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beit.yml
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beit.yml
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Models:
- Name: upernet_beit-base_8x2_640x640_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: BEiT-B
crop size: (640,640)
lr schd: 160000
inference time (ms/im):
- value: 500.0
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (640,640)
Training Memory (GB): 15.88
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 53.08
mIoU(ms+flip): 53.84
Config: configs/beit/upernet_beit-base_8x2_640x640_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k-eead221d.pth
- Name: upernet_beit-large_fp16_8x1_640x640_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: BEiT-L
crop size: (640,640)
lr schd: 320000
inference time (ms/im):
- value: 1041.67
hardware: V100
backend: PyTorch
batch size: 1
mode: FP16
resolution: (640,640)
Training Memory (GB): 22.64
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 56.33
mIoU(ms+flip): 56.84
Config: configs/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k-8fc0dd5d.pth