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mmsegmentation/configs/gcnet/gcnet.yml

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Collections:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Name: gcnet
Models:
- Config: configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 254.45
lr schd: 40000
memory (GB): 5.8
Name: gcnet_r50-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 77.69
mIoU(ms+flip): 78.56
Task: Semantic Segmentation
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_r101-d8_512x1024_40k_cityscapes.py
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 383.14
lr schd: 40000
memory (GB): 9.2
Name: gcnet_r101-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.28
mIoU(ms+flip): 79.34
Task: Semantic Segmentation
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_r50-d8_769x769_40k_cityscapes.py
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 598.8
lr schd: 40000
memory (GB): 6.5
Name: gcnet_r50-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.12
mIoU(ms+flip): 80.09
Task: Semantic Segmentation
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_r101-d8_769x769_40k_cityscapes.py
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 884.96
lr schd: 40000
memory (GB): 10.5
Name: gcnet_r101-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.95
mIoU(ms+flip): 80.71
Task: Semantic Segmentation
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_r50-d8_512x1024_80k_cityscapes.py
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: gcnet_r50-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.48
mIoU(ms+flip): 80.01
Task: Semantic Segmentation
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_r101-d8_512x1024_80k_cityscapes.py
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: gcnet_r101-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.03
mIoU(ms+flip): 79.84
Task: Semantic Segmentation
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_r50-d8_769x769_80k_cityscapes.py
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: gcnet_r50-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.68
mIoU(ms+flip): 80.66
Task: Semantic Segmentation
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_r101-d8_769x769_80k_cityscapes.py
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: gcnet_r101-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.18
mIoU(ms+flip): 80.71
Task: Semantic Segmentation
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_r50-d8_512x512_80k_ade20k.py
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 42.77
lr schd: 80000
memory (GB): 8.5
Name: gcnet_r50-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 41.47
mIoU(ms+flip): 42.85
Task: Semantic Segmentation
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_r101-d8_512x512_80k_ade20k.py
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 65.79
lr schd: 80000
memory (GB): 12.0
Name: gcnet_r101-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 42.82
mIoU(ms+flip): 44.54
Task: Semantic Segmentation
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_r50-d8_512x512_160k_ade20k.py
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: gcnet_r50-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 42.37
mIoU(ms+flip): 43.52
Task: Semantic Segmentation
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_r101-d8_512x512_160k_ade20k.py
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: gcnet_r101-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 43.69
mIoU(ms+flip): 45.21
Task: Semantic Segmentation
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_r50-d8_512x512_20k_voc12aug.py
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 42.83
lr schd: 20000
memory (GB): 5.8
Name: gcnet_r50-d8_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.42
mIoU(ms+flip): 77.51
Task: Semantic Segmentation
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_r101-d8_512x512_20k_voc12aug.py
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 67.57
lr schd: 20000
memory (GB): 9.2
Name: gcnet_r101-d8_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.41
mIoU(ms+flip): 78.56
Task: Semantic Segmentation
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_r50-d8_512x512_40k_voc12aug.py
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Name: gcnet_r50-d8_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.24
mIoU(ms+flip): 77.63
Task: Semantic Segmentation
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_r101-d8_512x512_40k_voc12aug.py
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Name: gcnet_r101-d8_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.84
mIoU(ms+flip): 78.59
Task: Semantic Segmentation
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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