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pspnet.yml
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Collections:
- Name: PSPNet
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
- Dark Zurich and Nighttime Driving
- COCO-Stuff 10k
- COCO-Stuff 164k
- LoveDA
- Potsdam
- Vaihingen
- iSAID
Paper:
URL: https://arxiv.org/abs/1612.01105
Title: Pyramid Scene Parsing Network
README: configs/pspnet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
Version: v0.17.0
Converted From:
Code: https://github.com/hszhao/PSPNet
Models:
- Name: pspnet_r50-d8_512x1024_40k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 245.7
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.85
mIoU(ms+flip): 79.18
Config: configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py
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
- Name: pspnet_r101-d8_512x1024_40k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 373.13
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.34
mIoU(ms+flip): 79.74
Config: configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py
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
- Name: pspnet_r50-d8_769x769_40k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50-D8
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): 6.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.26
mIoU(ms+flip): 79.88
Config: configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py
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
- Name: pspnet_r101-d8_769x769_40k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 869.57
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 10.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.08
mIoU(ms+flip): 80.28
Config: configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py
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
- Name: pspnet_r18-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-18-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 63.65
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: 74.87
mIoU(ms+flip): 76.04
Config: configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py
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
- Name: pspnet_r50-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.55
mIoU(ms+flip): 79.79
Config: configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py
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
- Name: pspnet_r101-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.76
mIoU(ms+flip): 81.01
Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py
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
- Name: pspnet_r101-d8_fp16_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 114.03
hardware: V100
backend: PyTorch
batch size: 1
mode: FP16
resolution: (512,1024)
Training Memory (GB): 5.34
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.46
Config: configs/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes/pspnet_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230919-a0875e5c.pth
- Name: pspnet_r18-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-18-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 161.29
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 1.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.9
mIoU(ms+flip): 77.86
Config: configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py
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
- Name: pspnet_r50-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.59
mIoU(ms+flip): 80.69
Config: configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py
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
- Name: pspnet_r101-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.77
mIoU(ms+flip): 81.06
Config: configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py
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
- Name: pspnet_r18b-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-18b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 61.43
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 1.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.23
mIoU(ms+flip): 75.79
Config: configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py
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
- Name: pspnet_r50b-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 232.56
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 6.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.22
mIoU(ms+flip): 79.46
Config: configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py
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
- Name: pspnet_r101b-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-101b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 362.32
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 9.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.69
mIoU(ms+flip): 80.79
Config: configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py
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
- Name: pspnet_r18b-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-18b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 156.01
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 1.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.92
mIoU(ms+flip): 76.9
Config: configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py
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
- Name: pspnet_r50b-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-50b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 531.91
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 6.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.5
mIoU(ms+flip): 79.96
Config: configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py
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
- Name: pspnet_r101b-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
backbone: R-101b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 854.7
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 10.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.87
mIoU(ms+flip): 80.04
Config: configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py
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
- Name: pspnet_r50-d8_512x512_80k_ade20k
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 42.5
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 8.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.13
mIoU(ms+flip): 41.94
Config: configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py
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
- Name: pspnet_r101-d8_512x512_80k_ade20k
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 65.36
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 12.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.57
mIoU(ms+flip): 44.35
Config: configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py
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
- Name: pspnet_r50-d8_512x512_160k_ade20k
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.48
mIoU(ms+flip): 43.44
Config: configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py
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
- Name: pspnet_r101-d8_512x512_160k_ade20k
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.39
mIoU(ms+flip): 45.35
Config: configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py
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
- Name: pspnet_r50-d8_512x512_20k_voc12aug
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 42.39
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.78
mIoU(ms+flip): 77.61
Config: configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py
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
- Name: pspnet_r101-d8_512x512_20k_voc12aug
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 66.58
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.47
mIoU(ms+flip): 79.25
Config: configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py
