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[Feature] support mim (#549)

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* dice loss

* format code, add docstring and calculate denominator without valid_mask

* minor change

* restore

* add metafile

* add manifest.in and add config at setup.py

* add requirements

* modify manifest

* modify manifest

* Update MANIFEST.in

* add metafile

* add metadata

* fix typo

* Update metafile.yml

* Update metafile.yml

* minor change

* Update metafile.yml

* add subfix

* fix mmshow

* add more  metafile

* add config to model_zoo

* fix bug

* Update mminstall.txt

* [fix] Add models

* [Fix] Add collections

* [fix] Modify collection name

* [Fix] Set datasets to unet metafile

* [Fix] Modify collection names

* complement inference time
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谢昕辰 2021-06-01 06:07:24 +08:00 committed by GitHub
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include requirements/*.txt
include mmseg/model_zoo.yml
recursive-include mmseg/configs *.py *.yml
recursive-include mmseg/tools *.sh *.py

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Collections:
- Name: ANN
Metadata:
Training Data:
- Cityscapes
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: ann_r50-d8_512x1024_40k_cityscapes
In Collection: ANN
Metadata:
inference time (fps): 3.71
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.40
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth
Config: configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py
- Name: ann_r101-d8_512x1024_40k_cityscapes
In Collection: ANN
Metadata:
inference time (fps): 2.55
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.55
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth
Config: configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py
- Name: ann_r50-d8_769x769_40k_cityscapes
In Collection: ANN
Metadata:
inference time (fps): 1.70
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.89
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth
Config: configs/ann/ann_r50-d8_769x769_40k_cityscapes.py
- Name: ann_r101-d8_769x769_40k_cityscapes
In Collection: ANN
Metadata:
inference time (fps): 1.15
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.32
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth
Config: configs/ann/ann_r101-d8_769x769_40k_cityscapes.py
- Name: ann_r50-d8_512x1024_80k_cityscapes
In Collection: ANN
Metadata:
inference time (fps): 3.71
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.34
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth
Config: configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py
- Name: ann_r101-d8_512x1024_80k_cityscapes
In Collection: ANN
Metadata:
inference time (fps): 2.55
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.14
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth
Config: configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py
- Name: ann_r50-d8_769x769_80k_cityscapes
In Collection: ANN
Metadata:
inference time (fps): 1.70
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.88
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth
Config: configs/ann/ann_r50-d8_769x769_80k_cityscapes.py
- Name: ann_r101-d8_769x769_80k_cityscapes
In Collection: ANN
Metadata:
inference time (fps):
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.80
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth
Config: configs/ann/ann_r101-d8_769x769_80k_cityscapes.py
- Name: ann_r50-d8_512x512_80k_ade20k
In Collection: ANN
Metadata:
inference time (fps): 21.01
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.01
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth
Config: configs/ann/ann_r50-d8_512x512_80k_ade20k.py
- Name: ann_r101-d8_512x512_80k_ade20k
In Collection: ANN
Metadata:
inference time (fps): 14.12
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.94
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth
Config: configs/ann/ann_r101-d8_512x512_80k_ade20k.py
- Name: ann_r50-d8_512x512_160k_ade20k
In Collection: ANN
Metadata:
inference time (fps): 21.01
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.74
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth
Config: configs/ann/ann_r50-d8_512x512_160k_ade20k.py
- Name: ann_r101-d8_512x512_160k_ade20k
In Collection: ANN
Metadata:
inference time (fps): 14.12
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.94
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth
Config: configs/ann/ann_r101-d8_512x512_160k_ade20k.py
- Name: ann_r50-d8_512x512_20k_voc12aug
In Collection: ANN
Metadata:
inference time (fps): 20.92
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 74.86
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth
Config: configs/ann/ann_r50-d8_512x512_20k_voc12aug.py
- Name: ann_r101-d8_512x512_20k_voc12aug
In Collection: ANN
Metadata:
inference time (fps): 13.94
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.47
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth
Config: configs/ann/ann_r101-d8_512x512_20k_voc12aug.py
- Name: ann_r50-d8_512x512_40k_voc12aug
In Collection: ANN
Metadata:
inference time (fps): 20.92
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.56
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth
Config: configs/ann/ann_r50-d8_512x512_40k_voc12aug.py
- Name: ann_r101-d8_512x512_40k_voc12aug
In Collection: ANN
Metadata:
inference time (fps): 13.94
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.70
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth
Config: configs/ann/ann_r101-d8_512x512_40k_voc12aug.py

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Collections:
- Name: APCNet
Metadata:
Training Data:
- Cityscapes
- ADE20K
Models:
- Name: apcnet_r50-d8_512x1024_40k_cityscapes
In Collection: APCNet
Metadata:
inference time (fps): 3.57
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.02
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth
Config: configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py
- Name: apcnet_r101-d8_512x1024_40k_cityscapes
In Collection: APCNet
Metadata:
inference time (fps): 2.15
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.08
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth
Config: configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py
- Name: apcnet_r50-d8_769x769_40k_cityscapes
In Collection: APCNet
Metadata:
inference time (fps): 1.52
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.89
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth
Config: configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py
- Name: apcnet_r101-d8_769x769_40k_cityscapes
In Collection: APCNet
Metadata:
inference time (fps): 1.03
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.96
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth
Config: configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py
- Name: apcnet_r50-d8_512x1024_80k_cityscapes
In Collection: APCNet
Metadata:
inference time (fps): 3.57
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.96
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth
Config: configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py
- Name: apcnet_r101-d8_512x1024_80k_cityscapes
In Collection: APCNet
Metadata:
inference time (fps): 2.15
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.64
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth
Config: configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py
- Name: apcnet_r50-d8_769x769_80k_cityscapes
In Collection: APCNet
Metadata:
inference time (fps): 1.52
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.79
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth
Config: configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py
- Name: apcnet_r101-d8_769x769_80k_cityscapes
In Collection: APCNet
Metadata:
inference time (fps): 1.03
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.45
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth
Config: configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py
- Name: apcnet_r50-d8_512x512_80k_ade20k
In Collection: APCNet
Metadata:
inference time (fps): 19.61
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.20
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth
Config: configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py
- Name: apcnet_r101-d8_512x512_80k_ade20k
In Collection: APCNet
Metadata:
inference time (fps): 13.10
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.54
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth
Config: configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py
- Name: apcnet_r50-d8_512x512_160k_ade20k
In Collection: APCNet
Metadata:
inference time (fps): 19.61
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.40
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth
Config: configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py
- Name: apcnet_r101-d8_512x512_160k_ade20k
In Collection: APCNet
Metadata:
inference time (fps): 13.10
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.41
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth
Config: configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py

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Collections:
- Name: CCNet
Metadata:
Training Data:
- Cityscapes
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: ccnet_r50-d8_512x1024_40k_cityscapes
In Collection: CCNet
Metadata:
inference time (fps): 3.32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.76
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth
Config: configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py
- Name: ccnet_r101-d8_512x1024_40k_cityscapes
In Collection: CCNet
Metadata:
inference time (fps): 2.31
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.35
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth
Config: configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py
- Name: ccnet_r50-d8_769x769_40k_cityscapes
In Collection: CCNet
Metadata:
inference time (fps): 1.43
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.46
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth
Config: configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py
- Name: ccnet_r101-d8_769x769_40k_cityscapes
In Collection: CCNet
Metadata:
inference time (fps): 1.01
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.94
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth
Config: configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py
- Name: ccnet_r50-d8_512x1024_80k_cityscapes
In Collection: CCNet
Metadata:
inference time (fps): 3.32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.03
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth
Config: configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py
- Name: ccnet_r101-d8_512x1024_80k_cityscapes
In Collection: CCNet
Metadata:
inference time (fps): 2.31
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.87
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth
Config: configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py
- Name: ccnet_r50-d8_769x769_80k_cityscapes
In Collection: CCNet
Metadata:
inference time (fps): 1.43
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.29
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth
Config: configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py
- Name: ccnet_r101-d8_769x769_80k_cityscapes
In Collection: CCNet
Metadata:
inference time (fps): 1.01
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.45
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth
Config: configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py
- Name: ccnet_r50-d8_512x512_80k_ade20k
In Collection: CCNet
Metadata:
inference time (fps): 20.89
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.78
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth
Config: configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py
- Name: ccnet_r101-d8_512x512_80k_ade20k
In Collection: CCNet
Metadata:
inference time (fps): 14.11
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.97
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth
Config: configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py
- Name: ccnet_r50-d8_512x512_160k_ade20k
In Collection: CCNet
Metadata:
inference time (fps): 20.89
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.08
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth
Config: configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py
- Name: ccnet_r101-d8_512x512_160k_ade20k
In Collection: CCNet
Metadata:
inference time (fps): 14.11
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.71
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth
Config: configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py
- Name: ccnet_r50-d8_512x512_20k_voc12aug
In Collection: CCNet
Metadata:
inference time (fps): 20.45
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.17
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth
Config: configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py
- Name: ccnet_r101-d8_512x512_20k_voc12aug
In Collection: CCNet
Metadata:
inference time (fps): 13.64
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.27
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth
Config: configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py
- Name: ccnet_r50-d8_512x512_40k_voc12aug
In Collection: CCNet
Metadata:
inference time (fps): 20.45
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 75.96
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth
Config: configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py
- Name: ccnet_r101-d8_512x512_40k_voc12aug
In Collection: CCNet
Metadata:
inference time (fps): 13.64
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.87
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth
Config: configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py

