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