165 lines
5.1 KiB
YAML
165 lines
5.1 KiB
YAML
Collections:
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- Name: SETR
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Metadata:
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Training Data:
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- ADE20K
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- Cityscapes
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Paper:
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URL: https://arxiv.org/abs/2012.15840
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Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
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with Transformers
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README: configs/setr/README.md
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Code:
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URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
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Version: v0.17.0
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Converted From:
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Code: https://github.com/fudan-zvg/SETR
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Models:
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- Name: setr_vit-l_naive_8xb2-160k_ade20k-512x512
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In Collection: SETR
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Metadata:
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backbone: ViT-L
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crop size: (512,512)
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lr schd: 160000
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inference time (ms/im):
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- value: 211.86
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 18.4
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 48.28
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mIoU(ms+flip): 49.56
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Config: configs/setr/setr_vit-l_naive_8xb2-160k_ade20k-512x512.py
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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
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- Name: setr_vit-l_pup_8xb2-160k_ade20k-512x512
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In Collection: SETR
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Metadata:
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backbone: ViT-L
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crop size: (512,512)
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lr schd: 160000
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inference time (ms/im):
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- value: 222.22
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 19.54
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 48.24
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mIoU(ms+flip): 49.99
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Config: configs/setr/setr_vit-l_pup_8xb2-160k_ade20k-512x512.py
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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
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- Name: setr_vit-l-mla_8xb1-160k_ade20k-512x512
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In Collection: SETR
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Metadata:
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backbone: ViT-L
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crop size: (512,512)
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lr schd: 160000
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Training Memory (GB): 10.96
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 47.34
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mIoU(ms+flip): 49.05
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Config: configs/setr/setr_vit-l-mla_8xb1-160k_ade20k-512x512.py
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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
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- Name: setr_vit-l_mla_8xb2-160k_ade20k-512x512
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In Collection: SETR
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Metadata:
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backbone: ViT-L
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crop size: (512,512)
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lr schd: 160000
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inference time (ms/im):
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- value: 190.48
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 17.3
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 47.39
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mIoU(ms+flip): 49.37
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Config: configs/setr/setr_vit-l_mla_8xb2-160k_ade20k-512x512.py
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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
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- Name: setr_vit-l_naive_8xb1-80k_cityscapes-768x768
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In Collection: SETR
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Metadata:
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backbone: ViT-L
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crop size: (768,768)
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lr schd: 80000
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inference time (ms/im):
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- value: 2564.1
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (768,768)
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Training Memory (GB): 24.06
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 78.1
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mIoU(ms+flip): 80.22
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Config: configs/setr/setr_vit-l_naive_8xb1-80k_cityscapes-768x768.py
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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
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- Name: setr_vit-l_pup_8xb1-80k_cityscapes-768x768
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In Collection: SETR
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Metadata:
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backbone: ViT-L
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crop size: (768,768)
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lr schd: 80000
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inference time (ms/im):
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- value: 2702.7
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (768,768)
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Training Memory (GB): 27.96
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.21
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mIoU(ms+flip): 81.02
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Config: configs/setr/setr_vit-l_pup_8xb1-80k_cityscapes-768x768.py
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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
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- Name: setr_vit-l_mla_8xb1-80k_cityscapes-768x768
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In Collection: SETR
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Metadata:
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backbone: ViT-L
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crop size: (768,768)
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lr schd: 80000
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inference time (ms/im):
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- value: 2439.02
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (768,768)
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Training Memory (GB): 24.1
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 77.0
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mIoU(ms+flip): 79.59
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Config: configs/setr/setr_vit-l_mla_8xb1-80k_cityscapes-768x768.py
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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
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