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mmsegmentation/configs/upernet/metafile.yaml

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Collections:
- Name: UPerNet
License: Apache License 2.0
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
README: configs/upernet/README.md
Frameworks:
- PyTorch
Models:
- Name: upernet_r50_4xb2-40k_cityscapes-512x1024
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.1
mIoU(ms+flip): 78.37
Config: configs/upernet/upernet_r50_4xb2-40k_cityscapes-512x1024.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-50
- UPerNet
Training Resources: 4x V100 GPUS
Memory (GB): 6.4
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
- Name: upernet_r101_4xb2-40k_cityscapes-512x1024
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.69
mIoU(ms+flip): 80.11
Config: configs/upernet/upernet_r101_4xb2-40k_cityscapes-512x1024.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-101
- UPerNet
Training Resources: 4x V100 GPUS
Memory (GB): 7.4
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
- Name: upernet_r50_4xb2-40k_cityscapes-769x769
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.98
mIoU(ms+flip): 79.7
Config: configs/upernet/upernet_r50_4xb2-40k_cityscapes-769x769.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-50
- UPerNet
Training Resources: 4x V100 GPUS
Memory (GB): 7.2
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
- Name: upernet_r101_4xb2-40k_cityscapes-769x769
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.03
mIoU(ms+flip): 80.77
Config: configs/upernet/upernet_r101_4xb2-40k_cityscapes-769x769.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-101
- UPerNet
Training Resources: 4x V100 GPUS
Memory (GB): 8.4
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
- Name: upernet_r50_4xb2-80k_cityscapes-512x1024
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.19
mIoU(ms+flip): 79.19
Config: configs/upernet/upernet_r50_4xb2-80k_cityscapes-512x1024.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-50
- UPerNet
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
- Name: upernet_r101_4xb2-80k_cityscapes-512x1024
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.4
mIoU(ms+flip): 80.46
Config: configs/upernet/upernet_r101_4xb2-80k_cityscapes-512x1024.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-101
- UPerNet
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
- Name: upernet_r50_4xb2-80k_cityscapes-769x769
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.39
mIoU(ms+flip): 80.92
Config: configs/upernet/upernet_r50_4xb2-80k_cityscapes-769x769.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-50
- UPerNet
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
- Name: upernet_r101_4xb2-80k_cityscapes-769x769
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.1
mIoU(ms+flip): 81.49
Config: configs/upernet/upernet_r101_4xb2-80k_cityscapes-769x769.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-101
- UPerNet
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
- Name: upernet_r50_4xb4-80k_ade20k-512x512
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 40.7
mIoU(ms+flip): 41.81
Config: configs/upernet/upernet_r50_4xb4-80k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- R-50
- UPerNet
Training Resources: 4x V100 GPUS
Memory (GB): 8.1
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
- Name: upernet_r101_4xb4-80k_ade20k-512x512
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.91
mIoU(ms+flip): 43.96
Config: configs/upernet/upernet_r101_4xb4-80k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- R-101
- UPerNet
Training Resources: 4x V100 GPUS
Memory (GB): 9.1
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
- Name: upernet_r50_4xb4-160k_ade20k-512x512
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.05
mIoU(ms+flip): 42.78
Config: configs/upernet/upernet_r50_4xb4-160k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- R-50
- UPerNet
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
- Name: upernet_r101_4xb4-160k_ade20k-512x512
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.82
mIoU(ms+flip): 44.85
Config: configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- R-101
- UPerNet
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
- Name: upernet_r50_4xb4-20k_voc12aug-512x512
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 74.82
mIoU(ms+flip): 76.35
Config: configs/upernet/upernet_r50_4xb4-20k_voc12aug-512x512.py
Metadata:
Training Data: Pascal VOC 2012 + Aug
Batch Size: 16
Architecture:
- R-50
- UPerNet
Training Resources: 4x V100 GPUS
Memory (GB): 6.4
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
- Name: upernet_r101_4xb4-20k_voc12aug-512x512
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.1
mIoU(ms+flip): 78.29
Config: configs/upernet/upernet_r101_4xb4-20k_voc12aug-512x512.py
Metadata:
Training Data: Pascal VOC 2012 + Aug
Batch Size: 16
Architecture:
- R-101
- UPerNet
Training Resources: 4x V100 GPUS
Memory (GB): 7.5
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
- Name: upernet_r50_4xb4-40k_voc12aug-512x512
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 75.92
mIoU(ms+flip): 77.44
Config: configs/upernet/upernet_r50_4xb4-40k_voc12aug-512x512.py
Metadata:
Training Data: Pascal VOC 2012 + Aug
Batch Size: 16
Architecture:
- R-50
- UPerNet
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
- Name: upernet_r101_4xb4-40k_voc12aug-512x512
In Collection: UPerNet
Results:
Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.43
mIoU(ms+flip): 78.56
Config: configs/upernet/upernet_r101_4xb4-40k_voc12aug-512x512.py
Metadata:
Training Data: Pascal VOC 2012 + Aug
Batch Size: 16
Architecture:
- R-101
- UPerNet
Training Resources: 4x V100 GPUS
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549.log.json
Paper:
Title: Unified Perceptual Parsing for Scene Understanding
URL: https://arxiv.org/pdf/1807.10221.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
Framework: PyTorch
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