configs/beit/metafile.yaml

Models:
- Name: beit-base_upernet_8xb2-160k_ade20k-640x640
  In Collection: UPerNet
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 53.08
      mIoU(ms+flip): 53.84
  Config: configs/beit/beit-base_upernet_8xb2-160k_ade20k-640x640.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 16
    Architecture:
    - BEiT-B
    - UPerNet
    Training Resources: 8x V100 GPUS
    Memory (GB): 15.88
  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
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k.log.json
  Paper:
    Title: 'BEiT: BERT Pre-Training of Image Transformers'
    URL: https://arxiv.org/abs/2106.08254
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/backbones/beit.py#1404
  Framework: PyTorch
- Name: beit-large_upernet_8xb1-amp-160k_ade20k-640x640
  In Collection: UPerNet
  Results:
    Task: Semantic Segmentation
    Dataset: ADE20K
    Metrics:
      mIoU: 56.33
      mIoU(ms+flip): 56.84
  Config: configs/beit/beit-large_upernet_8xb1-amp-160k_ade20k-640x640.py
  Metadata:
    Training Data: ADE20K
    Batch Size: 8
    Architecture:
    - BEiT-L
    - UPerNet
    Training Resources: 8x V100 GPUS
    Memory (GB): 22.64
  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
  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.log.json
  Paper:
    Title: 'BEiT: BERT Pre-Training of Image Transformers'
    URL: https://arxiv.org/abs/2106.08254
  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/backbones/beit.py#1404
  Framework: PyTorch