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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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