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metafile.yaml
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Collections:
- Name: ICNet
License: Apache License 2.0
Metadata:
Training Data:
- Cityscapes
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
README: configs/icnet/README.md
Frameworks:
- PyTorch
Models:
- Name: icnet_r18-d8_4xb2-80k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 68.14
mIoU(ms+flip): 70.16
Config: configs/icnet/icnet_r18-d8_4xb2-80k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-18-D8
- ICNet
Training Resources: 4x V100 GPUS
Memory (GB): 1.7
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r18-d8_4xb2-160k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 71.64
mIoU(ms+flip): 74.18
Config: configs/icnet/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-18-D8
- ICNet
Training Resources: 4x V100 GPUS
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 72.51
mIoU(ms+flip): 74.78
Config: configs/icnet/icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-18-D8
- ICNet
- (in1k-pre)
Training Resources: 4x V100 GPUS
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
Training log: 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.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.43
mIoU(ms+flip): 76.72
Config: configs/icnet/icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-18-D8
- ICNet
- (in1k-pre)
Training Resources: 4x V100 GPUS
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
Training log: 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.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r50-d8_4xb2-80k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 68.91
mIoU(ms+flip): 69.72
Config: configs/icnet/icnet_r50-d8_4xb2-80k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-50-D8
- ICNet
Training Resources: 4x V100 GPUS
Memory (GB): 2.53
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r50-d8_4xb2-160k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.82
mIoU(ms+flip): 75.67
Config: configs/icnet/icnet_r50-d8_4xb2-160k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-50-D8
- ICNet
Training Resources: 4x V100 GPUS
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.58
mIoU(ms+flip): 76.41
Config: configs/icnet/icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-50-D8
- ICNet
- (in1k-pre)
Training Resources: 4x V100 GPUS
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
Training log: 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.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.29
mIoU(ms+flip): 78.09
Config: configs/icnet/icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-50-D8
- ICNet
- (in1k-pre)
Training Resources: 4x V100 GPUS
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
Training log: 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.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r101-d8_4xb2-80k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 70.28
mIoU(ms+flip): 71.95
Config: configs/icnet/icnet_r101-d8_4xb2-80k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-101-D8
- ICNet
Training Resources: 4x V100 GPUS
Memory (GB): 3.08
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r101-d8_4xb2-160k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.8
mIoU(ms+flip): 76.1
Config: configs/icnet/icnet_r101-d8_4xb2-160k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-101-D8
- ICNet
Training Resources: 4x V100 GPUS
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.57
mIoU(ms+flip): 77.86
Config: configs/icnet/icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-101-D8
- ICNet
- (in1k-pre)
Training Resources: 4x V100 GPUS
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
Training log: 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.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch
- Name: icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832
In Collection: ICNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.15
mIoU(ms+flip): 77.98
Config: configs/icnet/icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-101-D8
- ICNet
- (in1k-pre)
Training Resources: 4x V100 GPUS
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
Training log: 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.log.json
Paper:
Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
URL: https://arxiv.org/abs/1704.08545
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
Framework: PyTorch