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mmsegmentation/configs/hrnet/hrnet.yml
MengzhangLI 1cf6049758 change (#1083)
2021-11-30 20:54:25 +08:00

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Collections:
- Name: hrnet
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
- LoveDA
Paper:
URL: https://arxiv.org/abs/1908.07919
Title: Deep High-Resolution Representation Learning for Human Pose Estimation
README: configs/hrnet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
Version: v0.17.0
Converted From:
Code: https://github.com/HRNet/HRNet-Semantic-Segmentation
Models:
- Name: fcn_hr18s_512x1024_40k_cityscapes
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 42.12
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 1.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.86
mIoU(ms+flip): 75.91
Config: configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth
- Name: fcn_hr18_512x1024_40k_cityscapes
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 77.1
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 2.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.19
mIoU(ms+flip): 78.92
Config: configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth
- Name: fcn_hr48_512x1024_40k_cityscapes
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 155.76
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 6.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.48
mIoU(ms+flip): 79.69
Config: configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth
- Name: fcn_hr18s_512x1024_80k_cityscapes
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.31
mIoU(ms+flip): 77.48
Config: configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth
- Name: fcn_hr18_512x1024_80k_cityscapes
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.65
mIoU(ms+flip): 80.35
Config: configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth
- Name: fcn_hr48_512x1024_80k_cityscapes
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.93
mIoU(ms+flip): 80.72
Config: configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth
- Name: fcn_hr18s_512x1024_160k_cityscapes
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,1024)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.31
mIoU(ms+flip): 78.31
Config: configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth
- Name: fcn_hr18_512x1024_160k_cityscapes
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,1024)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.8
mIoU(ms+flip): 80.74
Config: configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth
- Name: fcn_hr48_512x1024_160k_cityscapes
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,1024)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.65
mIoU(ms+flip): 81.92
Config: configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth
- Name: fcn_hr18s_512x512_80k_ade20k
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 25.87
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 3.8
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 31.38
mIoU(ms+flip): 32.45
Config: configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth
- Name: fcn_hr18_512x512_80k_ade20k
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 44.31
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 4.9
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 36.27
mIoU(ms+flip): 37.28
Config: configs/hrnet/fcn_hr18_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910-6c9382c0.pth
- Name: fcn_hr48_512x512_80k_ade20k
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 47.1
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 8.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.9
mIoU(ms+flip): 43.27
Config: configs/hrnet/fcn_hr48_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth
- Name: fcn_hr18s_512x512_160k_ade20k
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 33.07
mIoU(ms+flip): 34.56
Config: configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739-f1e7c2e7.pth
- Name: fcn_hr18_512x512_160k_ade20k
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 36.79
mIoU(ms+flip): 38.58
Config: configs/hrnet/fcn_hr18_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth
- Name: fcn_hr48_512x512_160k_ade20k
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.02
mIoU(ms+flip): 43.86
Config: configs/hrnet/fcn_hr48_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth
- Name: fcn_hr18s_512x512_20k_voc12aug
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 23.06
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 1.8
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 65.5
mIoU(ms+flip): 68.89
Config: configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910-0aceadb4.pth
- Name: fcn_hr18_512x512_20k_voc12aug
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 42.59
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 2.9
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 72.3
mIoU(ms+flip): 74.71
Config: configs/hrnet/fcn_hr18_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth
- Name: fcn_hr48_512x512_20k_voc12aug
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 45.35
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.2
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 75.87
mIoU(ms+flip): 78.58
Config: configs/hrnet/fcn_hr48_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth
- Name: fcn_hr18s_512x512_40k_voc12aug
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 66.61
mIoU(ms+flip): 70.0
Config: configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth
- Name: fcn_hr18_512x512_40k_voc12aug
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 72.9
mIoU(ms+flip): 75.59
Config: configs/hrnet/fcn_hr18_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth
- Name: fcn_hr48_512x512_40k_voc12aug
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.24
mIoU(ms+flip): 78.49
Config: configs/hrnet/fcn_hr48_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth
- Name: fcn_hr48_480x480_40k_pascal_context
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (480,480)
lr schd: 40000
inference time (ms/im):
- value: 112.87
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (480,480)
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 45.14
mIoU(ms+flip): 47.42
Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth
- Name: fcn_hr48_480x480_80k_pascal_context
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (480,480)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 45.84
mIoU(ms+flip): 47.84
Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth
- Name: fcn_hr48_480x480_40k_pascal_context_59
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (480,480)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context 59
Metrics:
mIoU: 50.33
mIoU(ms+flip): 52.83
Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth
- Name: fcn_hr48_480x480_80k_pascal_context_59
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (480,480)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context 59
Metrics:
mIoU: 51.12
mIoU(ms+flip): 53.56
Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth
- Name: fcn_hr18s_512x512_80k_loveda
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 33.26
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 1.72
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
Metrics:
mIoU: 49.3
mIoU(ms+flip): 49.23
Config: configs/hrnet/fcn_hr18s_512x512_80k_loveda.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211105_180825-41dcc5dc.pth
- Name: fcn_hr18_512x512_80k_loveda
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 59.63
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 2.9
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
Metrics:
mIoU: 50.87
mIoU(ms+flip): 51.24
Config: configs/hrnet/fcn_hr18_512x512_80k_loveda.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211105_165542-95be4d2b.pth
- Name: fcn_hr48_512x512_80k_loveda
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 110.01
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.25
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
Metrics:
mIoU: 51.04
mIoU(ms+flip): 51.12
Config: configs/hrnet/fcn_hr48_512x512_80k_loveda.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211105_131509-f07e47c6.pth
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