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emanet.yml
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emanet.yml
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Collections:
- Name: EMANet
Metadata:
Training Data:
- Cityscapes
Paper:
URL: https://arxiv.org/abs/1907.13426
Title: Expectation-Maximization Attention Networks for Semantic Segmentation
README: configs/emanet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80
Version: v0.17.0
Converted From:
Code: https://xialipku.github.io/EMANet
Models:
- Name: emanet_r50-d8_512x1024_80k_cityscapes
In Collection: EMANet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 218.34
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 5.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.59
mIoU(ms+flip): 79.44
Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py
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
- Name: emanet_r101-d8_512x1024_80k_cityscapes
In Collection: EMANet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 348.43
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 6.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.1
mIoU(ms+flip): 81.21
Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py
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
- Name: emanet_r50-d8_769x769_80k_cityscapes
In Collection: EMANet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 507.61
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 8.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.33
mIoU(ms+flip): 80.49
Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py
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
- Name: emanet_r101-d8_769x769_80k_cityscapes
In Collection: EMANet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 819.67
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 10.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.62
mIoU(ms+flip): 81.0
Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py
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