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Collections:
- Name: CGNet
Metadata:
Training Data:
- Cityscapes
Models:
- Name: cgnet_680x680_60k_cityscapes
In Collection: CGNet
Metadata:
inference time (fps): 30.51
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 65.63
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth
Config: configs/cgnet/cgnet_680x680_60k_cityscapes.py
- Name: cgnet_512x1024_60k_cityscapes
In Collection: CGNet
Metadata:
inference time (fps): 31.14
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 68.27
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth
Config: configs/cgnet/cgnet_512x1024_60k_cityscapes.py

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Collections:
- Name: DANet
Metadata:
Training Data:
- Cityscapes
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: danet_r50-d8_512x1024_40k_cityscapes
In Collection: DANet
Metadata:
inference time (fps): 2.66
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.74
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth
Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py
- Name: danet_r101-d8_512x1024_40k_cityscapes
In Collection: DANet
Metadata:
inference time (fps): 1.99
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.52
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth
Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py
- Name: danet_r50-d8_769x769_40k_cityscapes
In Collection: DANet
Metadata:
inference time (fps): 1.56
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.88
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth
Config: configs/danet/danet_r50-d8_769x769_40k_cityscapes.py
- Name: danet_r101-d8_769x769_40k_cityscapes
In Collection: DANet
Metadata:
inference time (fps): 1.07
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.88
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth
Config: configs/danet/danet_r101-d8_769x769_40k_cityscapes.py
- Name: danet_r50-d8_512x1024_80k_cityscapes
In Collection: DANet
Metadata:
inference time (fps): 2.66
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.34
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth
Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py
- Name: danet_r101-d8_512x1024_80k_cityscapes
In Collection: DANet
Metadata:
inference time (fps): 1.99
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.41
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth
Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py
- Name: danet_r50-d8_769x769_80k_cityscapes
In Collection: DANet
Metadata:
inference time (fps): 1.56
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.27
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth
Config: configs/danet/danet_r50-d8_769x769_80k_cityscapes.py
- Name: danet_r101-d8_769x769_80k_cityscapes
In Collection: DANet
Metadata:
inference time (fps): 1.07
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.47
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth
Config: configs/danet/danet_r101-d8_769x769_80k_cityscapes.py
- Name: danet_r50-d8_512x512_80k_ade20k
In Collection: DANet
Metadata:
inference time (fps): 21.20
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.66
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth
Config: configs/danet/danet_r50-d8_512x512_80k_ade20k.py
- Name: danet_r101-d8_512x512_80k_ade20k
In Collection: DANet
Metadata:
inference time (fps): 14.18
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.64
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth
Config: configs/danet/danet_r101-d8_512x512_80k_ade20k.py
- Name: danet_r50-d8_512x512_160k_ade20k
In Collection: DANet
Metadata:
inference time (fps): 21.20
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.45
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth
Config: configs/danet/danet_r50-d8_512x512_160k_ade20k.py
- Name: danet_r101-d8_512x512_160k_ade20k
In Collection: DANet
Metadata:
inference time (fps): 14.18
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.17
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth
Config: configs/danet/danet_r101-d8_512x512_160k_ade20k.py
- Name: danet_r50-d8_512x512_20k_voc12aug
In Collection: DANet
Metadata:
inference time (fps): 20.94
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 74.45
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth
Config: configs/danet/danet_r50-d8_512x512_20k_voc12aug.py
- Name: danet_r101-d8_512x512_20k_voc12aug
In Collection: DANet
Metadata:
inference time (fps): 13.76
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.02
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth
Config: configs/danet/danet_r101-d8_512x512_20k_voc12aug.py
- Name: danet_r50-d8_512x512_40k_voc12aug
In Collection: DANet
Metadata:
inference time (fps): 20.94
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.37
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth
Config: configs/danet/danet_r50-d8_512x512_40k_voc12aug.py
- Name: danet_r101-d8_512x512_40k_voc12aug
In Collection: DANet
Metadata:
inference time (fps): 13.76
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.51
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth
Config: configs/danet/danet_r101-d8_512x512_40k_voc12aug.py

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Collections:
- Name: DeepLabV3
Metadata:
Training Data:
- Cityscapes
- Pascal Context
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: deeplabv3_r50-d8_512x1024_40k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 2.57
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.09
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth
Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py
- Name: deeplabv3_r101-d8_512x1024_40k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 1.92
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.12
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth
Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py
- Name: deeplabv3_r50-d8_769x769_40k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 1.11
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.58
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth
Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py
- Name: deeplabv3_r101-d8_769x769_40k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 0.83
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.27
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth
Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py
- Name: deeplabv3_r18-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 13.78
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.70
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth
Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3_r50-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 2.57
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.32
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth
Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3_r101-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 1.92
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.20
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth
Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3_r18-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 5.55
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.60
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth
Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py
- Name: deeplabv3_r50-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 1.11
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.89
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth
Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py
- Name: deeplabv3_r101-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 0.83
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.67
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth
Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py
- Name: deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 6.96
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.71
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-67b0c992.pth
Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py
- Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 6.96
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.36
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth
Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py
- Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 13.93
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.26
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth
Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 2.74
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.63
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth
Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 1.81
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.01
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth
Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3_r18b-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 5.79
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.63
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth
Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py
- Name: deeplabv3_r50b-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 1.16
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.80
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth
Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py
- Name: deeplabv3_r101b-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 0.82
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.41
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth
Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py
- Name: deeplabv3_r50-d8_512x512_80k_ade20k
In Collection: DeepLabV3
Metadata:
inference time (fps): 14.76
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.42
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py
- Name: deeplabv3_r101-d8_512x512_80k_ade20k
In Collection: DeepLabV3
Metadata:
inference time (fps): 10.14
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.08
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py
- Name: deeplabv3_r50-d8_512x512_160k_ade20k
In Collection: DeepLabV3
Metadata:
inference time (fps): 14.76
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.66
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py
- Name: deeplabv3_r101-d8_512x512_160k_ade20k
In Collection: DeepLabV3
Metadata:
inference time (fps): 10.14
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.00
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py
- Name: deeplabv3_r50-d8_512x512_20k_voc12aug
In Collection: DeepLabV3
Metadata:
inference time (fps): 13.88
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.17
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py
- Name: deeplabv3_r101-d8_512x512_20k_voc12aug
In Collection: DeepLabV3
Metadata:
inference time (fps): 9.81
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.70
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py
- Name: deeplabv3_r50-d8_512x512_40k_voc12aug
In Collection: DeepLabV3
Metadata:
inference time (fps): 13.88
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.68
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py
- Name: deeplabv3_r101-d8_512x512_40k_voc12aug
In Collection: DeepLabV3
Metadata:
inference time (fps): 9.81
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.92
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py
- Name: deeplabv3_r101-d8_480x480_40k_pascal_context
In Collection: DeepLabV3
Metadata:
inference time (fps): 7.09
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 46.55
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth
Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py
- Name: deeplabv3_r101-d8_480x480_80k_pascal_context
In Collection: DeepLabV3
Metadata:
inference time (fps): 7.09
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 46.42
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth
Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py
- Name: deeplabv3_r101-d8_480x480_40k_pascal_context
In Collection: DeepLabV3
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 52.61
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth
Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py
- Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59
In Collection: DeepLabV3
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 52.46
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth
Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py

