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resnest.yml
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resnest.yml
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Models:
- Name: fcn_s101-d8_512x1024_80k_cityscapes
In Collection: FCN
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)
Training 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: PSPNet
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)
Training 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: DeepLabV3
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)
Training 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: DeepLabV3+
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)
Training 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: FCN
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)
Training 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: PSPNet
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)
Training 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: DeepLabV3
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)
Training 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: DeepLabV3+
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)
Training 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