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mmsegmentation/configs/gcnet/gcnet.yml

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Collections:
- Name: gcnet
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Paper:
URL: https://arxiv.org/abs/1904.11492
Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
README: configs/gcnet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
Version: v0.17.0
Converted From:
Code: https://github.com/xvjiarui/GCNet
Models:
- Name: gcnet_r50-d8_512x1024_40k_cityscapes
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 254.45
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 5.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.69
mIoU(ms+flip): 78.56
Config: configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py
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
- Name: gcnet_r101-d8_512x1024_40k_cityscapes
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 383.14
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.28
mIoU(ms+flip): 79.34
Config: configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py
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
- Name: gcnet_r50-d8_769x769_40k_cityscapes
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 598.8
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 6.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.12
mIoU(ms+flip): 80.09
Config: configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py
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
- Name: gcnet_r101-d8_769x769_40k_cityscapes
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 884.96
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 10.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.95
mIoU(ms+flip): 80.71
Config: configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py
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
- Name: gcnet_r50-d8_512x1024_80k_cityscapes
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.48
mIoU(ms+flip): 80.01
Config: configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py
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
- Name: gcnet_r101-d8_512x1024_80k_cityscapes
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.03
mIoU(ms+flip): 79.84
Config: configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py
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
- Name: gcnet_r50-d8_769x769_80k_cityscapes
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.68
mIoU(ms+flip): 80.66
Config: configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py
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
- Name: gcnet_r101-d8_769x769_80k_cityscapes
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.18
mIoU(ms+flip): 80.71
Config: configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py
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
- Name: gcnet_r50-d8_512x512_80k_ade20k
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 42.77
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 8.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.47
mIoU(ms+flip): 42.85
Config: configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
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
- Name: gcnet_r101-d8_512x512_80k_ade20k
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 65.79
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 12.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.82
mIoU(ms+flip): 44.54
Config: configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py
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
- Name: gcnet_r50-d8_512x512_160k_ade20k
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.37
mIoU(ms+flip): 43.52
Config: configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py
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
- Name: gcnet_r101-d8_512x512_160k_ade20k
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.69
mIoU(ms+flip): 45.21
Config: configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py
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
- Name: gcnet_r50-d8_512x512_20k_voc12aug
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 42.83
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 5.8
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.42
mIoU(ms+flip): 77.51
Config: configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py
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
- Name: gcnet_r101-d8_512x512_20k_voc12aug
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 67.57
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.41
mIoU(ms+flip): 78.56
Config: configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py
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
- Name: gcnet_r50-d8_512x512_40k_voc12aug
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.24
mIoU(ms+flip): 77.63
Config: configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py
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
- Name: gcnet_r101-d8_512x512_40k_voc12aug
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.84
mIoU(ms+flip): 78.59
Config: configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py
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
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