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metafile.yml
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Collections:
- Name: dnl
Metadata:
Training Data:
- Cityscapes
- ADE20K
Models:
- Name: dnl_r50-d8_512x1024_40k_cityscapes
In Collection: dnl
Metadata:
inference time (ms/im):
- value: 390.62
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.61
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/dnl/dnl_r50-d8_512x1024_40k_cityscapes.py
- Name: dnl_r101-d8_512x1024_40k_cityscapes
In Collection: dnl
Metadata:
inference time (ms/im):
- value: 510.2
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.31
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/dnl/dnl_r101-d8_512x1024_40k_cityscapes.py
- Name: dnl_r50-d8_769x769_40k_cityscapes
In Collection: dnl
Metadata:
inference time (ms/im):
- value: 666.67
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.44
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/dnl/dnl_r50-d8_769x769_40k_cityscapes.py
- Name: dnl_r101-d8_769x769_40k_cityscapes
In Collection: dnl
Metadata:
inference time (ms/im):
- value: 980.39
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.39
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/dnl/dnl_r101-d8_769x769_40k_cityscapes.py
- Name: dnl_r50-d8_512x1024_80k_cityscapes
In Collection: dnl
Metadata:
inference time (ms/im):
- value: 390.62
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.33
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/dnl/dnl_r50-d8_512x1024_80k_cityscapes.py
- Name: dnl_r101-d8_512x1024_80k_cityscapes
In Collection: dnl
Metadata:
inference time (ms/im):
- value: 510.2
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.41
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/dnl/dnl_r101-d8_512x1024_80k_cityscapes.py
- Name: dnl_r50-d8_769x769_80k_cityscapes
In Collection: dnl
Metadata:
inference time (ms/im):
- value: 666.67
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.36
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/dnl/dnl_r50-d8_769x769_80k_cityscapes.py
- Name: dnl_r101-d8_769x769_80k_cityscapes
In Collection: dnl
Metadata:
inference time (ms/im):
- value: 980.39
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.41
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/dnl/dnl_r101-d8_769x769_80k_cityscapes.py
- Name: dnl_r50-d8_512x512_80k_ade20k
In Collection: dnl
Metadata:
inference time (ms/im):
- value: 48.4
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.76
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/dnl/dnl_r50-d8_512x512_80k_ade20k.py
- Name: dnl_r101-d8_512x512_80k_ade20k
In Collection: dnl
Metadata:
inference time (ms/im):
- value: 79.74
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.76
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/dnl/dnl_r101-d8_512x512_80k_ade20k.py
- Name: dnl_r50-d8_512x512_160k_ade20k
In Collection: dnl
Metadata:
inference time (ms/im):
- value: 48.4
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.87
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/dnl/dnl_r50-d8_512x512_160k_ade20k.py
- Name: dnl_r101-d8_512x512_160k_ade20k
In Collection: dnl
Metadata:
inference time (ms/im):
- value: 79.74
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.25
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
Config: configs/dnl/dnl_r101-d8_512x512_160k_ade20k.py