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---
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))
``` |