added readme

README.md

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 ---
-license: mit
+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))
+```

onnx/model.onnx

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