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Collections:
- Name: DeepLabV3+
Metadata:
Training Data:
- Cityscapes
- Pascal Context
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: deeplabv3plus_r50-d8_512x1024_40k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 3.94
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.61
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth
Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py
- Name: deeplabv3plus_r101-d8_512x1024_40k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 2.60
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.21
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth
Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py
- Name: deeplabv3plus_r50-d8_769x769_40k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 1.72
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.97
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth
Config: configs/deeplabv3+/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py
- Name: deeplabv3plus_r101-d8_769x769_40k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 1.15
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.46
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth
Config: configs/deeplabv3+/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py
- Name: deeplabv3plus_r18-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 14.27
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.89
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth
Config: configs/deeplabv3+/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3plus_r50-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 3.94
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.09
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth
Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3plus_r101-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 2.60
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.97
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth
Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3plus_r18-d8_769x769_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 5.74
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.26
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth
Config: configs/deeplabv3+/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py
- Name: deeplabv3plus_r50-d8_769x769_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 1.72
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.83
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth
Config: configs/deeplabv3+/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py
- Name: deeplabv3plus_r101-d8_769x769_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 1.15
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.98
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405-a7573d20.pth
Config: configs/deeplabv3+/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py
- Name: deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 7.48
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.09
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth
Config: configs/deeplabv3+/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py
- Name: deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 7.48
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.90
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth
Config: configs/deeplabv3+/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py
- Name: deeplabv3plus_r18b-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 14.95
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.87
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth
Config: configs/deeplabv3+/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3plus_r50b-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 3.94
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.28
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth
Config: configs/deeplabv3+/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3plus_r101b-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 2.60
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.16
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth
Config: configs/deeplabv3+/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3plus_r18b-d8_769x769_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 5.96
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.36
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth
Config: configs/deeplabv3+/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py
- Name: deeplabv3plus_r50b-d8_769x769_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 1.72
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.41
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth
Config: configs/deeplabv3+/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py
- Name: deeplabv3plus_r101b-d8_769x769_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 1.10
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.88
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth
Config: configs/deeplabv3+/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py
- Name: deeplabv3plus_r50-d8_512x512_80k_ade20k
In Collection: DeepLabV3+
Metadata:
inference time (fps): 21.01
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.72
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth
Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_80k_ade20k.py
- Name: deeplabv3plus_r101-d8_512x512_80k_ade20k
In Collection: DeepLabV3+
Metadata:
inference time (fps): 14.16
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.60
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth
Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_80k_ade20k.py
- Name: deeplabv3plus_r50-d8_512x512_160k_ade20k
In Collection: DeepLabV3+
Metadata:
inference time (fps): 21.01
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.95
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth
Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_160k_ade20k.py
- Name: deeplabv3plus_r101-d8_512x512_160k_ade20k
In Collection: DeepLabV3+
Metadata:
inference time (fps): 14.16
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.47
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth
Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_160k_ade20k.py
- Name: deeplabv3plus_r50-d8_512x512_20k_voc12aug
In Collection: DeepLabV3+
Metadata:
inference time (fps): 21
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 75.93
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth
Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py
- Name: deeplabv3plus_r101-d8_512x512_20k_voc12aug
In Collection: DeepLabV3+
Metadata:
inference time (fps): 13.88
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.22
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth
Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py
- Name: deeplabv3plus_r50-d8_512x512_40k_voc12aug
In Collection: DeepLabV3+
Metadata:
inference time (fps): 21
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.81
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth
Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py
- Name: deeplabv3plus_r101-d8_512x512_40k_voc12aug
In Collection: DeepLabV3+
Metadata:
inference time (fps): 13.88
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.62
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth
Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py
- Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context
In Collection: DeepLabV3+
Metadata:
inference time (fps): 9.09
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 47.30
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth
Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py
- Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
In Collection: DeepLabV3+
Metadata:
inference time (fps): 9.09
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 47.23
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth
Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py
- Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context
In Collection: DeepLabV3+
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 52.86
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth
Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py
- Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
In Collection: DeepLabV3+
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 53.2
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth
Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py

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Collections:
- Name: DMNet
Metadata:
Training Data:
- Cityscapes
- ADE20K
Models:
- Name: dmnet_r50-d8_512x1024_40k_cityscapes
In Collection: DMNet
Metadata:
inference time (fps): 3.66
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.78
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth
Config: configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py
- Name: dmnet_r101-d8_512x1024_40k_cityscapes
In Collection: DMNet
Metadata:
inference time (fps): 2.54
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.37
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth
Config: configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py
- Name: dmnet_r50-d8_769x769_40k_cityscapes
In Collection: DMNet
Metadata:
inference time (fps): 1.57
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.49
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth
Config: configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py
- Name: dmnet_r101-d8_769x769_40k_cityscapes
In Collection: DMNet
Metadata:
inference time (fps): 1.01
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.62
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth
Config: configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py
- Name: dmnet_r50-d8_512x1024_80k_cityscapes
In Collection: DMNet
Metadata:
inference time (fps): 3.66
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.07
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth
Config: configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py
- Name: dmnet_r101-d8_512x1024_80k_cityscapes
In Collection: DMNet
Metadata:
inference time (fps): 2.54
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.64
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth
Config: configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py
- Name: dmnet_r50-d8_769x769_80k_cityscapes
In Collection: DMNet
Metadata:
inference time (fps): 1.57
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.22
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth
Config: configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py
- Name: dmnet_r101-d8_769x769_80k_cityscapes
In Collection: DMNet
Metadata:
inference time (fps): 1.01
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.19
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth
Config: configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py
- Name: dmnet_r50-d8_512x512_80k_ade20k
In Collection: DMNet
Metadata:
inference time (fps): 20.95
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.37
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth
Config: configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py
- Name: dmnet_r101-d8_512x512_80k_ade20k
In Collection: DMNet
Metadata:
inference time (fps): 13.88
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.34
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth
Config: configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py
- Name: dmnet_r50-d8_512x512_160k_ade20k
In Collection: DMNet
Metadata:
inference time (fps): 20.95
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.15
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth
Config: configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py
- Name: dmnet_r101-d8_512x512_160k_ade20k
In Collection: DMNet
Metadata:
inference time (fps): 13.88
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.42
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth
Config: configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py

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Collections:
- Name: dnl
Metadata:
Training Data:
- Cityscapes
- ADE20K
Models:
- Name: dnl_r50-d8_512x1024_40k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 2.56
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.61
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth
Config: configs/dnl/dnl_r50-d8_512x1024_40k_cityscapes.py
- Name: dnl_r101-d8_512x1024_40k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 1.96
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.31
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth
Config: configs/dnl/dnl_r101-d8_512x1024_40k_cityscapes.py
- Name: dnl_r50-d8_769x769_40k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 1.50
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.44
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth
Config: configs/dnl/dnl_r50-d8_769x769_40k_cityscapes.py
- Name: dnl_r101-d8_769x769_40k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 1.02
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.39
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth
Config: configs/dnl/dnl_r101-d8_769x769_40k_cityscapes.py
- Name: dnl_r50-d8_512x1024_80k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 2.56
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.33
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth
Config: configs/dnl/dnl_r50-d8_512x1024_80k_cityscapes.py
- Name: dnl_r101-d8_512x1024_80k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 1.96
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.41
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth
Config: configs/dnl/dnl_r101-d8_512x1024_80k_cityscapes.py
- Name: dnl_r50-d8_769x769_80k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 1.50
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.36
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth
Config: configs/dnl/dnl_r50-d8_769x769_80k_cityscapes.py
- Name: dnl_r101-d8_769x769_80k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 1.02
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.41
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth
Config: configs/dnl/dnl_r101-d8_769x769_80k_cityscapes.py
- Name: dnl_r50-d8_512x512_80k_ade20k
In Collection: dnl
Metadata:
inference time (fps): 20.66
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.76
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth
Config: configs/dnl/dnl_r50-d8_512x512_80k_ade20k.py
- Name: dnl_r101-d8_512x512_80k_ade20k
In Collection: dnl
Metadata:
inference time (fps): 12.54
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.76
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth
Config: configs/dnl/dnl_r101-d8_512x512_80k_ade20k.py
- Name: dnl_r50-d8_512x512_160k_ade20k
In Collection: dnl
Metadata:
inference time (fps): 20.66
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.87
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth
Config: configs/dnl/dnl_r50-d8_512x512_160k_ade20k.py
- Name: dnl_r101-d8_512x512_160k_ade20k
In Collection: dnl
Metadata:
inference time (fps): 12.54
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.25
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth
Config: configs/dnl/dnl_r101-d8_512x512_160k_ade20k.py

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Collections:
- Name: EMANet
Metadata:
Training Data:
- Cityscapes
Models:
- Name: emanet_r50-d8_512x1024_80k_cityscapes
In Collection: EMANet
Metadata:
inference time (fps): 4.58
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.59
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth
Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py
- Name: emanet_r101-d8_512x1024_80k_cityscapes
In Collection: EMANet
Metadata:
inference time (fps): 2.87
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.10
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth
Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py
- Name: emanet_r50-d8_769x769_80k_cityscapes
In Collection: EMANet
Metadata:
inference time (fps): 1.97
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.33
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth
Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py
- Name: emanet_r101-d8_769x769_80k_cityscapes
In Collection: EMANet
Metadata:
inference time (fps): 1.22
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.62
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth
Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py

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Collections:
- Name: encnet
Metadata:
Training Data:
- Cityscapes
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: encnet_r50-d8_512x1024_40k_cityscapes
In Collection: encnet
Metadata:
inference time (fps): 4.58
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.67
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth
Config: configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py
- Name: encnet_r101-d8_512x1024_40k_cityscapes
In Collection: encnet
Metadata:
inference time (fps): 2.66
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.81
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth
Config: configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py
- Name: encnet_r50-d8_769x769_40k_cityscapes
In Collection: encnet
Metadata:
inference time (fps): 1.82
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.24
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth
Config: configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py
- Name: encnet_r101-d8_769x769_40k_cityscapes
In Collection: encnet
Metadata:
inference time (fps): 1.26
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.25
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth
Config: configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py
- Name: encnet_r50-d8_512x1024_80k_cityscapes
In Collection: encnet
Metadata:
inference time (fps): 4.58
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.94
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth
Config: configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py
- Name: encnet_r101-d8_512x1024_80k_cityscapes
In Collection: encnet
Metadata:
inference time (fps): 2.66
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.55
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth
Config: configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py
- Name: encnet_r50-d8_769x769_80k_cityscapes
In Collection: encnet
Metadata:
inference time (fps): 1.82
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.44
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth
Config: configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py
- Name: encnet_r101-d8_769x769_80k_cityscapes
In Collection: encnet
Metadata:
inference time (fps): 1.26
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.10
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth
Config: configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py
- Name: encnet_r50-d8_512x512_80k_ade20k
In Collection: encnet
Metadata:
inference time (fps): 22.81
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 39.53
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth
Config: configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py
- Name: encnet_r101-d8_512x512_80k_ade20k
In Collection: encnet
Metadata:
inference time (fps): 14.87
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.11
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth
Config: configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py
- Name: encnet_r50-d8_512x512_160k_ade20k
In Collection: encnet
Metadata:
inference time (fps): 22.81
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 40.10
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth
Config: configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py
- Name: encnet_r101-d8_512x512_160k_ade20k
In Collection: encnet
Metadata:
inference time (fps): 14.87
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.61
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth
Config: configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py

