Edit model card


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

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
Hosted inference API

Unable to determine this model’s pipeline type. Check the docs .