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sem_fpn.yml
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sem_fpn.yml
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Collections:
- Name: FPN
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
URL: https://arxiv.org/abs/1901.02446
Title: Panoptic Feature Pyramid Networks
README: configs/sem_fpn/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fpn_head.py#L12
Version: v0.17.0
Converted From:
Code: https://github.com/facebookresearch/detectron2
Models:
- Name: fpn_r50_512x1024_80k_cityscapes
In Collection: FPN
Metadata:
backbone: R-50
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 73.86
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 2.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.52
mIoU(ms+flip): 76.08
Config: configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth
- Name: fpn_r101_512x1024_80k_cityscapes
In Collection: FPN
Metadata:
backbone: R-101
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 97.18
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 3.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.8
mIoU(ms+flip): 77.4
Config: configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth
- Name: fpn_r50_512x512_160k_ade20k
In Collection: FPN
Metadata:
backbone: R-50
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 17.93
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 4.9
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 37.49
mIoU(ms+flip): 39.09
Config: configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth
- Name: fpn_r101_512x512_160k_ade20k
In Collection: FPN
Metadata:
backbone: R-101
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 24.64
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 5.9
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 39.35
mIoU(ms+flip): 40.72
Config: configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth