| | --- |
| | license: apache-2.0 |
| | tags: |
| | - vision |
| | - image-classification |
| | datasets: |
| | - imagenet-1k |
| | widget: |
| | - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg |
| | example_title: Tiger |
| | - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg |
| | example_title: Teapot |
| | - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg |
| | example_title: Palace |
| | --- |
| | |
| | # LeViT |
| |
|
| | LeViT-128S model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference |
| | ](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT). |
| |
|
| | Disclaimer: The team releasing LeViT did not write a model card for this model so this model card has been written by the Hugging Face team. |
| |
|
| | ## Usage |
| |
|
| | Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: |
| |
|
| | ```python |
| | from transformers import LevitFeatureExtractor, LevitForImageClassificationWithTeacher |
| | 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 = LevitFeatureExtractor.from_pretrained('facebook/levit-128S') |
| | model = LevitForImageClassificationWithTeacher.from_pretrained('facebook/levit-128S') |
| | |
| | inputs = feature_extractor(images=image, return_tensors="pt") |
| | outputs = model(**inputs) |
| | logits = outputs.logits |
| | # model predicts one of the 1000 ImageNet classes |
| | predicted_class_idx = logits.argmax(-1).item() |
| | print("Predicted class:", model.config.id2label[predicted_class_idx]) |
| | ``` |
