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pspnet.yml
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Collections:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
Name: pspnet
Models:
- Config: configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py
In Collection: pspnet
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: 245.7
lr schd: 40000
memory (GB): 6.1
Name: pspnet_r50-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 77.85
mIoU(ms+flip): 79.18
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth
- Config: configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py
In Collection: pspnet
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: 373.13
lr schd: 40000
memory (GB): 9.6
Name: pspnet_r101-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.34
mIoU(ms+flip): 79.74
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth
- Config: configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py
In Collection: pspnet
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: 568.18
lr schd: 40000
memory (GB): 6.9
Name: pspnet_r50-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.26
mIoU(ms+flip): 79.88
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth
- Config: configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py
In Collection: pspnet
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: 869.57
lr schd: 40000
memory (GB): 10.9
Name: pspnet_r101-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.08
mIoU(ms+flip): 80.28
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth
- Config: configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-18-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 63.65
lr schd: 80000
memory (GB): 1.7
Name: pspnet_r18-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 74.87
mIoU(ms+flip): 76.04
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth
- Config: configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: pspnet_r50-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.55
mIoU(ms+flip): 79.79
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth
- Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: pspnet_r101-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.76
mIoU(ms+flip): 81.01
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth
- Config: configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-18-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 161.29
lr schd: 80000
memory (GB): 1.9
Name: pspnet_r18-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 75.9
mIoU(ms+flip): 77.86
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth
- Config: configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: pspnet_r50-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.59
mIoU(ms+flip): 80.69
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth
- Config: configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: pspnet_r101-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.77
mIoU(ms+flip): 81.06
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth
- Config: configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-18b-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 61.43
lr schd: 80000
memory (GB): 1.5
Name: pspnet_r18b-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 74.23
mIoU(ms+flip): 75.79
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth
- Config: configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-50b-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 232.56
lr schd: 80000
memory (GB): 6.0
Name: pspnet_r50b-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.22
mIoU(ms+flip): 79.46
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth
- Config: configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-101b-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 362.32
lr schd: 80000
memory (GB): 9.5
Name: pspnet_r101b-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.69
mIoU(ms+flip): 80.79
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth
- Config: configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-18b-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 156.01
lr schd: 80000
memory (GB): 1.7
Name: pspnet_r18b-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 74.92
mIoU(ms+flip): 76.9
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth
- Config: configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-50b-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 531.91
lr schd: 80000
memory (GB): 6.8
Name: pspnet_r50b-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.5
mIoU(ms+flip): 79.96
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth
- Config: configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-101b-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 854.7
lr schd: 80000
memory (GB): 10.8
Name: pspnet_r101b-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.87
mIoU(ms+flip): 80.04
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth
- Config: configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py
In Collection: pspnet
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: 42.5
lr schd: 80000
memory (GB): 8.5
Name: pspnet_r50-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 41.13
mIoU(ms+flip): 41.94
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth
- Config: configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py
In Collection: pspnet
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: 65.36
lr schd: 80000
memory (GB): 12.0
Name: pspnet_r101-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 43.57
mIoU(ms+flip): 44.35
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth
- Config: configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py
In Collection: pspnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: pspnet_r50-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 42.48
mIoU(ms+flip): 43.44
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth
- Config: configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py
In Collection: pspnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: pspnet_r101-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 44.39
mIoU(ms+flip): 45.35
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth
- Config: configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py
In Collection: pspnet
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: 42.39
lr schd: 20000
memory (GB): 6.1
Name: pspnet_r50-d8_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.78
mIoU(ms+flip): 77.61
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth
- Config: configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py
In Collection: pspnet
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: 66.58
lr schd: 20000
memory (GB): 9.6
Name: pspnet_r101-d8_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.47
mIoU(ms+flip): 79.25
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth
- Config: configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py
In Collection: pspnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Name: pspnet_r50-d8_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.29
mIoU(ms+flip): 78.48
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth
- Config: configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py
In Collection: pspnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Name: pspnet_r101-d8_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.52
mIoU(ms+flip): 79.57
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth
- Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py
In Collection: pspnet
Metadata:
backbone: R-101-D8
crop size: (480,480)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (480,480)
value: 103.31
lr schd: 40000
memory (GB): 8.8
Name: pspnet_r101-d8_480x480_40k_pascal_context
Results:
Dataset: Pascal Context
Metrics:
mIoU: 46.6
mIoU(ms+flip): 47.78
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth
- Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py
In Collection: pspnet
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Name: pspnet_r101-d8_480x480_80k_pascal_context
Results:
Dataset: Pascal Context
Metrics:
mIoU: 46.03
mIoU(ms+flip): 47.15
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth
- Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py
In Collection: pspnet
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
Name: pspnet_r101-d8_480x480_40k_pascal_context_59
Results:
Dataset: Pascal Context 59
Metrics:
mIoU: 52.02
mIoU(ms+flip): 53.54
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59_20210416_114524-86d44cd4.pth
- Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py
In Collection: pspnet
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Name: pspnet_r101-d8_480x480_80k_pascal_context_59
Results:
Dataset: Pascal Context 59
Metrics:
mIoU: 52.47
mIoU(ms+flip): 53.99
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59_20210416_114418-fa6caaa2.pth