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- Collections:
- - Name: PSANet
- Metadata:
- Training Data:
- - Cityscapes
- - ADE20K
- - Pascal VOC 2012 + Aug
- Paper:
- URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
- Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
- README: configs/psanet/README.md
- Code:
- URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
- Version: v0.17.0
- Converted From:
- Code: https://github.com/hszhao/PSANet
- Models:
- - Name: psanet_r50-d8_512x1024_40k_cityscapes
- In Collection: PSANet
- Metadata:
- backbone: R-50-D8
- crop size: (512,1024)
- lr schd: 40000
- inference time (ms/im):
- - value: 315.46
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,1024)
- Training Memory (GB): 7.0
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 77.63
- mIoU(ms+flip): 79.04
- Config: configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117-99fac37c.pth
- - Name: psanet_r101-d8_512x1024_40k_cityscapes
- In Collection: PSANet
- Metadata:
- backbone: R-101-D8
- crop size: (512,1024)
- lr schd: 40000
- inference time (ms/im):
- - value: 454.55
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,1024)
- Training Memory (GB): 10.5
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 79.14
- mIoU(ms+flip): 80.19
- Config: configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418-27b9cfa7.pth
- - Name: psanet_r50-d8_769x769_40k_cityscapes
- In Collection: PSANet
- Metadata:
- backbone: R-50-D8
- crop size: (769,769)
- lr schd: 40000
- inference time (ms/im):
- - value: 714.29
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (769,769)
- Training Memory (GB): 7.9
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 77.99
- mIoU(ms+flip): 79.64
- Config: configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717-d5365506.pth
- - Name: psanet_r101-d8_769x769_40k_cityscapes
- In Collection: PSANet
- Metadata:
- backbone: R-101-D8
- crop size: (769,769)
- lr schd: 40000
- inference time (ms/im):
- - value: 1020.41
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (769,769)
- Training Memory (GB): 11.9
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 78.43
- mIoU(ms+flip): 80.26
- Config: configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107-997da1e6.pth
- - Name: psanet_r50-d8_512x1024_80k_cityscapes
- In Collection: PSANet
- Metadata:
- backbone: R-50-D8
- crop size: (512,1024)
- lr schd: 80000
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 77.24
- mIoU(ms+flip): 78.69
- Config: configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842-ab60a24f.pth
- - Name: psanet_r101-d8_512x1024_80k_cityscapes
- In Collection: PSANet
- Metadata:
- backbone: R-101-D8
- crop size: (512,1024)
- lr schd: 80000
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 79.31
- mIoU(ms+flip): 80.53
- Config: configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823-0f73a169.pth
- - Name: psanet_r50-d8_769x769_80k_cityscapes
- In Collection: PSANet
- Metadata:
- backbone: R-50-D8
- crop size: (769,769)
- lr schd: 80000
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 79.31
- mIoU(ms+flip): 80.91
- Config: configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134-fe42f49e.pth
- - Name: psanet_r101-d8_769x769_80k_cityscapes
- In Collection: PSANet
- Metadata:
- backbone: R-101-D8
- crop size: (769,769)
- lr schd: 80000
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 79.69
- mIoU(ms+flip): 80.89
- Config: configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550-7665827b.pth
- - Name: psanet_r50-d8_512x512_80k_ade20k
- In Collection: PSANet
- Metadata:
- backbone: R-50-D8
- crop size: (512,512)
- lr schd: 80000
- inference time (ms/im):
- - value: 52.88
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 9.0
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 41.14
- mIoU(ms+flip): 41.91
- Config: configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141-835e4b97.pth
- - Name: psanet_r101-d8_512x512_80k_ade20k
- In Collection: PSANet
- Metadata:
- backbone: R-101-D8
- crop size: (512,512)
- lr schd: 80000
- inference time (ms/im):
- - value: 76.16
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 12.5
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 43.8
- mIoU(ms+flip): 44.75
- Config: configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117-1fab60d4.pth
- - Name: psanet_r50-d8_512x512_160k_ade20k
- In Collection: PSANet
- Metadata:
- backbone: R-50-D8
- crop size: (512,512)
- lr schd: 160000
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 41.67
- mIoU(ms+flip): 42.95
- Config: configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258-148077dd.pth
- - Name: psanet_r101-d8_512x512_160k_ade20k
- In Collection: PSANet
- Metadata:
- backbone: R-101-D8
- crop size: (512,512)
- lr schd: 160000
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 43.74
- mIoU(ms+flip): 45.38
- Config: configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537-dbfa564c.pth
- - Name: psanet_r50-d8_512x512_20k_voc12aug
- In Collection: PSANet
- Metadata:
- backbone: R-50-D8
- crop size: (512,512)
- lr schd: 20000
- inference time (ms/im):
- - value: 54.82
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 6.9
- Results:
- - Task: Semantic Segmentation
- Dataset: Pascal VOC 2012 + Aug
- Metrics:
- mIoU: 76.39
- mIoU(ms+flip): 77.34
- Config: configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413-2f1bbaa1.pth
- - Name: psanet_r101-d8_512x512_20k_voc12aug
- In Collection: PSANet
- Metadata:
- backbone: R-101-D8
- crop size: (512,512)
- lr schd: 20000
- inference time (ms/im):
- - value: 79.18
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 10.4
- Results:
- - Task: Semantic Segmentation
- Dataset: Pascal VOC 2012 + Aug
- Metrics:
- mIoU: 77.91
- mIoU(ms+flip): 79.3
- Config: configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624-946fef11.pth
- - Name: psanet_r50-d8_512x512_40k_voc12aug
- In Collection: PSANet
- Metadata:
- backbone: R-50-D8
- crop size: (512,512)
- lr schd: 40000
- Results:
- - Task: Semantic Segmentation
- Dataset: Pascal VOC 2012 + Aug
- Metrics:
- mIoU: 76.3
- mIoU(ms+flip): 77.35
- Config: configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946-f596afb5.pth
- - Name: psanet_r101-d8_512x512_40k_voc12aug
- In Collection: PSANet
- Metadata:
- backbone: R-101-D8
- crop size: (512,512)
- lr schd: 40000
- Results:
- - Task: Semantic Segmentation
- Dataset: Pascal VOC 2012 + Aug
- Metrics:
- mIoU: 77.73
- mIoU(ms+flip): 79.05
- Config: configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946-1f560f9e.pth
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