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mmsegmentation/configs/isanet/isanet.yml

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Collections:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Name: isanet
Models:
- Config: configs/isanet/isanet_r50-d8_512x1024_40k_cityscapes.py
In Collection: isanet
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: 343.64
lr schd: 40000
memory (GB): 5.869
Name: isanet_r50-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.49
mIoU(ms+flip): 79.44
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_40k_cityscapes/isanet_r50-d8_512x1024_40k_cityscapes_20210901_054739-981bd763.pth
- Config: configs/isanet/isanet_r50-d8_512x1024_80k_cityscapes.py
In Collection: isanet
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: 343.64
lr schd: 80000
memory (GB): 5.869
Name: isanet_r50-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.68
mIoU(ms+flip): 80.25
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_80k_cityscapes/isanet_r50-d8_512x1024_80k_cityscapes_20210901_074202-89384497.pth
- Config: configs/isanet/isanet_r50-d8_769x769_40k_cityscapes.py
In Collection: isanet
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: 649.35
lr schd: 40000
memory (GB): 6.759
Name: isanet_r50-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.7
mIoU(ms+flip): 80.28
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_40k_cityscapes/isanet_r50-d8_769x769_40k_cityscapes_20210903_050200-4ae7e65b.pth
- Config: configs/isanet/isanet_r50-d8_769x769_80k_cityscapes.py
In Collection: isanet
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: 649.35
lr schd: 80000
memory (GB): 6.759
Name: isanet_r50-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.29
mIoU(ms+flip): 80.53
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_80k_cityscapes/isanet_r50-d8_769x769_80k_cityscapes_20210903_101126-99b54519.pth
- Config: configs/isanet/isanet_r101-d8_512x1024_40k_cityscapes.py
In Collection: isanet
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: 425.53
lr schd: 40000
memory (GB): 9.425
Name: isanet_r101-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.58
mIoU(ms+flip): 81.05
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_40k_cityscapes/isanet_r101-d8_512x1024_40k_cityscapes_20210901_145553-293e6bd6.pth
- Config: configs/isanet/isanet_r101-d8_512x1024_80k_cityscapes.py
In Collection: isanet
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: 425.53
lr schd: 80000
memory (GB): 9.425
Name: isanet_r101-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 80.32
mIoU(ms+flip): 81.58
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_80k_cityscapes/isanet_r101-d8_512x1024_80k_cityscapes_20210901_145243-5b99c9b2.pth
- Config: configs/isanet/isanet_r101-d8_769x769_40k_cityscapes.py
In Collection: isanet
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: 1086.96
lr schd: 40000
memory (GB): 10.815
Name: isanet_r101-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.68
mIoU(ms+flip): 80.95
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_40k_cityscapes/isanet_r101-d8_769x769_40k_cityscapes_20210903_111320-509e7224.pth
- Config: configs/isanet/isanet_r101-d8_769x769_80k_cityscapes.py
In Collection: isanet
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: 1086.96
lr schd: 80000
memory (GB): 10.815
Name: isanet_r101-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 80.61
mIoU(ms+flip): 81.59
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_80k_cityscapes/isanet_r101-d8_769x769_80k_cityscapes_20210903_111319-24f71dfa.pth
- Config: configs/isanet/isanet_r50-d8_512x512_80k_ade20k.py
In Collection: isanet
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: 44.35
lr schd: 80000
memory (GB): 9.0
Name: isanet_r50-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 41.12
mIoU(ms+flip): 42.35
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_80k_ade20k/isanet_r50-d8_512x512_80k_ade20k_20210903_124557-6ed83a0c.pth
- Config: configs/isanet/isanet_r50-d8_512x512_160k_ade20k.py
In Collection: isanet
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: 44.35
lr schd: 160000
memory (GB): 9.0
Name: isanet_r50-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 42.59
mIoU(ms+flip): 43.07
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_160k_ade20k/isanet_r50-d8_512x512_160k_ade20k_20210903_104850-f752d0a3.pth
- Config: configs/isanet/isanet_r101-d8_512x512_80k_ade20k.py
In Collection: isanet
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: 94.7
lr schd: 80000
memory (GB): 12.562
Name: isanet_r101-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 43.51
mIoU(ms+flip): 44.38
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_80k_ade20k/isanet_r101-d8_512x512_80k_ade20k_20210903_162056-68b235c2.pth
- Config: configs/isanet/isanet_r101-d8_512x512_160k_ade20k.py
In Collection: isanet
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: 94.7
lr schd: 160000
memory (GB): 12.562
Name: isanet_r101-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 43.8
mIoU(ms+flip): 45.4
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_160k_ade20k/isanet_r101-d8_512x512_160k_ade20k_20210903_211431-a7879dcd.pth
- Config: configs/isanet/isanet_r50-d8_512x512_20k_voc12aug.py
In Collection: isanet
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: 43.33
lr schd: 20000
memory (GB): 5.9
Name: isanet_r50-d8_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.78
mIoU(ms+flip): 77.79
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_20k_voc12aug/isanet_r50-d8_512x512_20k_voc12aug_20210901_164838-79d59b80.pth
- Config: configs/isanet/isanet_r50-d8_512x512_40k_voc12aug.py
In Collection: isanet
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: 43.33
lr schd: 40000
memory (GB): 5.9
Name: isanet_r50-d8_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.2
mIoU(ms+flip): 77.22
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_40k_voc12aug/isanet_r50-d8_512x512_40k_voc12aug_20210901_151349-7d08a54e.pth
- Config: configs/isanet/isanet_r101-d8_512x512_20k_voc12aug.py
In Collection: isanet
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: 134.77
lr schd: 20000
memory (GB): 9.465
Name: isanet_r101-d8_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.46
mIoU(ms+flip): 79.16
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_20k_voc12aug/isanet_r101-d8_512x512_20k_voc12aug_20210901_115805-3ccbf355.pth
- Config: configs/isanet/isanet_r101-d8_512x512_40k_voc12aug.py
In Collection: isanet
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: 134.77
lr schd: 40000
memory (GB): 9.465
Name: isanet_r101-d8_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.12
mIoU(ms+flip): 79.04
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_40k_voc12aug/isanet_r101-d8_512x512_40k_voc12aug_20210901_145814-bc71233b.pth
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