readme: ---
  language: en
  tags:
  - deit
  license: apache-2.0
  ---
  
  # DeiT
  
  ## Model description
  
   DeiT proposed in [this paper](https://arxiv.org/abs/2012.12877) are more efficiently trained transformers for image classification, requiring far less data and far less computing resources compared to the original ViT models.
  
  ## Original implementation
  
  Follow [this link](https://huggingface.co/docs/transformers/main/en/model_doc/deit#deit) to see the original implementation.
  
  ## How to use
  
  ```{python}
  from onnxruntime import InferenceSession
  from transformers import DeiTFeatureExtractor, DeiTForImageClassification
  import torch
  from PIL import Image
  import requests
  
  torch.manual_seed(3)
  url = "http://images.cocodataset.org/val2017/000000039769.jpg"
  image = Image.open(requests.get(url, stream=True).raw)
  
  feature_extractor = DeiTFeatureExtractor.from_pretrained("facebook/deit-base-distilled-patch16-224")
  inputs = feature_extractor(images=image, return_tensors="np")
  session = InferenceSession("onnx/model.onnx")
  
  # ONNX Runtime expects NumPy arrays as input
  outputs = session.run(output_names=["last_hidden_state"], input_feed=dict(inputs))
  ```