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Collections:
- Name: Fast-SCNN
Metadata:
Training Data:
- Cityscapes
Models:
- Name: fast_scnn_4x8_80k_lr0.12_cityscapes
In Collection: Fast-SCNN
Metadata:
inference time (fps): 63.61
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 69.06
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_4x8_80k_lr0.12_cityscapes-f5096c79.pth
Config: configs/fast-scnn/fast_scnn_4x8_80k_lr0.12_cityscapes.py

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Collections:
- Name: FCN
Metadata:
Training Data:
- Cityscapes
- Pascal Context
- Pascal VOC 2012 + Aug
- ADE20K
- Name: FCN-D6
Metadata:
Training Data:
- Cityscapes
- Pascal Context
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: fcn_r50-d8_512x1024_40k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 4.17
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 72.25
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth
Config: configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py
- Name: fcn_r101-d8_512x1024_40k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 2.66
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.45
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth
Config: configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py
- Name: fcn_r50-d8_769x769_40k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 1.80
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 71.47
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth
Config: configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py
- Name: fcn_r101-d8_769x769_40k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 1.19
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.93
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth
Config: configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py
- Name: fcn_r18-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 14.65
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 71.11
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth
Config: configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py
- Name: fcn_r50-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 4.17
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.61
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth
Config: configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py
- Name: fcn_r101-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 2.66
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.13
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth
Config: configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py
- Name: fcn_r18-d8_769x769_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 6.40
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 70.80
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth
Config: configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py
- Name: fcn_r50-d8_769x769_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 1.80
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 72.64
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth
Config: configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py
- Name: fcn_r101-d8_769x769_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 1.19
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.52
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth
Config: configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py
- Name: fcn_r18b-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 16.74
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 70.24
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth
Config: configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py
- Name: fcn_r50b-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 4.20
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.65
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth
Config: configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py
- Name: fcn_r101b-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 2.73
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.37
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth
Config: configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py
- Name: fcn_r18b-d8_769x769_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 6.70
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 69.66
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth
Config: configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py
- Name: fcn_r50b-d8_769x769_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 1.82
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.83
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth
Config: configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py
- Name: fcn_r101b-d8_769x769_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 1.15
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.02
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth
Config: configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py
- Name: fcn_d6_r50-d16_512x1024_40k_cityscapes
In Collection: FCN-D6
Metadata:
inference time (fps): 10.22
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.06
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth
Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_40k_cityscapes.py
- Name: fcn_d6_r50-d16_512x1024_80k_cityscapes
In Collection: FCN-D6
Metadata:
inference time (fps): 10.35
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.27
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth
Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_80k_cityscapes.py
- Name: fcn_d6_r50-d16_769x769_40k_cityscapes
In Collection: FCN-D6
Metadata:
inference time (fps): 4.17
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.82
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-1aab18ed.pth
Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_40k_cityscapes.py
- Name: fcn_d6_r50-d16_769x769_80k_cityscapes
In Collection: FCN-D6
Metadata:
inference time (fps): 4.15
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.04
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-109d88eb.pth
Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_80k_cityscapes.py
- Name: fcn_d6_r101-d16_512x1024_40k_cityscapes
In Collection: FCN-D6
Metadata:
inference time (fps): 8.04
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.36
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-9cf2b450.pth
Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_40k_cityscapes.py
- Name: fcn_d6_r101-d16_512x1024_80k_cityscapes
In Collection: FCN-D6
Metadata:
inference time (fps): 8.26
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.46
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-cb336445.pth
Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_80k_cityscapes.py
- Name: fcn_d6_r101-d16_769x769_40k_cityscapes
In Collection: FCN-D6
Metadata:
inference time (fps): 3.12
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.28
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-60b114e9.pth
Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_40k_cityscapes.py
- Name: fcn_d6_r101-d16_769x769_80k_cityscapes
In Collection: FCN-D6
Metadata:
inference time (fps): 3.21
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.06
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-e33adc4f.pth
Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_80k_cityscapes.py
- Name: fcn_r50-d8_512x512_80k_ade20k
In Collection: FCN
Metadata:
inference time (fps): 23.49
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 35.94
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth
Config: configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py
- Name: fcn_r101-d8_512x512_80k_ade20k
In Collection: FCN
Metadata:
inference time (fps): 14.78
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 39.61
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth
Config: configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py
- Name: fcn_r50-d8_512x512_160k_ade20k
In Collection: FCN
Metadata:
inference time (fps): 23.49
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 36.10
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth
Config: configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py
- Name: fcn_r101-d8_512x512_160k_ade20k
In Collection: FCN
Metadata:
inference time (fps): 14.78
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 39.91
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth
Config: configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py
- Name: fcn_r50-d8_512x512_20k_voc12aug
In Collection: FCN
Metadata:
inference time (fps): 23.28
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 67.08
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth
Config: configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py
- Name: fcn_r101-d8_512x512_20k_voc12aug
In Collection: FCN
Metadata:
inference time (fps): 14.81
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 71.16
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth
Config: configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py
- Name: fcn_r50-d8_512x512_40k_voc12aug
In Collection: FCN
Metadata:
inference time (fps): 23.28
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 66.97
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth
Config: configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py
- Name: fcn_r101-d8_512x512_40k_voc12aug
In Collection: FCN
Metadata:
inference time (fps): 14.81
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 69.91
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth
Config: configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py
- Name: fcn_r101-d8_480x480_40k_pascal_context
In Collection: FCN
Metadata:
inference time (fps): 9.93
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 44.43
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757-b5e97937.pth
Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py
- Name: fcn_r101-d8_480x480_80k_pascal_context
In Collection: FCN
Metadata:
inference time (fps): 9.93
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 44.13
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310-4711813f.pth
Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py
- Name: fcn_r101-d8_480x480_40k_pascal_context_59
In Collection: FCN
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 48.42
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth
Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py
- Name: fcn_r101-d8_480x480_80k_pascal_context_59
In Collection: FCN
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 49.35
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth
Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py

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Models:
- Name: fcn_r101-d8_512x1024_80k_fp16_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 8.64
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.80
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes/fcn_r101-d8_512x1024_80k_fp16_cityscapes-50245227.pth
Config: configs/fcn/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py
- Name: pspnet_r101-d8_512x1024_80k_fp16_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 8.77
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.46
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes/pspnet_r101-d8_512x1024_80k_fp16_cityscapes-ade37931.pth
Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py
- Name: deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 3.86
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.48
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes-bc86dc84.pth
Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py
- Name: deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 7.87
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.46
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes-cc58bc8d.pth
Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py

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Collections:
- Name: GCNet
Metadata:
Training Data:
- Cityscapes
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: gcnet_r50-d8_512x1024_40k_cityscapes
In Collection: GCNet
Metadata:
inference time (fps): 3.93
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.69
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436-4b0fd17b.pth
Config: configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py
- Name: gcnet_r101-d8_512x1024_40k_cityscapes
In Collection: GCNet
Metadata:
inference time (fps): 2.61
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.28
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436-5e62567f.pth
Config: configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py
- Name: gcnet_r50-d8_769x769_40k_cityscapes
In Collection: GCNet
Metadata:
inference time (fps): 1.67
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.12
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814-a26f4471.pth
Config: configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py
- Name: gcnet_r101-d8_769x769_40k_cityscapes
In Collection: GCNet
Metadata:
inference time (fps): 1.13
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.95
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550-ca4f0a84.pth
Config: configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py
- Name: gcnet_r50-d8_512x1024_80k_cityscapes
In Collection: GCNet
Metadata:
inference time (fps): 3.93
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.48
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450-ef8f069b.pth
Config: configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py
- Name: gcnet_r101-d8_512x1024_80k_cityscapes
In Collection: GCNet
Metadata:
inference time (fps): 2.61
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.03
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450-778ebf69.pth
Config: configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py
- Name: gcnet_r50-d8_769x769_80k_cityscapes
In Collection: GCNet
Metadata:
inference time (fps): 1.67
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.68
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516-4839565b.pth
Config: configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py
- Name: gcnet_r101-d8_769x769_80k_cityscapes
In Collection: GCNet
Metadata:
inference time (fps): 1.13
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.18
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628-8e043423.pth
Config: configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py
- Name: gcnet_r50-d8_512x512_80k_ade20k
In Collection: GCNet
Metadata:
inference time (fps): 23.38
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.47
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146-91a6da41.pth
Config: configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
- Name: gcnet_r101-d8_512x512_80k_ade20k
In Collection: GCNet
Metadata:
inference time (fps): 15.20
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.82
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811-c3fcb6dd.pth
Config: configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py
- Name: gcnet_r50-d8_512x512_160k_ade20k
In Collection: GCNet
Metadata:
inference time (fps): 23.38
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.37
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122-d95f3e1f.pth
Config: configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py
- Name: gcnet_r101-d8_512x512_160k_ade20k
In Collection: GCNet
Metadata:
inference time (fps): 15.20
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.69
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406-615528d7.pth
Config: configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py
- Name: gcnet_r50-d8_512x512_20k_voc12aug
In Collection: GCNet
Metadata:
inference time (fps): 23.35
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.42
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701-3cbfdab1.pth
Config: configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py
- Name: gcnet_r101-d8_512x512_20k_voc12aug
In Collection: GCNet
Metadata:
inference time (fps): 14.80
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.41
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713-6c720aa9.pth
Config: configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py
- Name: gcnet_r50-d8_512x512_40k_voc12aug
In Collection: GCNet
Metadata:
inference time (fps): 23.35
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.24
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105-9797336d.pth
Config: configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py
- Name: gcnet_r101-d8_512x512_40k_voc12aug
In Collection: GCNet
Metadata:
inference time (fps): 14.80
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.84
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806-1e38208d.pth
Config: configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py

