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mmsegmentation/configs/upernet/upernet.yml

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Collections:
- Name: UPerNet
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Paper:
URL: https://arxiv.org/pdf/1807.10221.pdf
Title: Unified Perceptual Parsing for Scene Understanding
README: configs/upernet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Version: v0.17.0
Converted From:
Code: https://github.com/CSAILVision/unifiedparsing
Models:
- Name: upernet_r18_512x1024_40k_cityscapes
In Collection: UPerNet
Metadata:
backbone: R-18
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 223.71
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 4.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.39
mIoU(ms+flip): 77.0
Config: configs/upernet/upernet_r18_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x1024_40k_cityscapes/upernet_r18_512x1024_40k_cityscapes_20220615_113231-12ee861d.pth
- Name: upernet_r50_512x1024_40k_cityscapes
In Collection: UPerNet
Metadata:
backbone: R-50
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 235.29
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 6.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.1
mIoU(ms+flip): 78.37
Config: configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth
- Name: upernet_r101_512x1024_40k_cityscapes
In Collection: UPerNet
Metadata:
backbone: R-101
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 263.85
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 7.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.69
mIoU(ms+flip): 80.11
Config: configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth
- Name: upernet_r50_769x769_40k_cityscapes
In Collection: UPerNet
Metadata:
backbone: R-50
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 568.18
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 7.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.98
mIoU(ms+flip): 79.7
Config: configs/upernet/upernet_r50_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth
- Name: upernet_r101_769x769_40k_cityscapes
In Collection: UPerNet
Metadata:
backbone: R-101
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 641.03
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 8.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.03
mIoU(ms+flip): 80.77
Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth
- Name: upernet_r18_512x1024_80k_cityscapes
In Collection: UPerNet
Metadata:
backbone: R-18
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.02
mIoU(ms+flip): 77.38
Config: configs/upernet/upernet_r18_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x1024_80k_cityscapes/upernet_r18_512x1024_80k_cityscapes_20220614_110712-c89a9188.pth
- Name: upernet_r50_512x1024_80k_cityscapes
In Collection: UPerNet
Metadata:
backbone: R-50
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.19
mIoU(ms+flip): 79.19
Config: configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth
- Name: upernet_r101_512x1024_80k_cityscapes
In Collection: UPerNet
Metadata:
backbone: R-101
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.4
mIoU(ms+flip): 80.46
Config: configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth
- Name: upernet_r50_769x769_80k_cityscapes
In Collection: UPerNet
Metadata:
backbone: R-50
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.39
mIoU(ms+flip): 80.92
Config: configs/upernet/upernet_r50_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth
- Name: upernet_r101_769x769_80k_cityscapes
In Collection: UPerNet
Metadata:
backbone: R-101
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.1
mIoU(ms+flip): 81.49
Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth
- Name: upernet_r18_512x512_80k_ade20k
In Collection: UPerNet
Metadata:
backbone: R-18
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 40.39
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.6
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 38.76
mIoU(ms+flip): 39.81
Config: configs/upernet/upernet_r18_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_80k_ade20k/upernet_r18_512x512_80k_ade20k_20220614_110319-22e81719.pth
- Name: upernet_r50_512x512_80k_ade20k
In Collection: UPerNet
Metadata:
backbone: R-50
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 42.74
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 8.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 40.7
mIoU(ms+flip): 41.81
Config: configs/upernet/upernet_r50_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth
- Name: upernet_r101_512x512_80k_ade20k
In Collection: UPerNet
Metadata:
backbone: R-101
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 49.16
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.91
mIoU(ms+flip): 43.96
Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth
- Name: upernet_r18_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: R-18
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 39.23
mIoU(ms+flip): 39.97
Config: configs/upernet/upernet_r18_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_160k_ade20k/upernet_r18_512x512_160k_ade20k_20220615_113300-791c3f3e.pth
- Name: upernet_r50_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: R-50
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.05
mIoU(ms+flip): 42.78
Config: configs/upernet/upernet_r50_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth
- Name: upernet_r101_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: R-101
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.82
mIoU(ms+flip): 44.85
Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth
- Name: upernet_r18_512x512_20k_voc12aug
In Collection: UPerNet
Metadata:
backbone: R-18
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 38.76
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 4.8
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 72.9
mIoU(ms+flip): 74.71
Config: configs/upernet/upernet_r18_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_20k_voc12aug/upernet_r18_512x512_20k_voc12aug_20220614_123910-ed66e455.pth
- Name: upernet_r50_512x512_20k_voc12aug
In Collection: UPerNet
Metadata:
backbone: R-50
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 43.16
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.4
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 74.82
mIoU(ms+flip): 76.35
Config: configs/upernet/upernet_r50_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth
- Name: upernet_r101_512x512_20k_voc12aug
In Collection: UPerNet
Metadata:
backbone: R-101
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 50.05
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.5
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.1
mIoU(ms+flip): 78.29
Config: configs/upernet/upernet_r101_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth
- Name: upernet_r18_512x512_40k_voc12aug
In Collection: UPerNet
Metadata:
backbone: R-18
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 73.71
mIoU(ms+flip): 74.61
Config: configs/upernet/upernet_r18_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_40k_voc12aug/upernet_r18_512x512_40k_voc12aug_20220614_153605-fafeb868.pth
- Name: upernet_r50_512x512_40k_voc12aug
In Collection: UPerNet
Metadata:
backbone: R-50
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 75.92
mIoU(ms+flip): 77.44
Config: configs/upernet/upernet_r50_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth
- Name: upernet_r101_512x512_40k_voc12aug
In Collection: UPerNet
Metadata:
backbone: R-101
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.43
mIoU(ms+flip): 78.56
Config: configs/upernet/upernet_r101_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth
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