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mmsegmentation/configs/dmnet/dmnet.yml

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Collections:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
Name: dmnet
Models:
- Config: configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py
In Collection: dmnet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 273.22
lr schd: 40000
memory (GB): 7.0
Name: dmnet_r50-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 77.78
mIoU(ms+flip): 79.14
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201215_042326-615373cf.pth
- Config: configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py
In Collection: dmnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 393.7
lr schd: 40000
memory (GB): 10.6
Name: dmnet_r101-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.37
mIoU(ms+flip): 79.72
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201215_043100-8291e976.pth
- Config: configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py
In Collection: dmnet
Metadata:
backbone: R-50-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 636.94
lr schd: 40000
memory (GB): 7.9
Name: dmnet_r50-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.49
mIoU(ms+flip): 80.27
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201215_093706-e7f0e23e.pth
- Config: configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py
In Collection: dmnet
Metadata:
backbone: R-101-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 990.1
lr schd: 40000
memory (GB): 12.0
Name: dmnet_r101-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 77.62
mIoU(ms+flip): 78.94
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201215_081348-a74261f6.pth
- Config: configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py
In Collection: dmnet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: dmnet_r50-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.07
mIoU(ms+flip): 80.22
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201215_053728-3c8893b9.pth
- Config: configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py
In Collection: dmnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: dmnet_r101-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.64
mIoU(ms+flip): 80.67
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201215_031718-fa081cb8.pth
- Config: configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py
In Collection: dmnet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: dmnet_r50-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.22
mIoU(ms+flip): 80.55
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201215_034006-6060840e.pth
- Config: configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py
In Collection: dmnet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: dmnet_r101-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.19
mIoU(ms+flip): 80.65
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201215_082810-7f0de59a.pth
- Config: configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py
In Collection: dmnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 47.73
lr schd: 80000
memory (GB): 9.4
Name: dmnet_r50-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 42.37
mIoU(ms+flip): 43.62
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201215_144744-f89092a6.pth
- Config: configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py
In Collection: dmnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 72.05
lr schd: 80000
memory (GB): 13.0
Name: dmnet_r101-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 45.34
mIoU(ms+flip): 46.13
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201215_104812-bfa45311.pth
- Config: configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py
In Collection: dmnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: dmnet_r50-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 43.15
mIoU(ms+flip): 44.17
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201215_115313-025ab3f9.pth
- Config: configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py
In Collection: dmnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: dmnet_r101-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 45.42
mIoU(ms+flip): 46.76
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201215_111145-a0bc02ef.pth
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