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mmsegmentation/configs/psanet/psanet.yml

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Collections:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Name: psanet
Models:
- Config: configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py
In Collection: psanet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 315.46
lr schd: 40000
memory (GB): 7.0
Name: psanet_r50-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 77.63
mIoU(ms+flip): 79.04
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py
In Collection: psanet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 454.55
lr schd: 40000
memory (GB): 10.5
Name: psanet_r101-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.14
mIoU(ms+flip): 80.19
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py
In Collection: psanet
Metadata:
backbone: R-50-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 714.29
lr schd: 40000
memory (GB): 7.9
Name: psanet_r50-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 77.99
mIoU(ms+flip): 79.64
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py
In Collection: psanet
Metadata:
backbone: R-101-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 1020.41
lr schd: 40000
memory (GB): 11.9
Name: psanet_r101-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.43
mIoU(ms+flip): 80.26
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py
In Collection: psanet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: psanet_r50-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 77.24
mIoU(ms+flip): 78.69
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py
In Collection: psanet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: psanet_r101-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.31
mIoU(ms+flip): 80.53
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py
In Collection: psanet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: psanet_r50-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.31
mIoU(ms+flip): 80.91
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py
In Collection: psanet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: psanet_r101-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.69
mIoU(ms+flip): 80.89
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py
In Collection: psanet
Metadata:
backbone: R-50-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 52.88
lr schd: 80000
memory (GB): 9.0
Name: psanet_r50-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 41.14
mIoU(ms+flip): 41.91
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py
In Collection: psanet
Metadata:
backbone: R-101-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 76.16
lr schd: 80000
memory (GB): 12.5
Name: psanet_r101-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 43.8
mIoU(ms+flip): 44.75
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py
In Collection: psanet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: psanet_r50-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 41.67
mIoU(ms+flip): 42.95
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py
In Collection: psanet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: psanet_r101-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 43.74
mIoU(ms+flip): 45.38
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py
In Collection: psanet
Metadata:
backbone: R-50-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 54.82
lr schd: 20000
memory (GB): 6.9
Name: psanet_r50-d8_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.39
mIoU(ms+flip): 77.34
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py
In Collection: psanet
Metadata:
backbone: R-101-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 79.18
lr schd: 20000
memory (GB): 10.4
Name: psanet_r101-d8_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.91
mIoU(ms+flip): 79.3
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py
In Collection: psanet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Name: psanet_r50-d8_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.3
mIoU(ms+flip): 77.35
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py
In Collection: psanet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Name: psanet_r101-d8_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.73
mIoU(ms+flip): 79.05
Task: Semantic Segmentation
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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