OWG/DeiT · Hugging Face DeiT Model description DeiT proposed in this paper 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 to see the original implementation. How to use 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)) Downloads last month - Downloads are not tracked for this model. How to track Inference Providers NEW This model isn't deployed by any Inference Provider. 🙋 Ask for provider support Paper for OWG/DeiT Paper • 2012.12877 • Published Dec 23, 2020 • 2