f72727c563
* init scripts * update markdown * update markdown * add docs * delete mit converter and use torch load function * rename segformer readme * update doc * modify doc * 更新中文文档 * Update useful_tools.md * Update useful_tools.md * modify doc * update segformer.yml
161 lines
4.8 KiB
YAML
161 lines
4.8 KiB
YAML
Collections:
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- Metadata:
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Training Data:
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- ADE20k
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Name: segformer
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Models:
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- Config: configs/segformer/segformer_mit-b0_512x512_160k_ade20k.py
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In Collection: segformer
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Metadata:
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backbone: MIT-B0
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crop size: (512,512)
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inference time (ms/im):
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- backend: PyTorch
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batch size: 1
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hardware: V100
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mode: FP32
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resolution: (512,512)
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value: 19.49
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lr schd: 160000
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memory (GB): 2.1
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Name: segformer_mit-b0_512x512_160k_ade20k
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Results:
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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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Task: Semantic Segmentation
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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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- Config: configs/segformer/segformer_mit-b1_512x512_160k_ade20k.py
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In Collection: segformer
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Metadata:
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backbone: MIT-B1
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crop size: (512,512)
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inference time (ms/im):
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- backend: PyTorch
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batch size: 1
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hardware: V100
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mode: FP32
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resolution: (512,512)
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value: 20.98
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lr schd: 160000
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memory (GB): 2.6
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Name: segformer_mit-b1_512x512_160k_ade20k
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Results:
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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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Task: Semantic Segmentation
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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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- Config: configs/segformer/segformer_mit-b2_512x512_160k_ade20k.py
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In Collection: segformer
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Metadata:
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backbone: MIT-B2
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crop size: (512,512)
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inference time (ms/im):
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- backend: PyTorch
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batch size: 1
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hardware: V100
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mode: FP32
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resolution: (512,512)
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value: 32.38
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lr schd: 160000
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memory (GB): 3.6
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Name: segformer_mit-b2_512x512_160k_ade20k
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Results:
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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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Task: Semantic Segmentation
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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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- Config: configs/segformer/segformer_mit-b3_512x512_160k_ade20k.py
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In Collection: segformer
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Metadata:
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backbone: MIT-B3
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crop size: (512,512)
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inference time (ms/im):
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- backend: PyTorch
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batch size: 1
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hardware: V100
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mode: FP32
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resolution: (512,512)
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value: 45.23
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lr schd: 160000
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memory (GB): 4.8
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Name: segformer_mit-b3_512x512_160k_ade20k
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Results:
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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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Task: Semantic Segmentation
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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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- Config: configs/segformer/segformer_mit-b4_512x512_160k_ade20k.py
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In Collection: segformer
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Metadata:
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backbone: MIT-B4
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crop size: (512,512)
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inference time (ms/im):
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- backend: PyTorch
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batch size: 1
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hardware: V100
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mode: FP32
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resolution: (512,512)
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value: 64.72
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lr schd: 160000
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memory (GB): 6.1
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Name: segformer_mit-b4_512x512_160k_ade20k
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Results:
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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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Task: Semantic Segmentation
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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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- Config: configs/segformer/segformer_mit-b5_512x512_160k_ade20k.py
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In Collection: segformer
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Metadata:
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backbone: MIT-B5
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crop size: (512,512)
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inference time (ms/im):
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- backend: PyTorch
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batch size: 1
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hardware: V100
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mode: FP32
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resolution: (512,512)
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value: 84.1
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lr schd: 160000
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memory (GB): 7.2
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Name: segformer_mit-b5_512x512_160k_ade20k
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Results:
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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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Task: Semantic Segmentation
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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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- Config: configs/segformer/segformer_mit-b5_640x640_160k_ade20k.py
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In Collection: segformer
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Metadata:
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backbone: MIT-B5
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crop size: (640,640)
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inference time (ms/im):
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- backend: PyTorch
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batch size: 1
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hardware: V100
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mode: FP32
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resolution: (640,640)
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value: 88.5
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lr schd: 160000
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memory (GB): 11.5
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Name: segformer_mit-b5_640x640_160k_ade20k
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Results:
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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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Task: Semantic Segmentation
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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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