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metafile.yaml
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Collections:
- Name: ERFNet
License: Apache License 2.0
Metadata:
Training Data:
- Cityscapes
Paper:
Title: 'ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation'
URL: http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf
README: configs/erfnet/README.md
Frameworks:
- PyTorch
Models:
- Name: erfnet_fcn_4xb4-160k_cityscapes-512x1024
In Collection: ERFNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 72.5
mIoU(ms+flip): 74.75
Config: configs/erfnet/erfnet_fcn_4xb4-160k_cityscapes-512x1024.py
Metadata:
Training Data: Cityscapes
Batch Size: 16
Architecture:
- ERFNet
- ERFNet
Training Resources: 4x V100 GPUS
Memory (GB): 6.04
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20220704_162145-dc90157a.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20220704_162145.log.json
Paper:
Title: 'ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation'
URL: http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/erfnet.py#L321
Framework: PyTorch