From c697c92e0e0530f6563c78d1715e7cf043d22cfd Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Mon, 13 Sep 2021 19:52:27 +0000 Subject: [PATCH] Update model card --- README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 4f55b12..72a6aaf 100644 --- a/README.md +++ b/README.md @@ -34,10 +34,13 @@ Here is how to use this model to classify an image of the COCO 2017 dataset into from transformers import ViTFeatureExtractor, ViTForImageClassification 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 = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224') model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224') + inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits @@ -46,7 +49,7 @@ predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` -Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change. +For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/vit.html#). ## Training data