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mmsegmentation/configs/hrnet/hrnet.yml

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Models:
- Name: fcn_hr18s_512x1024_40k_cityscapes
In Collection: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
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: FCN
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 40.21
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 1.59
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
Metrics:
mIoU: 49.28
mIoU(ms+flip): 49.42
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_20211210_203228-60a86a7a.pth
- Name: fcn_hr18_512x512_80k_loveda
In Collection: FCN
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 77.4
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 2.76
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
Metrics:
mIoU: 50.81
mIoU(ms+flip): 50.95
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_20211210_203952-93d9c3b3.pth
- Name: fcn_hr48_512x512_80k_loveda
In Collection: FCN
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 104.06
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.2
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
Metrics:
mIoU: 51.42
mIoU(ms+flip): 51.64
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_20211211_044756-67072f55.pth
- Name: fcn_hr18s_512x512_80k_potsdam
In Collection: FCN
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 27.78
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 1.58
Results:
- Task: Semantic Segmentation
Dataset: Potsdam
Metrics:
mIoU: 77.64
mIoU(ms+flip): 78.8
Config: configs/hrnet/fcn_hr18s_512x512_80k_potsdam.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517-ba32af63.pth
- Name: fcn_hr18_512x512_80k_potsdam
In Collection: FCN
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 51.95
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 2.76
Results:
- Task: Semantic Segmentation
Dataset: Potsdam
Metrics:
mIoU: 78.26
mIoU(ms+flip): 79.24
Config: configs/hrnet/fcn_hr18_512x512_80k_potsdam.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517-5d0387ad.pth
- Name: fcn_hr48_512x512_80k_potsdam
In Collection: FCN
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 60.9
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.2
Results:
- Task: Semantic Segmentation
Dataset: Potsdam
Metrics:
mIoU: 78.39
mIoU(ms+flip): 79.34
Config: configs/hrnet/fcn_hr48_512x512_80k_potsdam.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601-97434c78.pth
- Name: fcn_hr18s_4x4_512x512_80k_vaihingen
In Collection: FCN
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 26.24
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 1.58
Results:
- Task: Semantic Segmentation
Dataset: Vaihingen
Metrics:
mIoU: 71.81
mIoU(ms+flip): 73.1
Config: configs/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909-b23aae02.pth
- Name: fcn_hr18_4x4_512x512_80k_vaihingen
In Collection: FCN
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 51.15
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 2.76
Results:
- Task: Semantic Segmentation
Dataset: Vaihingen
Metrics:
mIoU: 72.57
mIoU(ms+flip): 74.09
Config: configs/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216-2ec3ae8a.pth
- Name: fcn_hr48_4x4_512x512_80k_vaihingen
In Collection: FCN
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 57.97
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.2
Results:
- Task: Semantic Segmentation
Dataset: Vaihingen
Metrics:
mIoU: 72.5
mIoU(ms+flip): 73.52
Config: configs/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244-7133cb22.pth
- Name: fcn_hr18s_4x4_896x896_80k_isaid
In Collection: FCN
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (896,896)
lr schd: 80000
inference time (ms/im):
- value: 72.25
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (896,896)
Training Memory (GB): 4.95
Results:
- Task: Semantic Segmentation
Dataset: iSAID
Metrics:
mIoU: 62.3
mIoU(ms+flip): 62.97
Config: configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603-3cc0769b.pth
- Name: fcn_hr18_4x4_896x896_80k_isaid
In Collection: FCN
Metadata:
backbone: HRNetV2p-W18
crop size: (896,896)
lr schd: 80000
inference time (ms/im):
- value: 129.7
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (896,896)
Training Memory (GB): 8.3
Results:
- Task: Semantic Segmentation
Dataset: iSAID
Metrics:
mIoU: 65.06
mIoU(ms+flip): 65.6
Config: configs/hrnet/fcn_hr18_4x4_896x896_80k_isaid.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230-49bf752e.pth
- Name: fcn_hr48_4x4_896x896_80k_isaid
In Collection: FCN
Metadata:
backbone: HRNetV2p-W48
crop size: (896,896)
lr schd: 80000
inference time (ms/im):
- value: 136.24
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (896,896)
Training Memory (GB): 16.89
Results:
- Task: Semantic Segmentation
Dataset: iSAID
Metrics:
mIoU: 67.8
mIoU(ms+flip): 68.53
Config: configs/hrnet/fcn_hr48_4x4_896x896_80k_isaid.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643-547fc420.pth
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