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metafile.yaml
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Collections:
- Name: FPN
License: Apache License 2.0
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
Title: Panoptic Feature Pyramid Networks
URL: https://arxiv.org/abs/1901.02446
README: configs/sem_fpn/README.md
Frameworks:
- PyTorch
Models:
- Name: fpn_r50_4xb2-80k_cityscapes-512x1024
In Collection: FPN
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.52
mIoU(ms+flip): 76.08
Config: configs/sem_fpn/fpn_r50_4xb2-80k_cityscapes-512x1024.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-50
- FPN
Training Resources: 4x V100 GPUS
Memory (GB): 2.8
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes-20200717_021437.log.json
Paper:
Title: Panoptic Feature Pyramid Networks
URL: https://arxiv.org/abs/1901.02446
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fpn_head.py#L12
Framework: PyTorch
- Name: fpn_r101_4xb2-80k_cityscapes-512x1024
In Collection: FPN
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.8
mIoU(ms+flip): 77.4
Config: configs/sem_fpn/fpn_r101_4xb2-80k_cityscapes-512x1024.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- R-101
- FPN
Training Resources: 4x V100 GPUS
Memory (GB): 3.9
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes-20200717_012416.log.json
Paper:
Title: Panoptic Feature Pyramid Networks
URL: https://arxiv.org/abs/1901.02446
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fpn_head.py#L12
Framework: PyTorch
- Name: fpn_r50_4xb4-160k_ade20k-512x512
In Collection: FPN
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 37.49
mIoU(ms+flip): 39.09
Config: configs/sem_fpn/fpn_r50_4xb4-160k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- R-50
- FPN
Training Resources: 4x V100 GPUS
Memory (GB): 4.9
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k-20200718_131734.log.json
Paper:
Title: Panoptic Feature Pyramid Networks
URL: https://arxiv.org/abs/1901.02446
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fpn_head.py#L12
Framework: PyTorch
- Name: fpn_r101_4xb4-160k_ade20k-512x512
In Collection: FPN
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 39.35
mIoU(ms+flip): 40.72
Config: configs/sem_fpn/fpn_r101_4xb4-160k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- R-101
- FPN
Training Resources: 4x V100 GPUS
Memory (GB): 5.9
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k-20200718_131734.log.json
Paper:
Title: Panoptic Feature Pyramid Networks
URL: https://arxiv.org/abs/1901.02446
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fpn_head.py#L12
Framework: PyTorch