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STDC/configs/vit/vit.yml
谢昕辰 52b4fa5b9a
[Enhancement] md2yml pre-commit hook (#732) 
* init script

* update scripts and generate new yml

* fix lint: deeplabv3plus.yml

* modify resolution representation

* remove  field

* format crop_size
2021-07-31 09:31:58 -07:00

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Collections:
- Name: vit
Metadata:
Training Data:
- ADE20K
Models:
- Name: upernet_vit-b16_mln_512x512_80k_ade20k
In Collection: vit
Metadata:
backbone: ViT-B + MLN
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 144.09
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.2
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 47.71
mIoU(ms+flip): 49.51
Config: configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_80k_ade20k/upernet_vit-b16_mln_512x512_80k_ade20k-0403cee1.pth
- Name: upernet_vit-b16_mln_512x512_160k_ade20k
In Collection: vit
Metadata:
backbone: ViT-B + MLN
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 131.93
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.2
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 46.75
mIoU(ms+flip): 48.46
Config: configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_160k_ade20k/upernet_vit-b16_mln_512x512_160k_ade20k-852fa768.pth
- Name: upernet_vit-b16_ln_mln_512x512_160k_ade20k
In Collection: vit
Metadata:
backbone: ViT-B + LN + MLN
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 146.63
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.21
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 47.73
mIoU(ms+flip): 49.95
Config: configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k/upernet_vit-b16_ln_mln_512x512_160k_ade20k-f444c077.pth
- Name: upernet_deit-s16_512x512_80k_ade20k
In Collection: vit
Metadata:
backbone: DeiT-S
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 33.5
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 4.68
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.96
mIoU(ms+flip): 43.79
Config: configs/vit/upernet_deit-s16_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_80k_ade20k/upernet_deit-s16_512x512_80k_ade20k-afc93ec2.pth
- Name: upernet_deit-s16_512x512_160k_ade20k
In Collection: vit
Metadata:
backbone: DeiT-S
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 34.26
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 4.68
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.87
mIoU(ms+flip): 43.79
Config: configs/vit/upernet_deit-s16_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_160k_ade20k/upernet_deit-s16_512x512_160k_ade20k-5110d916.pth
- Name: upernet_deit-s16_mln_512x512_160k_ade20k
In Collection: vit
Metadata:
backbone: DeiT-S + MLN
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 89.45
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 5.69
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.82
mIoU(ms+flip): 45.07
Config: configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_mln_512x512_160k_ade20k/upernet_deit-s16_mln_512x512_160k_ade20k-fb9a5dfb.pth
- Name: upernet_deit-s16_ln_mln_512x512_160k_ade20k
In Collection: vit
Metadata:
backbone: DeiT-S + LN + MLN
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 80.71
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 5.69
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.52
mIoU(ms+flip): 45.01
Config: configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k/upernet_deit-s16_ln_mln_512x512_160k_ade20k-c0cd652f.pth
- Name: upernet_deit-b16_512x512_80k_ade20k
In Collection: vit
Metadata:
backbone: DeiT-B
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 103.2
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 7.75
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.24
mIoU(ms+flip): 46.73
Config: configs/vit/upernet_deit-b16_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_80k_ade20k/upernet_deit-b16_512x512_80k_ade20k-1e090789.pth
- Name: upernet_deit-b16_512x512_160k_ade20k
In Collection: vit
Metadata:
backbone: DeiT-B
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 96.25
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 7.75
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.36
mIoU(ms+flip): 47.16
Config: configs/vit/upernet_deit-b16_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_160k_ade20k/upernet_deit-b16_512x512_160k_ade20k-828705d7.pth
- Name: upernet_deit-b16_mln_512x512_160k_ade20k
In Collection: vit
Metadata:
backbone: DeiT-B + MLN
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 128.53
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.21
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.46
mIoU(ms+flip): 47.16
Config: configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_mln_512x512_160k_ade20k/upernet_deit-b16_mln_512x512_160k_ade20k-4e1450f3.pth
- Name: upernet_deit-b16_ln_mln_512x512_160k_ade20k
In Collection: vit
Metadata:
backbone: DeiT-B + LN + MLN
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 129.03
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.21
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.37
mIoU(ms+flip): 47.23
Config: configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k/upernet_deit-b16_ln_mln_512x512_160k_ade20k-8a959c14.pth
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