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mmsegmentation/configs/deeplabv3/deeplabv3.yml

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Collections:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
Name: deeplabv3
Models:
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py
In Collection: deeplabv3
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: 389.11
lr schd: 40000
memory (GB): 6.1
Name: deeplabv3_r50-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.09
mIoU(ms+flip): 80.45
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py
In Collection: deeplabv3
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: 520.83
lr schd: 40000
memory (GB): 9.6
Name: deeplabv3_r101-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 77.12
mIoU(ms+flip): 79.61
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth
- Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py
In Collection: deeplabv3
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: 900.9
lr schd: 40000
memory (GB): 6.9
Name: deeplabv3_r50-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.58
mIoU(ms+flip): 79.89
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py
In Collection: deeplabv3
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: 1204.82
lr schd: 40000
memory (GB): 10.9
Name: deeplabv3_r101-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.27
mIoU(ms+flip): 80.11
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth
- Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-18-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 72.57
lr schd: 80000
memory (GB): 1.7
Name: deeplabv3_r18-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 76.7
mIoU(ms+flip): 78.27
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: deeplabv3_r50-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.32
mIoU(ms+flip): 80.57
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: deeplabv3_r101-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 80.2
mIoU(ms+flip): 81.21
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth
- Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-18-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 180.18
lr schd: 80000
memory (GB): 1.9
Name: deeplabv3_r18-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 76.6
mIoU(ms+flip): 78.26
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth
- Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: deeplabv3_r50-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.89
mIoU(ms+flip): 81.06
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: deeplabv3_r101-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.67
mIoU(ms+flip): 80.81
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth
- Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D16-MG124
crop size: (512,1024)
lr schd: 80000
Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.36
mIoU(ms+flip): 79.84
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth
- Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-18b-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 71.79
lr schd: 80000
memory (GB): 1.6
Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 76.26
mIoU(ms+flip): 77.88
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth
- Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-50b-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 364.96
lr schd: 80000
memory (GB): 6.0
Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.63
mIoU(ms+flip): 80.98
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth
- Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-101b-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 552.49
lr schd: 80000
memory (GB): 9.5
Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 80.01
mIoU(ms+flip): 81.21
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth
- Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-18b-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 172.71
lr schd: 80000
memory (GB): 1.8
Name: deeplabv3_r18b-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 76.63
mIoU(ms+flip): 77.51
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth
- Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-50b-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 862.07
lr schd: 80000
memory (GB): 6.8
Name: deeplabv3_r50b-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.8
mIoU(ms+flip): 80.27
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth
- Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-101b-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 1219.51
lr schd: 80000
memory (GB): 10.7
Name: deeplabv3_r101b-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.41
mIoU(ms+flip): 80.73
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py
In Collection: deeplabv3
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: 67.75
lr schd: 80000
memory (GB): 8.9
Name: deeplabv3_r50-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 42.42
mIoU(ms+flip): 43.28
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py
In Collection: deeplabv3
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: 98.62
lr schd: 80000
memory (GB): 12.4
Name: deeplabv3_r101-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 44.08
mIoU(ms+flip): 45.19
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py
In Collection: deeplabv3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: deeplabv3_r50-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 42.66
mIoU(ms+flip): 44.09
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: deeplabv3_r101-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 45.0
mIoU(ms+flip): 46.66
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py
In Collection: deeplabv3
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: 72.05
lr schd: 20000
memory (GB): 6.1
Name: deeplabv3_r50-d8_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.17
mIoU(ms+flip): 77.42
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py
In Collection: deeplabv3
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: 101.94
lr schd: 20000
memory (GB): 9.6
Name: deeplabv3_r101-d8_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.7
mIoU(ms+flip): 79.95
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py
In Collection: deeplabv3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Name: deeplabv3_r50-d8_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.68
mIoU(ms+flip): 78.78
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Name: deeplabv3_r101-d8_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.92
mIoU(ms+flip): 79.18
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D8
crop size: (480,480)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (480,480)
value: 141.04
lr schd: 40000
memory (GB): 9.2
Name: deeplabv3_r101-d8_480x480_40k_pascal_context
Results:
Dataset: Pascal Context
Metrics:
mIoU: 46.55
mIoU(ms+flip): 47.81
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Name: deeplabv3_r101-d8_480x480_80k_pascal_context
Results:
Dataset: Pascal Context
Metrics:
mIoU: 46.42
mIoU(ms+flip): 47.53
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
Name: deeplabv3_r101-d8_480x480_40k_pascal_context_59
Results:
Dataset: Pascal Context 59
Metrics:
mIoU: 52.61
mIoU(ms+flip): 54.28
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59
Results:
Dataset: Pascal Context 59
Metrics:
mIoU: 52.46
mIoU(ms+flip): 54.09
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth
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