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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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