Models:
- Name: upernet_mae-base_fp16_8x2_512x512_160k_ade20k
  In Collection: UPerNet
  Metadata:
    backbone: ViT-B
    crop size: (512,512)
    lr schd: 160000
    inference time (ms/im):
    - value: 140.06
      hardware: V100
      backend: PyTorch
      batch size: 1
      mode: FP16
      resolution: (512,512)
    Training Memory (GB): 9.96
  Results:
  - Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 48.13
      mIoU(ms+flip): 48.7
  Config: configs/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k.py
  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k/upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752-f92a2975.pth
