mirrors
/
mmsegmentation
mirror of https://github.com/open-mmlab/mmsegmentation.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity
mmsegmentation/configs/fcn/fcn.yml

828 lines
26 KiB
YAML
Raw Blame History

Collections:
- Name: FCN
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
Paper:
URL: https://arxiv.org/abs/1411.4038
Title: Fully Convolutional Networks for Semantic Segmentation
README: configs/fcn/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
Version: v0.17.0
Converted From:
Code: https://github.com/BVLC/caffe/wiki/Model-Zoo#fcn
Models:
- Name: fcn_r50-d8_512x1024_40k_cityscapes
In Collection: FCN
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 239.81
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 5.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 72.25
mIoU(ms+flip): 73.36
Config: configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth
- Name: fcn_r101-d8_512x1024_40k_cityscapes
In Collection: FCN
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 375.94
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.45
mIoU(ms+flip): 76.58
Config: configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth
- Name: fcn_r50-d8_769x769_40k_cityscapes
In Collection: FCN
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 555.56
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 6.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 71.47
mIoU(ms+flip): 72.54
Config: configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth
- Name: fcn_r101-d8_769x769_40k_cityscapes
In Collection: FCN
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 840.34
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 10.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.93
mIoU(ms+flip): 75.14
Config: configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth
- Name: fcn_r18-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-18-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 68.26
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 1.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 71.11
mIoU(ms+flip): 72.91
Config: configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth
- Name: fcn_r50-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.61
mIoU(ms+flip): 74.24
Config: configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth
- Name: fcn_r101-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.13
mIoU(ms+flip): 75.94
Config: configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth
- Name: fcn_r101-d8_fp16_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 115.74
hardware: V100
backend: PyTorch
batch size: 1
mode: FP16
resolution: (512,1024)
Training Memory (GB): 5.37
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.8
Config: configs/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes/fcn_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230921-fb13e883.pth
- Name: fcn_r18-d8_769x769_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-18-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 156.25
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 1.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 70.8
mIoU(ms+flip): 73.16
Config: configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth
- Name: fcn_r50-d8_769x769_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 72.64
mIoU(ms+flip): 73.32
Config: configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth
- Name: fcn_r101-d8_769x769_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.52
mIoU(ms+flip): 76.61
Config: configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth
- Name: fcn_r18b-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-18b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 59.74
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 1.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 70.24
mIoU(ms+flip): 72.77
Config: configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth
- Name: fcn_r50b-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-50b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 238.1
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 5.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.65
mIoU(ms+flip): 77.59
Config: configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth
- Name: fcn_r101b-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-101b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 366.3
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 9.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.37
mIoU(ms+flip): 78.77
Config: configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth
- Name: fcn_r18b-d8_769x769_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-18b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 149.25
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 1.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 69.66
mIoU(ms+flip): 72.07
Config: configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth
- Name: fcn_r50b-d8_769x769_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-50b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 549.45
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 6.3
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.83
mIoU(ms+flip): 76.6
Config: configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth
- Name: fcn_r101b-d8_769x769_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-101b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 869.57
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 10.3
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.02
mIoU(ms+flip): 78.67
Config: configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth
- Name: fcn_d6_r50-d16_512x1024_40k_cityscapes
In Collection: FCN
Metadata:
backbone: R-50-D16
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 97.85
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 3.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.06
mIoU(ms+flip): 78.85
Config: configs/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes_20210305_130133-98d5d1bc.pth
- Name: fcn_d6_r50-d16_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-50-D16
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 96.62
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.27
mIoU(ms+flip): 78.88
Config: configs/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes_20210306_115604-133c292f.pth
- Name: fcn_d6_r50-d16_769x769_40k_cityscapes
In Collection: FCN
Metadata:
backbone: R-50-D16
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 239.81
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 3.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.82
mIoU(ms+flip): 78.22
Config: configs/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes_20210305_185744-1aab18ed.pth
