| --- |
| 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)) |
| ``` |
