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Collections: |
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- Name: SETR |
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License: Apache License 2.0 |
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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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Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective |
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with Transformers |
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URL: https://arxiv.org/abs/2012.15840 |
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README: configs/setr/README.md |
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Frameworks: |
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- PyTorch |
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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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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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Metadata: |
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Training Data: ADE20K |
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Batch Size: 16 |
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Architecture: |
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- ViT-L |
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- SETR |
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- Naive |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 18.4 |
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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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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 |
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Paper: |
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Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective |
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with Transformers |
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URL: https://arxiv.org/abs/2012.15840 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11 |
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Framework: PyTorch |
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- Name: setr_vit-l_pup_8xb2-160k_ade20k-512x512 |
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In Collection: SETR |
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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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Metadata: |
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Training Data: ADE20K |
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Batch Size: 16 |
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Architecture: |
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- ViT-L |
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- SETR |
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- PUP |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 19.54 |
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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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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 |
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Paper: |
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Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective |
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with Transformers |
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URL: https://arxiv.org/abs/2012.15840 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11 |
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Framework: PyTorch |
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- Name: setr_vit-l-mla_8xb1-160k_ade20k-512x512 |
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In Collection: SETR |
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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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Metadata: |
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Training Data: ADE20K |
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Batch Size: 8 |
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Architecture: |
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- ViT-L |
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- SETR |
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- MLA |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 10.96 |
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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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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 |
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Paper: |
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Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective |
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with Transformers |
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URL: https://arxiv.org/abs/2012.15840 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11 |
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Framework: PyTorch |
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- Name: setr_vit-l_mla_8xb2-160k_ade20k-512x512 |
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In Collection: SETR |
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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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Metadata: |
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Training Data: ADE20K |
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Batch Size: 16 |
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Architecture: |
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- ViT-L |
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- SETR |
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- MLA |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 17.3 |
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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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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 |
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Paper: |
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Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective |
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with Transformers |
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URL: https://arxiv.org/abs/2012.15840 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11 |
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Framework: PyTorch |
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- Name: setr_vit-l_naive_8xb1-80k_cityscapes-768x768 |
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In Collection: SETR |
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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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Metadata: |
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Training Data: Cityscapes |
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Batch Size: 8 |
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Architecture: |
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- ViT-L |
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- SETR |
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- Naive |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 24.06 |
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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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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 |
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Paper: |
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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 |
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Framework: PyTorch |
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- Name: setr_vit-l_pup_8xb1-80k_cityscapes-768x768 |
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In Collection: SETR |
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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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Metadata: |
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Training Data: Cityscapes |
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Batch Size: 8 |
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Architecture: |
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- ViT-L |
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- SETR |
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- PUP |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 27.96 |
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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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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 |
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Paper: |
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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 |
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Framework: PyTorch |
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- Name: setr_vit-l_mla_8xb1-80k_cityscapes-768x768 |
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In Collection: SETR |
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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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Metadata: |
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Training Data: Cityscapes |
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Batch Size: 8 |
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Architecture: |
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- ViT-L |
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- SETR |
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- MLA |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 24.1 |
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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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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 |
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Paper: |
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Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective |
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with Transformers |
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URL: https://arxiv.org/abs/2012.15840 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11 |
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Framework: PyTorch |
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