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mmsegmentation/configs/hrnet/hrnet.yml

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Collections:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
Name: hrnet
Models:
- Config: configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 42.12
lr schd: 40000
memory (GB): 1.7
Name: fcn_hr18s_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 73.86
mIoU(ms+flip): 75.91
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth
- Config: configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 77.1
lr schd: 40000
memory (GB): 2.9
Name: fcn_hr18_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 77.19
mIoU(ms+flip): 78.92
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth
- Config: configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 155.76
lr schd: 40000
memory (GB): 6.2
Name: fcn_hr48_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.48
mIoU(ms+flip): 79.69
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth
- Config: configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,1024)
lr schd: 80000
Name: fcn_hr18s_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 75.31
mIoU(ms+flip): 77.48
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth
- Config: configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,1024)
lr schd: 80000
Name: fcn_hr18_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.65
mIoU(ms+flip): 80.35
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth
- Config: configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,1024)
lr schd: 80000
Name: fcn_hr48_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.93
mIoU(ms+flip): 80.72
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth
- Config: configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,1024)
lr schd: 160000
Name: fcn_hr18s_512x1024_160k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 76.31
mIoU(ms+flip): 78.31
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth
- Config: configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,1024)
lr schd: 160000
Name: fcn_hr18_512x1024_160k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.8
mIoU(ms+flip): 80.74
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth
- Config: configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,1024)
lr schd: 160000
Name: fcn_hr48_512x1024_160k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 80.65
mIoU(ms+flip): 81.92
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth
- Config: configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 25.87
lr schd: 80000
memory (GB): 3.8
Name: fcn_hr18s_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 31.38
mIoU(ms+flip): 32.45
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth
- Config: configs/hrnet/fcn_hr18_512x512_80k_ade20k.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 44.31
lr schd: 80000
memory (GB): 4.9
Name: fcn_hr18_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 35.51
mIoU(ms+flip): 36.8
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20200614_185145-66f20cb7.pth
- Config: configs/hrnet/fcn_hr48_512x512_80k_ade20k.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 47.1
lr schd: 80000
memory (GB): 8.2
Name: fcn_hr48_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 41.9
mIoU(ms+flip): 43.27
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth
- Config: configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 160000
Name: fcn_hr18s_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 33.0
mIoU(ms+flip): 34.55
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20200614_214413-870f65ac.pth
- Config: configs/hrnet/fcn_hr18_512x512_160k_ade20k.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 160000
Name: fcn_hr18_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 36.79
mIoU(ms+flip): 38.58
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth
- Config: configs/hrnet/fcn_hr48_512x512_160k_ade20k.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 160000
Name: fcn_hr48_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 42.02
mIoU(ms+flip): 43.86
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth
- Config: configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 23.06
lr schd: 20000
memory (GB): 1.8
Name: fcn_hr18s_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 65.2
mIoU(ms+flip): 68.55
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20200617_224503-56e36088.pth
- Config: configs/hrnet/fcn_hr18_512x512_20k_voc12aug.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 42.59
lr schd: 20000
memory (GB): 2.9
Name: fcn_hr18_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 72.3
mIoU(ms+flip): 74.71
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth
- Config: configs/hrnet/fcn_hr48_512x512_20k_voc12aug.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 45.35
lr schd: 20000
memory (GB): 6.2
Name: fcn_hr48_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 75.87
mIoU(ms+flip): 78.58
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth
- Config: configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 40000
Name: fcn_hr18s_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 66.61
mIoU(ms+flip): 70.0
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth
- Config: configs/hrnet/fcn_hr18_512x512_40k_voc12aug.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 40000
Name: fcn_hr18_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 72.9
mIoU(ms+flip): 75.59
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth
- Config: configs/hrnet/fcn_hr48_512x512_40k_voc12aug.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 40000
Name: fcn_hr48_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.24
mIoU(ms+flip): 78.49
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth
- Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (480,480)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (480,480)
value: 112.87
lr schd: 40000
memory (GB): 6.1
Name: fcn_hr48_480x480_40k_pascal_context
Results:
Dataset: Pascal Context
Metrics:
mIoU: 45.14
mIoU(ms+flip): 47.42
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (480,480)
lr schd: 80000
Name: fcn_hr48_480x480_80k_pascal_context
Results:
Dataset: Pascal Context
Metrics:
mIoU: 45.84
mIoU(ms+flip): 47.84
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (480,480)
lr schd: 40000
Name: fcn_hr48_480x480_40k_pascal_context_59
Results:
Dataset: Pascal Context 59
Metrics:
mIoU: 50.33
mIoU(ms+flip): 52.83
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (480,480)
lr schd: 80000
Name: fcn_hr48_480x480_80k_pascal_context_59
Results:
Dataset: Pascal Context 59
Metrics:
mIoU: 51.12
mIoU(ms+flip): 53.56
Task: Semantic Segmentation
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
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