- Name: fcn_d6_r50-d16_769x769_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-50-D16
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 240.96
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.04
mIoU(ms+flip): 78.4
Config: configs/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes_20210305_200413-109d88eb.pth
- Name: fcn_d6_r101-d16_512x1024_40k_cityscapes
In Collection: FCN
Metadata:
backbone: R-101-D16
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 124.38
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 4.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.36
mIoU(ms+flip): 79.18
Config: configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes_20210305_130337-9cf2b450.pth
- Name: fcn_d6_r101-d16_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-101-D16
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 121.07
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.46
mIoU(ms+flip): 80.42
Config: configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes_20210308_102747-cb336445.pth
- Name: fcn_d6_r101-d16_769x769_40k_cityscapes
In Collection: FCN
Metadata:
backbone: R-101-D16
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 320.51
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 5.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.28
mIoU(ms+flip): 78.95
Config: configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes_20210308_102453-60b114e9.pth
- Name: fcn_d6_r101-d16_769x769_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-101-D16
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 311.53
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.06
mIoU(ms+flip): 79.58
Config: configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes_20210306_120016-e33adc4f.pth
- Name: fcn_d6_r50b-d16_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-50b-D16
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 98.43
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 3.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.99
mIoU(ms+flip): 79.03
Config: configs/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes/fcn_d6_r50b-d16_512x1024_80k_cityscapes_20210311_125550-6a0b62e9.pth
- Name: fcn_d6_r50b-d16_769x769_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-50b-D16
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 239.81
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 3.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.86
mIoU(ms+flip): 78.52
Config: configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes/fcn_d6_r50b-d16_769x769_80k_cityscapes_20210311_131012-d665f231.pth
- Name: fcn_d6_r101b-d16_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-101b-D16
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 118.2
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 4.3
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.72
mIoU(ms+flip): 79.53
Config: configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes/fcn_d6_r101b-d16_512x1024_80k_cityscapes_20210311_144305-3f2eb5b4.pth
- Name: fcn_d6_r101b-d16_769x769_80k_cityscapes
In Collection: FCN
Metadata:
backbone: R-101b-D16
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 301.2
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 4.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.34
mIoU(ms+flip): 78.91
Config: configs/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes/fcn_d6_r101b-d16_769x769_80k_cityscapes_20210311_154527-c4d8bfbc.pth
- Name: fcn_r50-d8_512x512_80k_ade20k
In Collection: FCN
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 42.57
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 8.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 35.94
mIoU(ms+flip): 37.94
Config: configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth
- Name: fcn_r101-d8_512x512_80k_ade20k
In Collection: FCN
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 67.66
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 12.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 39.61
mIoU(ms+flip): 40.83
Config: configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth
- Name: fcn_r50-d8_512x512_160k_ade20k
In Collection: FCN
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 36.1
mIoU(ms+flip): 38.08
Config: configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth
- Name: fcn_r101-d8_512x512_160k_ade20k
In Collection: FCN
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 39.91
mIoU(ms+flip): 41.4
Config: configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth
- Name: fcn_r50-d8_512x512_20k_voc12aug
In Collection: FCN
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 42.96
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 5.7
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 67.08
mIoU(ms+flip): 69.94
Config: configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth
- Name: fcn_r101-d8_512x512_20k_voc12aug
In Collection: FCN
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 67.52
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 71.16
mIoU(ms+flip): 73.57
Config: configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth
- Name: fcn_r50-d8_512x512_40k_voc12aug
In Collection: FCN
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 66.97
mIoU(ms+flip): 69.04
Config: configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth
- Name: fcn_r101-d8_512x512_40k_voc12aug
In Collection: FCN
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 69.91
mIoU(ms+flip): 72.38
Config: configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth
- Name: fcn_r101-d8_480x480_40k_pascal_context
In Collection: FCN
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
inference time (ms/im):
- value: 100.7
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (480,480)
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 44.43
mIoU(ms+flip): 45.63
Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context_20210421_154757-b5e97937.pth
- Name: fcn_r101-d8_480x480_80k_pascal_context
In Collection: FCN
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 44.13
mIoU(ms+flip): 45.26
Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context_20210421_163310-4711813f.pth
- Name: fcn_r101-d8_480x480_40k_pascal_context_59
In Collection: FCN
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context 59
Metrics:
mIoU: 48.42
mIoU(ms+flip): 50.4
Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth
- Name: fcn_r101-d8_480x480_80k_pascal_context_59
In Collection: FCN
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context 59
Metrics:
mIoU: 49.35
mIoU(ms+flip): 51.38
Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth
Reference in New Issue View Git Blame Copy Permalink