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icnet.yml
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icnet.yml
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Collections:
- Name: ICNet
Metadata:
Training Data:
- Cityscapes
Paper:
URL: https://arxiv.org/abs/1704.08545
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
README: configs/icnet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Version: v0.18.0
Converted From:
Code: https://github.com/hszhao/ICNet
Models:
- Name: icnet_r18-d8_832x832_80k_cityscapes
In Collection: ICNet
Metadata:
backbone: R-18-D8
crop size: (832,832)
lr schd: 80000
inference time (ms/im):
- value: 36.87
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (832,832)
Training Memory (GB): 1.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 68.14
mIoU(ms+flip): 70.16
Config: configs/icnet/icnet_r18-d8_832x832_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521-2e36638d.pth
- Name: icnet_r18-d8_832x832_160k_cityscapes
In Collection: ICNet
Metadata:
backbone: R-18-D8
crop size: (832,832)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 71.64
mIoU(ms+flip): 74.18
Config: configs/icnet/icnet_r18-d8_832x832_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153-2c6eb6e0.pth
- Name: icnet_r18-d8_in1k-pre_832x832_80k_cityscapes
In Collection: ICNet
Metadata:
backbone: R-18-D8
crop size: (832,832)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 72.51
mIoU(ms+flip): 74.78
Config: configs/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354-1cbe3022.pth
- Name: icnet_r18-d8_in1k-pre_832x832_160k_cityscapes
In Collection: ICNet
Metadata:
backbone: R-18-D8
crop size: (832,832)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.43
mIoU(ms+flip): 76.72
Config: configs/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702-619c8ae1.pth
- Name: icnet_r50-d8_832x832_80k_cityscapes
In Collection: ICNet
Metadata:
backbone: R-50-D8
crop size: (832,832)
lr schd: 80000
inference time (ms/im):
- value: 49.8
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (832,832)
Training Memory (GB): 2.53
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 68.91
mIoU(ms+flip): 69.72
Config: configs/icnet/icnet_r50-d8_832x832_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625-c6407341.pth
- Name: icnet_r50-d8_832x832_160k_cityscapes
In Collection: ICNet
Metadata:
backbone: R-50-D8
crop size: (832,832)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.82
mIoU(ms+flip): 75.67
Config: configs/icnet/icnet_r50-d8_832x832_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612-a95f0d4e.pth
- Name: icnet_r50-d8_in1k-pre_832x832_80k_cityscapes
In Collection: ICNet
Metadata:
backbone: R-50-D8
crop size: (832,832)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.58
mIoU(ms+flip): 76.41
Config: configs/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943-1743dc7b.pth
- Name: icnet_r50-d8_in1k-pre_832x832_160k_cityscapes
In Collection: ICNet
Metadata:
backbone: R-50-D8
crop size: (832,832)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.29
mIoU(ms+flip): 78.09
Config: configs/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715-ce310aea.pth
- Name: icnet_r101-d8_832x832_80k_cityscapes
In Collection: ICNet
Metadata:
backbone: R-101-D8
crop size: (832,832)
lr schd: 80000
inference time (ms/im):
- value: 59.0
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (832,832)
Training Memory (GB): 3.08
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 70.28
mIoU(ms+flip): 71.95
Config: configs/icnet/icnet_r101-d8_832x832_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447-b52f936e.pth
- Name: icnet_r101-d8_832x832_160k_cityscapes
In Collection: ICNet
Metadata:
backbone: R-101-D8
crop size: (832,832)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.8
mIoU(ms+flip): 76.1
Config: configs/icnet/icnet_r101-d8_832x832_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350-3a1ebf1a.pth
- Name: icnet_r101-d8_in1k-pre_832x832_80k_cityscapes
In Collection: ICNet
Metadata:
backbone: R-101-D8
crop size: (832,832)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.57
mIoU(ms+flip): 77.86
Config: configs/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414-7ceb12c5.pth
- Name: icnet_r101-d8_in1k-pre_832x832_160k_cityscapes
In Collection: ICNet
Metadata:
backbone: R-101-D8
crop size: (832,832)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.15
mIoU(ms+flip): 77.98
Config: configs/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612-9484ae8a.pth