper: | |
| Title: Dual Attention Network for Scene Segmentation | |
| URL: https://arxiv.org/abs/1809.02983 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 | |
| Framework: PyTorch | |