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mmsegmentation/configs/resnest/resnest.yml

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Collections:
- Name: resnest
Metadata:
Training Data:
- Cityscapes
- ADE20k
Paper:
URL: https://arxiv.org/abs/2004.08955
Title: 'ResNeSt: Split-Attention Networks'
README: configs/resnest/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
Version: v0.17.0
Converted From:
Code: https://github.com/zhanghang1989/ResNeSt
Models:
- Name: fcn_s101-d8_512x1024_80k_cityscapes
In Collection: resnest
Metadata:
backbone: S-101-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 418.41
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 11.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.56
mIoU(ms+flip): 78.98
Config: configs/resnest/fcn_s101-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth
- Name: pspnet_s101-d8_512x1024_80k_cityscapes
In Collection: resnest
Metadata:
backbone: S-101-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 396.83
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 11.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.57
mIoU(ms+flip): 79.19
Config: configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth
- Name: deeplabv3_s101-d8_512x1024_80k_cityscapes
In Collection: resnest
Metadata:
backbone: S-101-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 531.91
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 11.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.67
mIoU(ms+flip): 80.51
Config: configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth
- Name: deeplabv3plus_s101-d8_512x1024_80k_cityscapes
In Collection: resnest
Metadata:
backbone: S-101-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 423.73
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 13.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.62
mIoU(ms+flip): 80.27
Config: configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth
- Name: fcn_s101-d8_512x512_160k_ade20k
In Collection: resnest
Metadata:
backbone: S-101-D8
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 77.76
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 14.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
Metrics:
mIoU: 45.62
mIoU(ms+flip): 46.16
Config: configs/resnest/fcn_s101-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth
- Name: pspnet_s101-d8_512x512_160k_ade20k
In Collection: resnest
Metadata:
backbone: S-101-D8
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 76.8
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 14.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
Metrics:
mIoU: 45.44
mIoU(ms+flip): 46.28
Config: configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth
- Name: deeplabv3_s101-d8_512x512_160k_ade20k
In Collection: resnest
Metadata:
backbone: S-101-D8
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 107.76
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 14.6
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
Metrics:
mIoU: 45.71
mIoU(ms+flip): 46.59
Config: configs/resnest/deeplabv3_s101-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth
- Name: deeplabv3plus_s101-d8_512x512_160k_ade20k
In Collection: resnest
Metadata:
backbone: S-101-D8
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 83.61
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 16.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
Metrics:
mIoU: 46.47
mIoU(ms+flip): 47.27
Config: configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth
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