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mmsegmentation/configs/deeplabv3/deeplabv3.yml

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Collections:
- Name: DeepLabV3
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
- COCO-Stuff 10k
- COCO-Stuff 164k
Paper:
URL: https://arxiv.org/abs/1706.05587
Title: Rethinking atrous convolution for semantic image segmentation
README: configs/deeplabv3/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
Version: v0.17.0
Converted From:
Code: https://github.com/tensorflow/models/tree/master/research/deeplab
Models:
- Name: deeplabv3_r50-d8_512x1024_40k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 389.11
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.09
mIoU(ms+flip): 80.45
Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth
- Name: deeplabv3_r101-d8_512x1024_40k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 520.83
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.12
mIoU(ms+flip): 79.61
Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth
- Name: deeplabv3_r50-d8_769x769_40k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 900.9
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 6.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.58
mIoU(ms+flip): 79.89
Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth
- Name: deeplabv3_r101-d8_769x769_40k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 1204.82
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 10.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.27
mIoU(ms+flip): 80.11
Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth
- Name: deeplabv3_r18-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-18-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 72.57
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: 76.7
mIoU(ms+flip): 78.27
Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth
- Name: deeplabv3_r50-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.32
mIoU(ms+flip): 80.57
Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth
- Name: deeplabv3_r101-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.2
mIoU(ms+flip): 81.21
Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth
- Name: deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 259.07
hardware: V100
backend: PyTorch
batch size: 1
mode: FP16
resolution: (512,1024)
Training Memory (GB): 5.75
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.48
Config: configs/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-774d9cec.pth
- Name: deeplabv3_r18-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-18-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 180.18
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: 76.6
mIoU(ms+flip): 78.26
Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth
- Name: deeplabv3_r50-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.89
mIoU(ms+flip): 81.06
Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth
- Name: deeplabv3_r101-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.67
mIoU(ms+flip): 80.81
Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth
- Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101-D16-MG124
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.36
mIoU(ms+flip): 79.84
Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth
- Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-18b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 71.79
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: 76.26
mIoU(ms+flip): 77.88
Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth
- Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-50b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 364.96
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 6.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.63
mIoU(ms+flip): 80.98
Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth
- Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101b-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 552.49
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 9.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.01
mIoU(ms+flip): 81.21
Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth
- Name: deeplabv3_r18b-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-18b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 172.71
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 1.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.63
mIoU(ms+flip): 77.51
Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth
- Name: deeplabv3_r50b-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-50b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 862.07
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 6.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.8
mIoU(ms+flip): 80.27
Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth
- Name: deeplabv3_r101b-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
backbone: R-101b-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 1219.51
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 10.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.41
mIoU(ms+flip): 80.73
Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth
- Name: deeplabv3_r50-d8_512x512_80k_ade20k
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 67.75
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 8.9
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.42
mIoU(ms+flip): 43.28
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth
- Name: deeplabv3_r101-d8_512x512_80k_ade20k
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 98.62
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 12.4
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.08
mIoU(ms+flip): 45.19
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth
- Name: deeplabv3_r50-d8_512x512_160k_ade20k
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.66
mIoU(ms+flip): 44.09
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth
- Name: deeplabv3_r101-d8_512x512_160k_ade20k
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.0
mIoU(ms+flip): 46.66
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth
- Name: deeplabv3_r50-d8_512x512_20k_voc12aug
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 72.05
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.17
mIoU(ms+flip): 77.42
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth
- Name: deeplabv3_r101-d8_512x512_20k_voc12aug
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 101.94
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.7
mIoU(ms+flip): 79.95
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth
- Name: deeplabv3_r50-d8_512x512_40k_voc12aug
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.68
mIoU(ms+flip): 78.78
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth
- Name: deeplabv3_r101-d8_512x512_40k_voc12aug
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.92
mIoU(ms+flip): 79.18
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth
- Name: deeplabv3_r101-d8_480x480_40k_pascal_context
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
inference time (ms/im):
- value: 141.04
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (480,480)
Training Memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 46.55
mIoU(ms+flip): 47.81
Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth
- Name: deeplabv3_r101-d8_480x480_80k_pascal_context
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
Metrics:
mIoU: 46.42
mIoU(ms+flip): 47.53
Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth
- Name: deeplabv3_r101-d8_480x480_40k_pascal_context_59
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context 59
Metrics:
mIoU: 52.61
mIoU(ms+flip): 54.28
Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth
- Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context 59
Metrics:
mIoU: 52.46
mIoU(ms+flip): 54.09
Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth
- Name: deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 92.59
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 34.66
mIoU(ms+flip): 36.08
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-b35f789d.pth
- Name: deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 114.94
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 13.2
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 37.3
mIoU(ms+flip): 38.42
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-c49752cb.pth
- Name: deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 35.73
mIoU(ms+flip): 37.09
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-dc76f3ff.pth
- Name: deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
Metrics:
mIoU: 37.81
mIoU(ms+flip): 38.8
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-636cb433.pth
- Name: deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 92.59
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 39.38
mIoU(ms+flip): 40.03
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016-88675c24.pth
- Name: deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 114.94
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 13.2
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 40.87
mIoU(ms+flip): 41.5
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252-13600dc2.pth
- Name: deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 41.09
mIoU(ms+flip): 41.69
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016-49f2812b.pth
- Name: deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 41.82
mIoU(ms+flip): 42.49
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402-f035acfd.pth
- Name: deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k
In Collection: DeepLabV3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 320000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 41.37
mIoU(ms+flip): 42.22
Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403-51b21115.pth
- Name: deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k
In Collection: DeepLabV3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 320000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 42.61
mIoU(ms+flip): 43.42
Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402-3cbca14d.pth
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