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Models:
- Name: fcn_hr18s_512x1024_40k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 23.74
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.86
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth
Config: configs/fcn/fcn_hr18s_512x1024_40k_cityscapes.py
- Name: fcn_hr18_512x1024_40k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 12.97
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.19
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth
Config: configs/fcn/fcn_hr18_512x1024_40k_cityscapes.py
- Name: fcn_hr48_512x1024_40k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 6.42
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.48
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth
Config: configs/fcn/fcn_hr48_512x1024_40k_cityscapes.py
- Name: fcn_hr18s_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 23.74
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.31
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth
Config: configs/fcn/fcn_hr18s_512x1024_80k_cityscapes.py
- Name: fcn_hr18_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 12.97
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.65
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth
Config: configs/fcn/fcn_hr18_512x1024_80k_cityscapes.py
- Name: fcn_hr48_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 6.42
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.93
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth
Config: configs/fcn/fcn_hr48_512x1024_80k_cityscapes.py
- Name: fcn_hr18s_512x1024_160k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 23.74
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.31
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth
Config: configs/fcn/fcn_hr18s_512x1024_160k_cityscapes.py
- Name: fcn_hr18_512x1024_160k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 12.97
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.80
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth
Config: configs/fcn/fcn_hr18_512x1024_160k_cityscapes.py
- Name: fcn_hr48_512x1024_160k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 6.42
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.65
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth
Config: configs/fcn/fcn_hr48_512x1024_160k_cityscapes.py
- Name: fcn_hr18s_512x512_80k_ade20k
In Collection: FCN
Metadata:
inference time (fps): 38.66
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 31.38
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth
Config: configs/fcn/fcn_hr18s_512x512_80k_ade20k.py
- Name: fcn_hr18_512x512_80k_ade20k
In Collection: FCN
Metadata:
inference time (fps): 22.57
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 35.51
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20200614_185145-66f20cb7.pth
Config: configs/fcn/fcn_hr18_512x512_80k_ade20k.py
- Name: fcn_hr48_512x512_80k_ade20k
In Collection: FCN
Metadata:
inference time (fps): 21.23
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.90
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth
Config: configs/fcn/fcn_hr48_512x512_80k_ade20k.py
- Name: fcn_hr18s_512x512_160k_ade20k
In Collection: FCN
Metadata:
inference time (fps): 38.66
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 33.00
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20200614_214413-870f65ac.pth
Config: configs/fcn/fcn_hr18s_512x512_160k_ade20k.py
- Name: fcn_hr18_512x512_160k_ade20k
In Collection: FCN
Metadata:
inference time (fps): 22.57
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 36.79
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth
Config: configs/fcn/fcn_hr18_512x512_160k_ade20k.py
- Name: fcn_hr48_512x512_160k_ade20k
In Collection: FCN
Metadata:
inference time (fps): 21.23
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.02
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth
Config: configs/fcn/fcn_hr48_512x512_160k_ade20k.py
- Name: fcn_hr18s_512x512_20k_voc12aug
In Collection: FCN
Metadata:
inference time (fps): 43.36
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 65.20
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20200617_224503-56e36088.pth
Config: configs/fcn/fcn_hr18s_512x512_20k_voc12aug.py
- Name: fcn_hr18_512x512_20k_voc12aug
In Collection: FCN
Metadata:
inference time (fps): 23.48
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 72.30
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth
Config: configs/fcn/fcn_hr18_512x512_20k_voc12aug.py
- Name: fcn_hr48_512x512_20k_voc12aug
In Collection: FCN
Metadata:
inference time (fps): 22.05
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 75.87
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth
Config: configs/fcn/fcn_hr48_512x512_20k_voc12aug.py
- Name: fcn_hr18s_512x512_40k_voc12aug
In Collection: FCN
Metadata:
inference time (fps): 43.36
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 66.61
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth
Config: configs/fcn/fcn_hr18s_512x512_40k_voc12aug.py
- Name: fcn_hr18_512x512_40k_voc12aug
In Collection: FCN
Metadata:
inference time (fps): 23.48
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 72.90
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth
Config: configs/fcn/fcn_hr18_512x512_40k_voc12aug.py
- Name: fcn_hr48_512x512_40k_voc12aug
In Collection: FCN
Metadata:
inference time (fps): 22.05
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.24
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth
Config: configs/fcn/fcn_hr48_512x512_40k_voc12aug.py
- Name: fcn_hr48_480x480_40k_pascal_context
In Collection: FCN
Metadata:
inference time (fps): 8.86
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 45.14
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth
Config: configs/fcn/fcn_hr48_480x480_40k_pascal_context.py
- Name: fcn_hr48_480x480_80k_pascal_context
In Collection: FCN
Metadata:
inference time (fps): 8.86
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 45.84
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth
Config: configs/fcn/fcn_hr48_480x480_80k_pascal_context.py
- Name: fcn_hr48_480x480_40k_pascal_context_59
In Collection: FCN
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 50.33
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth
Config: configs/fcn/fcn_hr48_480x480_40k_pascal_context_59.py
- Name: fcn_hr48_480x480_80k_pascal_context
In Collection: FCN
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 51.12
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth
Config: configs/fcn/fcn_hr48_480x480_80k_pascal_context.py

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Models:
- Name: fcn_m-v2-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 14.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 61.54
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth
Config: configs/fcn/fcn_m-v2-d8_512x1024_80k_cityscapes.py
- Name: pspnet_m-v2-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 11.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 70.23
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth
Config: configs/pspnet/pspnet_m-v2-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3_m-v2-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 8.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.84
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth
Config: configs/deeplabv3/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 8.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.20
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth
Config: configs/deeplabv3+/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py
- Name: fcn_m-v2-d8_512x512_160k_ade20k
In Collection: FCN
Metadata:
inference time (fps): 64.4
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 19.71
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth
Config: configs/fcn/fcn_m-v2-d8_512x512_160k_ade20k.py
- Name: pspnet_m-v2-d8_512x512_160k_ade20k
In Collection: PSPNet
Metadata:
inference time (fps): 57.7
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 29.68
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth
Config: configs/pspnet/pspnet_m-v2-d8_512x512_160k_ade20k.py
- Name: deeplabv3_m-v2-d8_512x512_160k_ade20k
In Collection: DeepLabV3
Metadata:
inference time (fps): 39.9
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 34.08
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth
Config: configs/deeplabv3/deeplabv3_m-v2-d8_512x512_160k_ade20k.py
- Name: deeplabv3plus_m-v2-d8_512x512_160k_ade20k
In Collection: DeepLabV3+
Metadata:
inference time (fps): 43.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 34.02
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth
Config: configs/deeplabv3+/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py

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Collections:
- Name: LRASPP
Metadata:
Training Data:
- Cityscapes
Models:
- Name: lraspp_m-v3-d8_512x1024_320k_cityscapes
In Collection: LRASPP
Metadata:
inference time (fps): 15.22
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 69.54
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes_20201224_220337-cfe8fb07.pth
Config: configs/lraspp/lraspp_m-v3-d8_512x1024_320k_cityscapes.py
- Name: lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes
In Collection: LRASPP
Metadata:
inference time (fps): 14.77
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 67.87
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes_20201224_220337-9f29cd72.pth
Config: configs/lraspp/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py
- Name: lraspp_m-v3s-d8_512x1024_320k_cityscapes
In Collection: LRASPP
Metadata:
inference time (fps): 23.64
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 64.11
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes_20201224_223935-61565b34.pth
Config: configs/lraspp/lraspp_m-v3s-d8_512x1024_320k_cityscapes.py
- Name: lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes
In Collection: LRASPP
Metadata:
inference time (fps): 24.50
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 62.74
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes_20201224_223935-03daeabb.pth
Config: configs/lraspp/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py

