Instructions to use google/vit-large-patch32-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vit-large-patch32-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/vit-large-patch32-384") 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("google/vit-large-patch32-384") model = AutoModelForImageClassification.from_pretrained("google/vit-large-patch32-384") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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logits = outputs.logits
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# model predicts one of the 1000 ImageNet classes
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predicted_class_idx = logits.argmax(-1).item()
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print("Predicted class:", model.config.
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```
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Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
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logits = outputs.logits
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# model predicts one of the 1000 ImageNet classes
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predicted_class_idx = logits.argmax(-1).item()
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print("Predicted class:", model.config.id2label[predicted_class_idx])
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```
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Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
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