hunk.code_window:             mlp_ratio (int): ratio of mlp hidden dim to embedding dim
               qkv_bias (bool): enable bias for qkv if True
               init_values: (float): layer-scale init values
               drop_rate (float): dropout rate
               attn_drop_rate (float): attention dropout rate
               drop_path_rate (float): stochastic depth rate
               weight_init (str): weight init scheme
  
hunk.labels: keep keep add keep keep keep keep
hunk.after_edit:             class_token (bool): use class token (default: False)
               rel_pos_ty pe (str): type of relative position
               shared_rel_pos (bool): share relative pos across all blocks
               fc_norm (bool): use pre classifier norm instead of pre-pool
  
hunk.file_path: timm/models/vision_transformer_relpos.py
hunk.type: add
file: # FBNet
  
  **FBNet** is a type of convolutional neural architectures discovered through [DNAS](https://paperswithcode.com/method/dnas) neural architecture search. It utilises a basic type of image model block inspired by [MobileNetv2](https://paperswithcode.com/method/mobilenetv2) that utilises depthwise convolutions and an inverted residual structure (see components).
  
  The principal building block is the [FBNet Block](https://paperswithcode.com/method/fbnet-block).
  
  {% include 'code_snippets.md' %}
  
  ## How do I train this model?
  
  You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
  
  ## Citation
  
  ```BibTeX
  @misc{wu2019fbnet,
        title={FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search}, 
        author={Bichen Wu and Xiaoliang Dai and Peizhao Zhang and Yanghan Wang and Fei Sun and Yiming Wu and Yuandong Tian and Peter Vajda and Yangqing Jia and Kurt Keutzer},
        year={2019},
        eprint={1812.03443},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: FBNet
    Paper:
      Title: 'FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural
        Architecture Search'
      URL: https://paperswithcode.com/paper/fbnet-hardware-aware-efficient-convnet-design
  Models:
  - Name: fbnetc_100
    In Collection: FBNet
    Metadata:
      FLOPs: 508940064
      Parameters: 5570000
      File Size: 22525094
      Architecture:
      - 1x1 Convolution
      - Convolution
      - Dense Connections
      - Dropout
      - FBNet Block
      - Global Average Pooling
      - Softmax
      Tasks:
      - Image Classification
      Training Techniques:
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 8x GPUs
      ID: fbnetc_100
      LR: 0.1
      Epochs: 360
      Layers: 22
      Dropout: 0.2
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.0005
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L985
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 75.12%
        Top 5 Accuracy: 92.37%
  -->
  