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Collections:
- Name: NonLocal
Metadata:
Training Data:
- Cityscapes
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: nonlocal_r50-d8_512x1024_40k_cityscapes
In Collection: NonLocal
Metadata:
inference time (fps): 2.72
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.24
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748-c75e81e3.pth
Config: configs/nonlocal/nonlocal_r50-d8_512x1024_40k_cityscapes.py
- Name: nonlocal_r101-d8_512x1024_40k_cityscapes
In Collection: NonLocal
Metadata:
inference time (fps): 1.95
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.66
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748-d63729fa.pth
Config: configs/nonlocal/nonlocal_r101-d8_512x1024_40k_cityscapes.py
- Name: nonlocal_r50-d8_769x769_40k_cityscapes
In Collection: NonLocal
Metadata:
inference time (fps): 1.52
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.33
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243-82ef6749.pth
Config: configs/nonlocal/nonlocal_r50-d8_769x769_40k_cityscapes.py
- Name: nonlocal_r101-d8_769x769_40k_cityscapes
In Collection: NonLocal
Metadata:
inference time (fps): 1.05
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.57
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348-8fe9a9dc.pth
Config: configs/nonlocal/nonlocal_r101-d8_769x769_40k_cityscapes.py
- Name: nonlocal_r50-d8_512x1024_80k_cityscapes
In Collection: NonLocal
Metadata:
inference time (fps): 2.72
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.01
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518-d6839fae.pth
Config: configs/nonlocal/nonlocal_r50-d8_512x1024_80k_cityscapes.py
- Name: nonlocal_r101-d8_512x1024_80k_cityscapes
In Collection: NonLocal
Metadata:
inference time (fps): 1.95
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.93
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411-32700183.pth
Config: configs/nonlocal/nonlocal_r101-d8_512x1024_80k_cityscapes.py
- Name: nonlocal_r50-d8_769x769_80k_cityscapes
In Collection: NonLocal
Metadata:
inference time (fps): 1.52
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.05
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506-1f9792f6.pth
Config: configs/nonlocal/nonlocal_r50-d8_769x769_80k_cityscapes.py
- Name: nonlocal_r101-d8_769x769_80k_cityscapes
In Collection: NonLocal
Metadata:
inference time (fps): 1.05
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.40
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428-0e1fa4f9.pth
Config: configs/nonlocal/nonlocal_r101-d8_769x769_80k_cityscapes.py
- Name: nonlocal_r50-d8_512x512_80k_ade20k
In Collection: NonLocal
Metadata:
inference time (fps): 21.37
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 40.75
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801-5ae0aa33.pth
Config: configs/nonlocal/nonlocal_r50-d8_512x512_80k_ade20k.py
- Name: nonlocal_r101-d8_512x512_80k_ade20k
In Collection: NonLocal
Metadata:
inference time (fps): 13.97
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.90
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758-24105919.pth
Config: configs/nonlocal/nonlocal_r101-d8_512x512_80k_ade20k.py
- Name: nonlocal_r50-d8_512x512_160k_ade20k
In Collection: NonLocal
Metadata:
inference time (fps): 21.37
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.03
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410-baef45e3.pth
Config: configs/nonlocal/nonlocal_r50-d8_512x512_160k_ade20k.py
- Name: nonlocal_r101-d8_512x512_160k_ade20k
In Collection: NonLocal
Metadata:
inference time (fps): 13.97
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.36
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20200616_003422-affd0f8d.pth
Config: configs/nonlocal/nonlocal_r101-d8_512x512_160k_ade20k.py
- Name: nonlocal_r50-d8_512x512_20k_voc12aug
In Collection: NonLocal
Metadata:
inference time (fps): 21.21
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.20
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613-07f2a57c.pth
Config: configs/nonlocal/nonlocal_r50-d8_512x512_20k_voc12aug.py
- Name: nonlocal_r101-d8_512x512_20k_voc12aug
In Collection: NonLocal
Metadata:
inference time (fps): 14.01
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.15
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615-948c68ab.pth
Config: configs/nonlocal/nonlocal_r101-d8_512x512_20k_voc12aug.py
- Name: nonlocal_r50-d8_512x512_40k_voc12aug
In Collection: NonLocal
Metadata:
inference time (fps): 21.21
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.65
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028-0139d4a9.pth
Config: configs/nonlocal/nonlocal_r50-d8_512x512_40k_voc12aug.py
- Name: nonlocal_r101-d8_512x512_40k_voc12aug
In Collection: NonLocal
Metadata:
inference time (fps): 14.01
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.27
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028-7e5ff470.pth
Config: configs/nonlocal/nonlocal_r101-d8_512x512_40k_voc12aug.py

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Collections:
- Name: OCRNet
Metadata:
Training Data:
- Cityscapes
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: ocrnet_hr18s_512x1024_40k_cityscapes
In Collection: OCRNet
Metadata:
inference time (fps): 10.45
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.30
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304-fa2436c2.pth
Config: configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py
- Name: ocrnet_hr18_512x1024_40k_cityscapes
In Collection: OCRNet
Metadata:
inference time (fps): 7.50
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.72
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth
Config: configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py
- Name: ocrnet_hr48_512x1024_40k_cityscapes
In Collection: OCRNet
Metadata:
inference time (fps): 4.22
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.58
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth
Config: configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py
- Name: ocrnet_hr18s_512x1024_80k_cityscapes
In Collection: OCRNet
Metadata:
inference time (fps): 10.45
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.16
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth
Config: configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py
- Name: ocrnet_hr18_512x1024_80k_cityscapes
In Collection: OCRNet
Metadata:
inference time (fps): 7.50
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.57
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth
Config: configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py
- Name: ocrnet_hr48_512x1024_80k_cityscapes
In Collection: OCRNet
Metadata:
inference time (fps): 4.22
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.70
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth
Config: configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py
- Name: ocrnet_hr18s_512x1024_160k_cityscapes
In Collection: OCRNet
Metadata:
inference time (fps): 10.45
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.45
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth
Config: configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py
- Name: ocrnet_hr18_512x1024_160k_cityscapes
In Collection: OCRNet
Metadata:
inference time (fps): 7.50
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.47
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth
Config: configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py
- Name: ocrnet_hr48_512x1024_160k_cityscapes
In Collection: OCRNet
Metadata:
inference time (fps): 4.22
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 81.35
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth
Config: configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py
- Name: ocrnet_r101-d8_512x1024_40k_b8_cityscapes
In Collection: OCRNet
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU:
Weights: https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py
Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py
- Name: ocrnet_r101-d8_512x1024_40k_b16_cityscapes
In Collection: OCRNet
Metadata:
inference time (fps): 8.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 3.02
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes-02ac0f13.pth
Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py
- Name: ocrnet_r101-d8_512x1024_80k_b16_cityscapes
In Collection: OCRNet
Metadata:
inference time (fps): 8.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 3.02
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes-db500f80.pth
Config: configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py
- Name: ocrnet_hr18s_512x512_80k_ade20k
In Collection: OCRNet
Metadata:
inference time (fps): 28.98
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 35.06
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth
Config: configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py
- Name: ocrnet_hr18_512x512_80k_ade20k
In Collection: OCRNet
Metadata:
inference time (fps): 18.93
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 37.79
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth
Config: configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py
- Name: ocrnet_hr48_512x512_80k_ade20k
In Collection: OCRNet
Metadata:
inference time (fps): 16.99
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.00
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth
Config: configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py
- Name: ocrnet_hr18s_512x512_160k_ade20k
In Collection: OCRNet
Metadata:
inference time (fps): 28.98
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 37.19
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth
Config: configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py
- Name: ocrnet_hr18_512x512_160k_ade20k
In Collection: OCRNet
Metadata:
inference time (fps): 18.93
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 39.32
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth
Config: configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
- Name: ocrnet_hr48_512x512_160k_ade20k
In Collection: OCRNet
Metadata:
inference time (fps): 16.99
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.25
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth
Config: configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py
- Name: ocrnet_hr18s_512x512_20k_voc12aug
In Collection: OCRNet
Metadata:
inference time (fps): 31.55
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 71.70
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth
Config: configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py
- Name: ocrnet_hr18_512x512_20k_voc12aug
In Collection: OCRNet
Metadata:
inference time (fps): 19.91
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 74.75
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth
Config: configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py
- Name: ocrnet_hr48_512x512_20k_voc12aug
In Collection: OCRNet
Metadata:
inference time (fps): 17.83
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.72
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth
Config: configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py
- Name: ocrnet_hr18s_512x512_40k_voc12aug
In Collection: OCRNet
Metadata:
inference time (fps): 31.55
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 72.76
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth
Config: configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py
- Name: ocrnet_hr18_512x512_40k_voc12aug
In Collection: OCRNet
Metadata:
inference time (fps): 19.91
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 74.98
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth
Config: configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py
- Name: ocrnet_hr48_512x512_40k_voc12aug
In Collection: OCRNet
Metadata:
inference time (fps): 17.83
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.14
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth
Config: configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py

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Collections:
- Name: PointRend
Metadata:
Training Data:
- Cityscapes
- ADE20K
Models:
- Name: pointrend_r50_512x1024_80k_cityscapes
In Collection: PointRend
Metadata:
inference time (fps): 8.48
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.47
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth
Config: configs/pointrend/pointrend_r50_512x1024_80k_cityscapes.py
- Name: pointrend_r101_512x1024_80k_cityscapes
In Collection: PointRend
Metadata:
inference time (fps): 7.00
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.30
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth
Config: configs/pointrend/pointrend_r101_512x1024_80k_cityscapes.py
- Name: pointrend_r50_512x512_160k_ade20k
In Collection: PointRend
Metadata:
inference time (fps): 17.31
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 37.64
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth
Config: configs/pointrend/pointrend_r50_512x512_160k_ade20k.py
- Name: pointrend_r101_512x512_160k_ade20k
In Collection: PointRend
Metadata:
inference time (fps): 15.50
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 40.02
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth
Config: configs/pointrend/pointrend_r101_512x512_160k_ade20k.py

