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Collections: |
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- Name: Segformer |
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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: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with |
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Transformers' |
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URL: https://arxiv.org/abs/2105.15203 |
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README: configs/segformer/README.md |
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Frameworks: |
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- PyTorch |
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Models: |
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- Name: segformer_mit-b0_8xb2-160k_ade20k-512x512 |
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In Collection: Segformer |
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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: 37.41 |
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mIoU(ms+flip): 38.34 |
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Config: configs/segformer/segformer_mit-b0_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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- MIT-B0 |
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- Segformer |
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Training Resources: 8x 1080 Ti GPUS |
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Memory (GB): 2.1 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530-8ffa8fda.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530.log.json |
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Paper: |
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Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with |
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Transformers' |
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URL: https://arxiv.org/abs/2105.15203 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 |
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Framework: PyTorch |
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- Name: segformer_mit-b1_8xb2-160k_ade20k-512x512 |
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In Collection: Segformer |
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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: 40.97 |
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mIoU(ms+flip): 42.54 |
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Config: configs/segformer/segformer_mit-b1_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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- MIT-B1 |
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- Segformer |
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Training Resources: 8x TITAN Xp GPUS |
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Memory (GB): 2.6 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106-d70e859d.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106.log.json |
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Paper: |
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Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with |
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Transformers' |
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URL: https://arxiv.org/abs/2105.15203 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 |
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Framework: PyTorch |
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- Name: segformer_mit-b2_8xb2-160k_ade20k-512x512 |
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In Collection: Segformer |
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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: 45.58 |
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mIoU(ms+flip): 47.03 |
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Config: configs/segformer/segformer_mit-b2_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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- MIT-B2 |
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- Segformer |
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Training Resources: 8x TITAN Xp GPUS |
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Memory (GB): 3.6 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103-cbd414ac.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103.log.json |
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Paper: |
|
Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with |
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Transformers' |
|
URL: https://arxiv.org/abs/2105.15203 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 |
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Framework: PyTorch |
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- Name: segformer_mit-b3_8xb2-160k_ade20k-512x512 |
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In Collection: Segformer |
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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: 47.82 |
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mIoU(ms+flip): 48.81 |
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Config: configs/segformer/segformer_mit-b3_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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- MIT-B3 |
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- Segformer |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 4.8 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410-962b98d2.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410.log.json |
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Paper: |
|
Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with |
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Transformers' |
|
URL: https://arxiv.org/abs/2105.15203 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 |
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Framework: PyTorch |
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- Name: segformer_mit-b4_8xb2-160k_ade20k-512x512 |
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In Collection: Segformer |
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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: 48.46 |
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mIoU(ms+flip): 49.76 |
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Config: configs/segformer/segformer_mit-b4_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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- MIT-B4 |
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- Segformer |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 6.1 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055-7f509d7d.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055.log.json |
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Paper: |
|
Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with |
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Transformers' |
|
URL: https://arxiv.org/abs/2105.15203 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 |
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Framework: PyTorch |
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- Name: segformer_mit-b5_8xb2-160k_ade20k-512x512 |
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In Collection: Segformer |
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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: 49.13 |
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mIoU(ms+flip): 50.22 |
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Config: configs/segformer/segformer_mit-b5_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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- MIT-B5 |
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- Segformer |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 7.2 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235-94cedf59.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235.log.json |
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Paper: |
|
Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with |
|
Transformers' |
|
URL: https://arxiv.org/abs/2105.15203 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 |
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Framework: PyTorch |
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- Name: segformer_mit-b5_8xb2-160k_ade20k-640x640 |
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In Collection: Segformer |
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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: 49.62 |
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mIoU(ms+flip): 50.36 |
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Config: configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-640x640.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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- MIT-B5 |
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- Segformer |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 11.5 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243-41d2845b.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243.log.json |
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Paper: |
|
Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with |
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Transformers' |
|
URL: https://arxiv.org/abs/2105.15203 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 |
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Framework: PyTorch |
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- Name: segformer_mit-b0_8xb1-160k_cityscapes-1024x1024 |
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In Collection: Segformer |
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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: 76.54 |
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mIoU(ms+flip): 78.22 |
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Config: configs/segformer/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.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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- MIT-B0 |
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- Segformer |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 3.64 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857-e7f88502.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857.log.json |
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Paper: |
|
Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with |
|
Transformers' |
|
URL: https://arxiv.org/abs/2105.15203 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 |
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Framework: PyTorch |
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- Name: segformer_mit-b1_8xb1-160k_cityscapes-1024x1024 |
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In Collection: Segformer |
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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: 78.56 |
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mIoU(ms+flip): 79.73 |
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Config: configs/segformer/segformer_mit-b1_8xb1-160k_cityscapes-1024x1024.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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- MIT-B1 |
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- Segformer |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 4.49 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213-655c7b3f.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213.log.json |
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Paper: |
|
Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with |
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Transformers' |
|
URL: https://arxiv.org/abs/2105.15203 |
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Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 |
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Framework: PyTorch |
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- Name: segformer_mit-b2_8xb1-160k_cityscapes-1024x1024 |
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In Collection: Segformer |
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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: 81.08 |
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mIoU(ms+flip): 82.18 |
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Config: configs/segformer/segformer_mit-b2_8xb1-160k_cityscapes-1024x1024.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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- MIT-B2 |
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- Segformer |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 7.42 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205-6096669a.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205.log.json |
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Paper: |
|
Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with |
|
Transformers' |
|
URL: https://arxiv.org/abs/2105.15203 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 |
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Framework: PyTorch |
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- Name: segformer_mit-b3_8xb1-160k_cityscapes-1024x1024 |
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In Collection: Segformer |
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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: 81.94 |
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mIoU(ms+flip): 83.14 |
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Config: configs/segformer/segformer_mit-b3_8xb1-160k_cityscapes-1024x1024.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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- MIT-B3 |
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- Segformer |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 10.86 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823-a8f8a177.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823.log.json |
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Paper: |
|
Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with |
|
Transformers' |
|
URL: https://arxiv.org/abs/2105.15203 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 |
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Framework: PyTorch |
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- Name: segformer_mit-b4_8xb1-160k_cityscapes-1024x1024 |
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In Collection: Segformer |
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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: 81.89 |
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mIoU(ms+flip): 83.38 |
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Config: configs/segformer/segformer_mit-b4_8xb1-160k_cityscapes-1024x1024.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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- MIT-B4 |
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- Segformer |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 15.07 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709-07f6c333.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709.log.json |
|
Paper: |
|
Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with |
|
Transformers' |
|
URL: https://arxiv.org/abs/2105.15203 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 |
|
Framework: PyTorch |
|
- Name: segformer_mit-b5_8xb1-160k_cityscapes-1024x1024 |
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In Collection: Segformer |
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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: 82.25 |
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mIoU(ms+flip): 83.48 |
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Config: configs/segformer/segformer_mit-b5_8xb1-160k_cityscapes-1024x1024.py |
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Metadata: |
|
Training Data: Cityscapes |
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Batch Size: 8 |
|
Architecture: |
|
- MIT-B5 |
|
- Segformer |
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Training Resources: 8x V100 GPUS |
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Memory (GB): 18.0 |
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934-87a052ec.pth |
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934.log.json |
|
Paper: |
|
Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with |
|
Transformers' |
|
URL: https://arxiv.org/abs/2105.15203 |
|
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246 |
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Framework: PyTorch |
|
|