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
- Name: pspnet_r50-d8_512x512_40k_voc12aug
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.29
mIoU(ms+flip): 78.48
Config: configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py
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
- Name: pspnet_r101-d8_512x512_40k_voc12aug
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.52
mIoU(ms+flip): 79.57
Config: configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py
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
- Name: pspnet_r101-d8_480x480_40k_pascal_context
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
inference time (ms/im):
- value: 103.31
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (480,480)
Training Memory (GB): 8.8
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 46.6
mIoU(ms+flip): 47.78
Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py
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
- Name: pspnet_r101-d8_480x480_80k_pascal_context
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 46.03
mIoU(ms+flip): 47.15
Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py
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
- Name: pspnet_r101-d8_480x480_40k_pascal_context_59
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context 59
Metrics:
mIoU: 52.02
mIoU(ms+flip): 53.54
Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py
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
- Name: pspnet_r101-d8_480x480_80k_pascal_context_59
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context 59
Metrics:
mIoU: 52.47
mIoU(ms+flip): 53.99
Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py
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
- Name: pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 48.78
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 35.69
mIoU(ms+flip): 36.62
Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258-b88df27f.pth
- Name: pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 90.09
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 13.2
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 37.26
mIoU(ms+flip): 38.52
Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135-76aae482.pth
- Name: pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 36.33
mIoU(ms+flip): 37.24
Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857-92e2902b.pth
- Name: pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 37.76
mIoU(ms+flip): 38.86
Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022-831aec95.pth
- Name: pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 48.78
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 38.8
mIoU(ms+flip): 39.19
Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-0e41b2db.pth
- Name: pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 90.09
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 13.2
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 40.34
mIoU(ms+flip): 40.79
Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-7eb41789.pth
- Name: pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 39.64
mIoU(ms+flip): 39.97
Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-51276a57.pth
- Name: pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 41.28
mIoU(ms+flip): 41.66
Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-4af9621b.pth
- Name: pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 320000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 40.53
mIoU(ms+flip): 40.75
Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-be9610cc.pth
- Name: pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 320000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 41.95
mIoU(ms+flip): 42.42
Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-72220c60.pth
- Name: pspnet_r18-d8_512x512_80k_loveda
In Collection: PSPNet
Metadata:
backbone: R-18-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 37.22
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 1.45
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
Metrics:
mIoU: 48.62
mIoU(ms+flip): 47.57
Config: configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100-b97697f1.pth
- Name: pspnet_r50-d8_512x512_80k_loveda
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 151.52
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.14
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
Metrics:
mIoU: 50.46
mIoU(ms+flip): 50.19
Config: configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728-88610f9f.pth
- Name: pspnet_r101-d8_512x512_80k_loveda
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 218.34
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.61
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
Metrics:
mIoU: 51.86
mIoU(ms+flip): 51.34
Config: configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212-1c06c6a8.pth
- Name: pspnet_r18-d8_4x4_512x512_80k_potsdam
In Collection: PSPNet
Metadata:
backbone: R-18-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 11.75
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 1.5
Results:
- Task: Semantic Segmentation
Dataset: Potsdam
Metrics:
mIoU: 77.09
mIoU(ms+flip): 78.3
Config: configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612-7cd046e1.pth
- Name: pspnet_r50-d8_4x4_512x512_80k_potsdam
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 33.1
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.14
Results:
- Task: Semantic Segmentation
Dataset: Potsdam
Metrics:
mIoU: 78.12
mIoU(ms+flip): 78.98
Config: configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541-2dd5fe67.pth
- Name: pspnet_r101-d8_4x4_512x512_80k_potsdam
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 51.55
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.61
Results:
- Task: Semantic Segmentation
Dataset: Potsdam
Metrics:
mIoU: 78.62
mIoU(ms+flip): 79.47
Config: configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612-aed036c4.pth
- Name: pspnet_r18-d8_4x4_512x512_80k_vaihingen
In Collection: PSPNet
Metadata:
backbone: R-18-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 11.76
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 1.45
Results:
- Task: Semantic Segmentation
Dataset: Vaihingen
Metrics:
mIoU: 71.46
mIoU(ms+flip): 73.36
Config: configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen/pspnet_r18-d8_4x4_512x512_80k_vaihingen_20211228_160355-52a8a6f6.pth
- Name: pspnet_r50-d8_4x4_512x512_80k_vaihingen
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 33.01
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.14
Results:
- Task: Semantic Segmentation
Dataset: Vaihingen
Metrics:
mIoU: 72.36
mIoU(ms+flip): 73.75
Config: configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355-382f8f5b.pth
- Name: pspnet_r101-d8_4x4_512x512_80k_vaihingen
In Collection: PSPNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 50.08
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.61
Results:
- Task: Semantic Segmentation
Dataset: Vaihingen
Metrics:
mIoU: 72.61
mIoU(ms+flip): 74.18
Config: configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806-8eba0a09.pth
- Name: pspnet_r18-d8_4x4_896x896_80k_isaid
In Collection: PSPNet
Metadata:
backbone: R-18-D8
crop size: (896,896)
lr schd: 80000
inference time (ms/im):
- value: 37.16
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (896,896)
Training Memory (GB): 4.52
Results:
- Task: Semantic Segmentation
Dataset: iSAID
Metrics:
mIoU: 60.22
mIoU(ms+flip): 61.25
Config: configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526-e84c0b6a.pth
- Name: pspnet_r50-d8_4x4_896x896_80k_isaid
In Collection: PSPNet
Metadata:
backbone: R-50-D8
crop size: (896,896)
lr schd: 80000
inference time (ms/im):
- value: 112.61
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (896,896)
Training Memory (GB): 16.58
Results:
- Task: Semantic Segmentation
Dataset: iSAID
Metrics:
mIoU: 65.36
mIoU(ms+flip): 66.48
Config: configs/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629-1f21dc32.pth