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Collections:
- Name: PSANet
Metadata:
Training Data:
- Cityscapes
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: psanet_r50-d8_512x1024_40k_cityscapes
In Collection: PSANet
Metadata:
inference time (fps): 3.17
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.63
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_r50-d8_512x1024_40k_cityscapes.py
- Name: psanet_r101-d8_512x1024_40k_cityscapes
In Collection: PSANet
Metadata:
inference time (fps): 2.20
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.14
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_r101-d8_512x1024_40k_cityscapes.py
- Name: psanet_r50-d8_769x769_40k_cityscapes
In Collection: PSANet
Metadata:
inference time (fps): 1.40
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.99
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_r50-d8_769x769_40k_cityscapes.py
- Name: psanet_r101-d8_769x769_40k_cityscapes
In Collection: PSANet
Metadata:
inference time (fps): 0.98
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.43
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_r101-d8_769x769_40k_cityscapes.py
- Name: psanet_r50-d8_512x1024_80k_cityscapes
In Collection: PSANet
Metadata:
inference time (fps): 3.17
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.24
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_r50-d8_512x1024_80k_cityscapes.py
- Name: psanet_r101-d8_512x1024_80k_cityscapes
In Collection: PSANet
Metadata:
inference time (fps): 2.20
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.31
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_r101-d8_512x1024_80k_cityscapes.py
- Name: psanet_r50-d8_769x769_80k_cityscapes
In Collection: PSANet
Metadata:
inference time (fps): 1.40
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.31
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_r50-d8_769x769_80k_cityscapes.py
- Name: psanet_r101-d8_769x769_80k_cityscapes
In Collection: PSANet
Metadata:
inference time (fps): 0.98
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.69
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_r101-d8_769x769_80k_cityscapes.py
- Name: psanet_r50-d8_512x512_80k_ade20k
In Collection: PSANet
Metadata:
inference time (fps): 18.91
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.14
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_r50-d8_512x512_80k_ade20k.py
- Name: psanet_r101-d8_512x512_80k_ade20k
In Collection: PSANet
Metadata:
inference time (fps): 13.13
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.80
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_r101-d8_512x512_80k_ade20k.py
- Name: psanet_r50-d8_512x512_160k_ade20k
In Collection: PSANet
Metadata:
inference time (fps): 18.91
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.67
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_r50-d8_512x512_160k_ade20k.py
- Name: psanet_r101-d8_512x512_160k_ade20k
In Collection: PSANet
Metadata:
inference time (fps): 13.13
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.74
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_r101-d8_512x512_160k_ade20k.py
- Name: psanet_r50-d8_512x512_20k_voc12aug
In Collection: PSANet
Metadata:
inference time (fps): 18.24
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.39
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_r50-d8_512x512_20k_voc12aug.py
- Name: psanet_r101-d8_512x512_20k_voc12aug
In Collection: PSANet
Metadata:
inference time (fps): 12.63
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.91
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_r101-d8_512x512_20k_voc12aug.py
- Name: psanet_r50-d8_512x512_40k_voc12aug
In Collection: PSANet
Metadata:
inference time (fps): 18.24
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.30
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_r50-d8_512x512_40k_voc12aug.py
- Name: psanet_r101-d8_512x512_40k_voc12aug
In Collection: PSANet
Metadata:
inference time (fps): 12.63
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.73
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
Config: configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py

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Collections:
- Name: PSPNet
Metadata:
Training Data:
- Cityscapes
- Pascal Context
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: pspnet_r50-d8_512x1024_40k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 4.07
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.85
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_r50-d8_512x1024_40k_cityscapes.py
- Name: pspnet_r101-d8_512x1024_40k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 2.68
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.34
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_r101-d8_512x1024_40k_cityscapes.py
- Name: pspnet_r50-d8_769x769_40k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 1.76
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.26
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_r50-d8_769x769_40k_cityscapes.py
- Name: pspnet_r101-d8_769x769_40k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 1.15
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.08
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_r101-d8_769x769_40k_cityscapes.py
- Name: pspnet_r18-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 15.71
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.87
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_r18-d8_512x1024_80k_cityscapes.py
- Name: pspnet_r50-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 4.07
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.55
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_r50-d8_512x1024_80k_cityscapes.py
- Name: pspnet_r101-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 2.68
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.76
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_r101-d8_512x1024_80k_cityscapes.py
- Name: pspnet_r18-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 6.20
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.90
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_r18-d8_769x769_80k_cityscapes.py
- Name: pspnet_r50-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 1.76
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.59
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_r50-d8_769x769_80k_cityscapes.py
- Name: pspnet_r101-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 1.15
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.77
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_r101-d8_769x769_80k_cityscapes.py
- Name: pspnet_r18b-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 16.28
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.23
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_r18b-d8_512x1024_80k_cityscapes.py
- Name: pspnet_r50b-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 4.30
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.22
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_r50b-d8_512x1024_80k_cityscapes.py
- Name: pspnet_r101b-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 2.76
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.69
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_r101b-d8_512x1024_80k_cityscapes.py
- Name: pspnet_r18b-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 6.41
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.92
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_r18b-d8_769x769_80k_cityscapes.py
- Name: pspnet_r50b-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 1.88
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.50
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_r50b-d8_769x769_80k_cityscapes.py
- Name: pspnet_r101b-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 1.17
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.87
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_r101b-d8_769x769_80k_cityscapes.py
- Name: pspnet_r50-d8_512x512_80k_ade20k
In Collection: PSPNet
Metadata:
inference time (fps): 23.53
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.13
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_r50-d8_512x512_80k_ade20k.py
- Name: pspnet_r101-d8_512x512_80k_ade20k
In Collection: PSPNet
Metadata:
inference time (fps): 15.30
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.57
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_r101-d8_512x512_80k_ade20k.py
- Name: pspnet_r50-d8_512x512_160k_ade20k
In Collection: PSPNet
Metadata:
inference time (fps): 23.53
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.48
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_r50-d8_512x512_160k_ade20k.py
- Name: pspnet_r101-d8_512x512_160k_ade20k
In Collection: PSPNet
Metadata:
inference time (fps): 15.30
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.39
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_r101-d8_512x512_160k_ade20k.py
- Name: pspnet_r50-d8_512x512_20k_voc12aug
In Collection: PSPNet
Metadata:
inference time (fps): 23.59
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.78
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_r50-d8_512x512_20k_voc12aug.py
- Name: pspnet_r101-d8_512x512_20k_voc12aug
In Collection: PSPNet
Metadata:
inference time (fps): 15.02
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.47
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_r101-d8_512x512_20k_voc12aug.py
- Name: pspnet_r50-d8_512x512_40k_voc12aug
In Collection: PSPNet
Metadata:
inference time (fps): 23.59
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.29
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_r50-d8_512x512_40k_voc12aug.py
- Name: pspnet_r101-d8_512x512_40k_voc12aug
In Collection: PSPNet
Metadata:
inference time (fps): 15.02
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.52
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_512x512_40k_voc12aug.py
- Name: pspnet_r101-d8_480x480_40k_pascal_context
In Collection: PSPNet
Metadata:
inference time (fps): 9.68
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 46.60
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_40k_pascal_context.py
- Name: pspnet_r101-d8_480x480_80k_pascal_context
In Collection: PSPNet
Metadata:
inference time (fps): 9.68
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 46.03
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_80k_pascal_context.py
- Name: pspnet_r101-d8_480x480_40k_pascal_context
In Collection: PSPNet
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 52.02
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_40k_pascal_context.py
- Name: pspnet_r101-d8_480x480_80k_pascal_context_59
In Collection: PSPNet
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 52.47
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
Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py

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Collections:
- Name: ResNeSt
Metadata:
Training Data:
- Cityscapes
- ADE20K
Models:
- Name: fcn_s101-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
inference time (fps): 2.39
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.56
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth
Config: configs/fcn/fcn_s101-d8_512x1024_80k_cityscapes.py
- Name: pspnet_s101-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
inference time (fps): 2.52
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.57
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth
Config: configs/pspnet/pspnet_s101-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3_s101-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
inference time (fps): 1.88
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.67
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth
Config: configs/deeplabv3/deeplabv3_s101-d8_512x1024_80k_cityscapes.py
- Name: deeplabv3plus_s101-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
inference time (fps): 2.36
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.62
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth
Config: configs/deeplabv3+/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py
- Name: fcn_s101-d8_512x512_160k_ade20k
In Collection: FCN
Metadata:
inference time (fps): 12.86
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.62
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth
Config: configs/fcn/fcn_s101-d8_512x512_160k_ade20k.py
- Name: pspnet_s101-d8_512x512_160k_ade20k
In Collection: PSPNet
Metadata:
inference time (fps): 13.02
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.44
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth
Config: configs/pspnet/pspnet_s101-d8_512x512_160k_ade20k.py
- Name: deeplabv3_s101-d8_512x512_160k_ade20k
In Collection: DeepLabV3
Metadata:
inference time (fps): 9.28
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.71
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth
Config: configs/deeplabv3/deeplabv3_s101-d8_512x512_160k_ade20k.py
- Name: deeplabv3plus_s101-d8_512x512_160k_ade20k
In Collection: DeepLabV3+
Metadata:
inference time (fps): 11.96
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 46.47
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth
Config: configs/deeplabv3+/deeplabv3plus_s101-d8_512x512_160k_ade20k.py

