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mmsegmentation/configs/swin/swin.yml

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Models:
- Name: upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
In Collection: UPerNet
Metadata:
backbone: Swin-T
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 47.48
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 5.02
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.41
mIoU(ms+flip): 45.79
Config: configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth
- Name: upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
In Collection: UPerNet
Metadata:
backbone: Swin-S
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 67.93
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.17
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 47.72
mIoU(ms+flip): 49.24
Config: configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth
- Name: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
In Collection: UPerNet
Metadata:
backbone: Swin-B
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 79.05
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.61
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 47.99
mIoU(ms+flip): 49.57
Config: configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340-593b0e13.pth
- Name: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K
In Collection: UPerNet
Metadata:
backbone: Swin-B
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 50.31
mIoU(ms+flip): 51.9
Config: configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650-762e2178.pth
- Name: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K
In Collection: UPerNet
Metadata:
backbone: Swin-B
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 82.64
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 8.52
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 48.35
mIoU(ms+flip): 49.65
Config: configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K_20210531_132020-05b22ea4.pth
- Name: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K
In Collection: UPerNet
Metadata:
backbone: Swin-B
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 50.76
mIoU(ms+flip): 52.4
Config: configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth
- Name: upernet_swin_large_patch4_window7_512x512_pretrain_224x224_22K_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: Swin-L
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 121.51
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 10.98
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 51.17
mIoU(ms+flip): 52.99
Config: configs/swin/upernet_swin_large_patch4_window7_512x512_pretrain_224x224_22K_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window7_512x512_pretrain_224x224_22K_160k_ade20k/upernet_swin_large_patch4_window7_512x512_pretrain_224x224_22K_160k_ade20k_20220318_015320-48d180dd.pth
- Name: upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: Swin-L
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 132.1
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 12.42
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 52.25
mIoU(ms+flip): 54.12
Config: configs/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth
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