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mmsegmentation/configs/fastfcn/fastfcn.yml

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Collections:
- Name: FastFCN
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
URL: https://arxiv.org/abs/1903.11816
Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
README: configs/fastfcn/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
Version: v0.18.0
Converted From:
Code: https://github.com/wuhuikai/FastFCN
Models:
- Name: fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 378.79
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 5.67
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.12
mIoU(ms+flip): 80.58
Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth
- Name: fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
Training Memory (GB): 9.79
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.52
mIoU(ms+flip): 80.91
Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth
- Name: fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 227.27
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 5.67
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.26
mIoU(ms+flip): 80.86
Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth
- Name: fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
Training Memory (GB): 9.94
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.76
mIoU(ms+flip): 80.03
Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth
- Name: fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 209.64
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 8.15
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.97
mIoU(ms+flip): 79.92
Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth
- Name: fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
Training Memory (GB): 15.45
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.6
mIoU(ms+flip): 80.25
Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth
- Name: fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 82.92
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 8.46
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.88
mIoU(ms+flip): 42.91
Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619-3aa40f2d.pth
- Name: fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.58
mIoU(ms+flip): 44.92
Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246-27036aee.pth
- Name: fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 52.06
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 8.02
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.4
mIoU(ms+flip): 42.12
Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137-993d07c8.pth
- Name: fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.63
mIoU(ms+flip): 43.71
Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455-e8f5a2fd.pth
- Name: fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 58.04
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 9.67
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 40.88
mIoU(ms+flip): 42.36
Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214-65aef6dd.pth
- Name: fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.5
mIoU(ms+flip): 44.21
Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456-d875ce3c.pth
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