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setr.yml
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setr.yml
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Collections:
- Name: SETR
Metadata:
Training Data:
- ADE20K
- Cityscapes
Paper:
URL: https://arxiv.org/abs/2012.15840
Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
with Transformers
README: configs/setr/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
Version: v0.17.0
Converted From:
Code: https://github.com/fudan-zvg/SETR
Models:
- Name: setr_naive_512x512_160k_b16_ade20k
In Collection: SETR
Metadata:
backbone: ViT-L
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 211.86
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 18.4
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 48.28
mIoU(ms+flip): 49.56
Config: configs/setr/setr_naive_512x512_160k_b16_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258-061f24f5.pth
- Name: setr_pup_512x512_160k_b16_ade20k
In Collection: SETR
Metadata:
backbone: ViT-L
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 222.22
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 19.54
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 48.24
mIoU(ms+flip): 49.99
Config: configs/setr/setr_pup_512x512_160k_b16_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343-7e0ce826.pth
- Name: setr_mla_512x512_160k_b8_ade20k
In Collection: SETR
Metadata:
backbone: ViT-L
crop size: (512,512)
lr schd: 160000
Training Memory (GB): 10.96
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 47.34
mIoU(ms+flip): 49.05
Config: configs/setr/setr_mla_512x512_160k_b8_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118-c6d21df0.pth
- Name: setr_mla_512x512_160k_b16_ade20k
In Collection: SETR
Metadata:
backbone: ViT-L
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 190.48
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 17.3
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 47.39
mIoU(ms+flip): 49.37
Config: configs/setr/setr_mla_512x512_160k_b16_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057-f9741de7.pth
- Name: setr_vit-large_naive_8x1_768x768_80k_cityscapes
In Collection: SETR
Metadata:
backbone: ViT-L
crop size: (768,768)
lr schd: 80000
inference time (ms/im):
- value: 2564.1
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (768,768)
Training Memory (GB): 24.06
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.1
mIoU(ms+flip): 80.22
Config: configs/setr/setr_vit-large_naive_8x1_768x768_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_vit-large_8x1_768x768_80k_cityscapes/setr_naive_vit-large_8x1_768x768_80k_cityscapes_20211123_000505-20728e80.pth
- Name: setr_vit-large_pup_8x1_768x768_80k_cityscapes
In Collection: SETR
Metadata:
backbone: ViT-L
crop size: (768,768)
lr schd: 80000
inference time (ms/im):
- value: 2702.7
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (768,768)
Training Memory (GB): 27.96
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.21
mIoU(ms+flip): 81.02
Config: configs/setr/setr_vit-large_pup_8x1_768x768_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_vit-large_8x1_768x768_80k_cityscapes/setr_pup_vit-large_8x1_768x768_80k_cityscapes_20211122_155115-f6f37b8f.pth
- Name: setr_vit-large_mla_8x1_768x768_80k_cityscapes
In Collection: SETR
Metadata:
backbone: ViT-L
crop size: (768,768)
lr schd: 80000
inference time (ms/im):
- value: 2439.02
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (768,768)
Training Memory (GB): 24.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.0
mIoU(ms+flip): 79.59
Config: configs/setr/setr_vit-large_mla_8x1_768x768_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_vit-large_8x1_768x768_80k_cityscapes/setr_mla_vit-large_8x1_768x768_80k_cityscapes_20211119_101003-7f8dccbe.pth