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mmsegmentation/configs/dnlnet/dnlnet.yml

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Collections:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
Name: dnlnet
Models:
- Config: configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py
In Collection: dnlnet
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: 390.62
lr schd: 40000
memory (GB): 7.3
Name: dnl_r50-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.61
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth
- Config: configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py
In Collection: dnlnet
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: 510.2
lr schd: 40000
memory (GB): 10.9
Name: dnl_r101-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.31
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth
- Config: configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py
In Collection: dnlnet
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: 666.67
lr schd: 40000
memory (GB): 9.2
Name: dnl_r50-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.44
mIoU(ms+flip): 80.27
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth
- Config: configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py
In Collection: dnlnet
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: 980.39
lr schd: 40000
memory (GB): 12.6
Name: dnl_r101-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 76.39
mIoU(ms+flip): 77.77
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth
- Config: configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py
In Collection: dnlnet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: dnl_r50-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.33
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth
- Config: configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py
In Collection: dnlnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: dnl_r101-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 80.41
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth
- Config: configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py
In Collection: dnlnet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: dnl_r50-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.36
mIoU(ms+flip): 80.7
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth
- Config: configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py
In Collection: dnlnet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: dnl_r101-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.41
mIoU(ms+flip): 80.68
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth
- Config: configs/dnlnet/dnl_r50-d8_512x512_80k_ade20k.py
In Collection: dnlnet
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: 48.4
lr schd: 80000
memory (GB): 8.8
Name: dnl_r50-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 41.76
mIoU(ms+flip): 42.99
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth
- Config: configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py
In Collection: dnlnet
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: 79.74
lr schd: 80000
memory (GB): 12.8
Name: dnl_r101-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 43.76
mIoU(ms+flip): 44.91
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth
- Config: configs/dnlnet/dnl_r50-d8_512x512_160k_ade20k.py
In Collection: dnlnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: dnl_r50-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 41.87
mIoU(ms+flip): 43.01
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth
- Config: configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py
In Collection: dnlnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: dnl_r101-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 44.25
mIoU(ms+flip): 45.78
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth
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