Instructions to use microsoft/beit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/beit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/beit-base-patch16-224") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("microsoft/beit-base-patch16-224") - Inference
- Notebooks
- Google Colab
- Kaggle
Fix typo
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README.md
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@@ -38,7 +38,7 @@ import requests
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url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
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image = Image.open(requests.get(url, stream=True).raw)
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feature_extractor = BEiTFeatureExtractor.from_pretrained('microsoft/beit-base-patch16-224')
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model = BEiTForImageClassification.from_pretrained('
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inputs = feature_extractor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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logits = outputs.logits
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url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
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image = Image.open(requests.get(url, stream=True).raw)
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feature_extractor = BEiTFeatureExtractor.from_pretrained('microsoft/beit-base-patch16-224')
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model = BEiTForImageClassification.from_pretrained('microsoft/beit-base-patch16-224')
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inputs = feature_extractor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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logits = outputs.logits
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