|
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 |
|
|