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mmsegmentation/configs/gcnet/metafile.yml

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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 (ms/im):
- value: 254.45
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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 (ms/im):
- value: 383.14
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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 (ms/im):
- value: 598.8
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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 (ms/im):
- value: 884.96
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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 (ms/im):
- value: 254.45
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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 (ms/im):
- value: 383.14
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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 (ms/im):
- value: 598.8
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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 (ms/im):
- value: 884.96
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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 (ms/im):
- value: 42.77
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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 (ms/im):
- value: 65.79
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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 (ms/im):
- value: 42.77
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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 (ms/im):
- value: 65.79
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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 (ms/im):
- value: 42.83
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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 (ms/im):
- value: 67.57
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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 (ms/im):
- value: 42.83
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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 (ms/im):
- value: 67.57
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
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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