mirror of
https://github.com/open-mmlab/mmsegmentation.git
synced 2025-06-02 23:55:30 +08:00
59fa6f648c
* change md2yml file * update metafile * update twins In Collection automatically * fix twins metafile * fix twins metafile * all metafile use value of Method * update collect name * update collect name * fix some typo * fix FCN D6 * change JPU to FastFCN * fix some typos in DNLNet, NonLocalNet, SETR, Segmenter, STDC, FastSCNN * fix typo in stdc * fix typo in DNLNet and UNet * fix NonLocalNet typo
104 lines
3.2 KiB
YAML
104 lines
3.2 KiB
YAML
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
|