From 9da301148150e37e533abef672062fa49f6bda4f Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Mon, 27 Feb 2023 15:08:16 +0000 Subject: [PATCH] Update README.md --- README.md | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 2f5cc73..6c15d36 100644 --- a/README.md +++ b/README.md @@ -33,14 +33,17 @@ fine-tuned versions on a task that interests you. 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 BeitFeatureExtractor, BeitForImageClassification +from transformers import BeitImageProcessor, BeitForImageClassification 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 = BeitFeatureExtractor.from_pretrained('microsoft/beit-base-patch16-224-pt22k-ft22k') + +processor = BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224-pt22k-ft22k') model = BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224-pt22k-ft22k') -inputs = feature_extractor(images=image, return_tensors="pt") + +inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 21,841 ImageNet-22k classes