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Collections:
- Name: FPN
Metadata:
Training Data:
- Cityscapes
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: fpn_r50_512x1024_80k_cityscapes
In Collection: FPN
Metadata:
inference time (fps): 13.54
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.52
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth
Config: configs/fpn/fpn_r50_512x1024_80k_cityscapes.py
- Name: fpn_r101_512x1024_80k_cityscapes
In Collection: FPN
Metadata:
inference time (fps): 10.29
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.80
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth
Config: configs/fpn/fpn_r101_512x1024_80k_cityscapes.py
- Name: fpn_r50_512x512_160k_ade20k
In Collection: FPN
Metadata:
inference time (fps): 55.77
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 37.49
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth
Config: configs/fpn/fpn_r50_512x512_160k_ade20k.py
- Name: fpn_r101_512x512_160k_ade20k
In Collection: FPN
Metadata:
inference time (fps): 40.58
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 39.35
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth
Config: configs/fpn/fpn_r101_512x512_160k_ade20k.py

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Models:
- Name: fcn_unet_s5-d16_64x64_40k_drive
In Collection: FCN
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: DRIVE
Metrics:
mIoU: 0.680
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-26cee593.pth
Config: configs/unet-s5-d16/fcn_unet_s5-d16_64x64_40k_drive.py
- Name: pspnet_unet_s5-d16_64x64_40k_drive
In Collection: PSPNet
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: DRIVE
Metrics:
mIoU: 0.599
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth
Config: configs/unet-s5-d16/pspnet_unet_s5-d16_64x64_40k_drive.py
- Name: deeplabv3_unet_s5-d16_64x64_40k_drive
In Collection: DeepLabV3
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: DRIVE
Metrics:
mIoU: 0.596
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth
Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_64x64_40k_drive.py
- Name: fcn_unet_s5-d16_128x128_40k_stare
In Collection: FCN
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: STARE
Metrics:
mIoU: 0.968
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-6ea7cfda.pth
Config: configs/unet-s5-d16/fcn_unet_s5-d16_128x128_40k_stare.py
- Name: pspnet_unet_s5-d16_128x128_40k_stare
In Collection: PSPNet
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: STARE
Metrics:
mIoU: 0.982
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth
Config: configs/unet-s5-d16/pspnet_unet_s5-d16_128x128_40k_stare.py
- Name: deeplabv3_unet_s5-d16_128x128_40k_stare
In Collection: DeepLabV3
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: STARE
Metrics:
mIoU: 0.999
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth
Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_128x128_40k_stare.py
- Name: fcn_unet_s5-d16_128x128_40k_chase_db1
In Collection: FCN
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: CHASE_DB1
Metrics:
mIoU: 0.968
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-95852f45.pth
Config: configs/unet-s5-d16/fcn_unet_s5-d16_128x128_40k_chase_db1.py
- Name: pspnet_unet_s5-d16_128x128_40k_chase_db1
In Collection: PSPNet
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: CHASE_DB1
Metrics:
mIoU: 0.982
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth
Config: configs/unet-s5-d16/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
- Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1
In Collection: DeepLabV3
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: CHASE_DB1
Metrics:
mIoU: 0.999
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth
Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
- Name: fcn_unet_s5-d16_256x256_40k_hrf
In Collection: FCN
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: HRF
Metrics:
mIoU: 2.525
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-df3ec8c4.pth
Config: configs/unet-s5-d16/fcn_unet_s5-d16_256x256_40k_hrf.py
- Name: pspnet_unet_s5-d16_256x256_40k_hrf
In Collection: PSPNet
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: HRF
Metrics:
mIoU: 2.588
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth
Config: configs/unet-s5-d16/pspnet_unet_s5-d16_256x256_40k_hrf.py
- Name: deeplabv3_unet_s5-d16_256x256_40k_hrf
In Collection: DeepLabV3
Metadata:
inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: HRF
Metrics:
mIoU: 2.604
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth
Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_256x256_40k_hrf.py

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Collections:
- Name: UPerNet
Metadata:
Training Data:
- Cityscapes
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: upernet_r50_512x1024_40k_cityscapes
In Collection: UPerNet
Metadata:
inference time (fps): 4.25
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.10
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth
Config: configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
- Name: upernet_r101_512x1024_40k_cityscapes
In Collection: UPerNet
Metadata:
inference time (fps): 3.79
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.69
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth
Config: configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
- Name: upernet_r50_769x769_40k_cityscapes
In Collection: UPerNet
Metadata:
inference time (fps): 1.76
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.98
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth
Config: configs/upernet/upernet_r50_769x769_40k_cityscapes.py
- Name: upernet_r101_769x769_40k_cityscapes
In Collection: UPerNet
Metadata:
inference time (fps): 1.56
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.03
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth
Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py
- Name: upernet_r50_512x1024_80k_cityscapes
In Collection: UPerNet
Metadata:
inference time (fps): 4.25
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.19
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth
Config: configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
- Name: upernet_r101_512x1024_80k_cityscapes
In Collection: UPerNet
Metadata:
inference time (fps): 3.79
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.40
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth
Config: configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
- Name: upernet_r50_769x769_80k_cityscapes
In Collection: UPerNet
Metadata:
inference time (fps): 1.76
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.39
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth
Config: configs/upernet/upernet_r50_769x769_80k_cityscapes.py
- Name: upernet_r101_769x769_80k_cityscapes
In Collection: UPerNet
Metadata:
inference time (fps): 1.56
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.10
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth
Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py
- Name: upernet_r50_512x512_80k_ade20k
In Collection: UPerNet
Metadata:
inference time (fps): 23.40
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 40.70
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth
Config: configs/upernet/upernet_r50_512x512_80k_ade20k.py
- Name: upernet_r101_512x512_80k_ade20k
In Collection: UPerNet
Metadata:
inference time (fps): 20.34
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.91
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth
Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py
- Name: upernet_r50_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
inference time (fps): 23.40
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.05
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth
Config: configs/upernet/upernet_r50_512x512_160k_ade20k.py
- Name: upernet_r101_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
inference time (fps): 20.34
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.82
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth
Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py
- Name: upernet_r50_512x512_20k_voc12aug
In Collection: UPerNet
Metadata:
inference time (fps): 23.17
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 74.82
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth
Config: configs/upernet/upernet_r50_512x512_20k_voc12aug.py
- Name: upernet_r101_512x512_20k_voc12aug
In Collection: UPerNet
Metadata:
inference time (fps): 19.98
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.10
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth
Config: configs/upernet/upernet_r101_512x512_20k_voc12aug.py
- Name: upernet_r50_512x512_40k_voc12aug
In Collection: UPerNet
Metadata:
inference time (fps): 23.17
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 75.92
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth
Config: configs/upernet/upernet_r50_512x512_40k_voc12aug.py
- Name: upernet_r101_512x512_40k_voc12aug
In Collection: UPerNet
Metadata:
inference time (fps): 19.98
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.43
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth
Config: configs/upernet/upernet_r101_512x512_40k_voc12aug.py

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Import:
- configs/ann/metafile.yml
- configs/apcnet/metafile.yml
- configs/ccnet/metafile.yml
- configs/cgnet/metafile.yml
- configs/danet/metafile.yml
- configs/deeplabv3/metafile.yml
- configs/deeplabv3plus/metafile.yml
- configs/dnlnet/metafile.yml
- configs/emanet/metafile.yml
- configs/encnet/metafile.yml
- configs/fastscnn/metafile.yml
- configs/fcn/metafile.yml
- configs/fp16/metafile.yml
- configs/gcnet/metafile.yml
- configs/hrnet/metafile.yml
- configs/mobilenet_v2/metafile.yml
- configs/mobilenet_v3/metafile.yml
- configs/nonlocal_net/metafile.yml
- configs/ocrnet/metafile.yml
- configs/point_rend/metafile.yml
- configs/psanet/metafile.yml
- configs/pspnet/metafile.yml
- configs/resnest/metafile.yml
- configs/sem_fpn/metafile.yml
- configs/unet/metafile.yml
- configs/upernet/metafile.yml

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requirements/mminstall.txt Normal file
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@ -0,0 +1 @@
mmcv-full>=1.3.1,<=1.4.0

1
setup.py
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@ -104,6 +104,7 @@ if __name__ == '__main__':
keywords='computer vision, semantic segmentation', keywords='computer vision, semantic segmentation',
url='http://github.com/open-mmlab/mmsegmentation', url='http://github.com/open-mmlab/mmsegmentation',
packages=find_packages(exclude=('configs', 'tools', 'demo')), packages=find_packages(exclude=('configs', 'tools', 'demo')),
include_package_data=True,
classifiers=[ classifiers=[
'Development Status :: 4 - Beta', 'Development Status :: 4 - Beta',
'License :: OSI Approved :: Apache Software License', 'License :: OSI Approved :: Apache Software License',