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Collections:
- Name: SETR
License: Apache License 2.0
Metadata:
Training Data:
- ADE20K
- Cityscapes
Paper:
Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
with Transformers
URL: https://arxiv.org/abs/2012.15840
README: configs/setr/README.md
Frameworks:
- PyTorch
Models:
- Name: setr_vit-l_naive_8xb2-160k_ade20k-512x512
In Collection: SETR
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 48.28
mIoU(ms+flip): 49.56
Config: configs/setr/setr_vit-l_naive_8xb2-160k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- ViT-L
- SETR
- Naive
Training Resources: 8x V100 GPUS
Memory (GB): 18.4
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258.log.json
Paper:
Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
with Transformers
URL: https://arxiv.org/abs/2012.15840
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
Framework: PyTorch
- Name: setr_vit-l_pup_8xb2-160k_ade20k-512x512
In Collection: SETR
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 48.24
mIoU(ms+flip): 49.99
Config: configs/setr/setr_vit-l_pup_8xb2-160k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- ViT-L
- SETR
- PUP
Training Resources: 8x V100 GPUS
Memory (GB): 19.54
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343.log.json
Paper:
Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
with Transformers
URL: https://arxiv.org/abs/2012.15840
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
Framework: PyTorch
- Name: setr_vit-l-mla_8xb1-160k_ade20k-512x512
In Collection: SETR
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 47.34
mIoU(ms+flip): 49.05
Config: configs/setr/setr_vit-l-mla_8xb1-160k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 8
Architecture:
- ViT-L
- SETR
- MLA
Training Resources: 8x V100 GPUS
Memory (GB): 10.96
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118.log.json
Paper:
Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
with Transformers
URL: https://arxiv.org/abs/2012.15840
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
Framework: PyTorch
- Name: setr_vit-l_mla_8xb2-160k_ade20k-512x512
In Collection: SETR
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 47.39
mIoU(ms+flip): 49.37
Config: configs/setr/setr_vit-l_mla_8xb2-160k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- ViT-L
- SETR
- MLA
Training Resources: 8x V100 GPUS
Memory (GB): 17.3
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
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057.log.json
Paper:
Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
with Transformers
URL: https://arxiv.org/abs/2012.15840
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
Framework: PyTorch
- Name: setr_vit-l_naive_8xb1-80k_cityscapes-768x768
In Collection: SETR
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.1
mIoU(ms+flip): 80.22
Config: configs/setr/setr_vit-l_naive_8xb1-80k_cityscapes-768x768.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- ViT-L
- SETR
- Naive
Training Resources: 8x V100 GPUS
Memory (GB): 24.06
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
Training log: 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.log.json
Paper:
Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
with Transformers
URL: https://arxiv.org/abs/2012.15840
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
Framework: PyTorch
- Name: setr_vit-l_pup_8xb1-80k_cityscapes-768x768
In Collection: SETR
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.21
mIoU(ms+flip): 81.02
Config: configs/setr/setr_vit-l_pup_8xb1-80k_cityscapes-768x768.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- ViT-L
- SETR
- PUP
Training Resources: 8x V100 GPUS
Memory (GB): 27.96
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
Training log: 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.log.json
Paper:
Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
with Transformers
URL: https://arxiv.org/abs/2012.15840
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
Framework: PyTorch
- Name: setr_vit-l_mla_8xb1-80k_cityscapes-768x768
In Collection: SETR
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.0
mIoU(ms+flip): 79.59
Config: configs/setr/setr_vit-l_mla_8xb1-80k_cityscapes-768x768.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- ViT-L
- SETR
- MLA
Training Resources: 8x V100 GPUS
Memory (GB): 24.1
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
Training log: 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.log.json
Paper:
Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
with Transformers
URL: https://arxiv.org/abs/2012.15840
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
Framework: PyTorch
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