Instructions to use WinKawaks/vit-tiny-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WinKawaks/vit-tiny-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="WinKawaks/vit-tiny-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("WinKawaks/vit-tiny-patch16-224") model = AutoModelForImageClassification.from_pretrained("WinKawaks/vit-tiny-patch16-224") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a65f8f346a753dcdf8405cb1b90db05d09471ffca3901f75de0b61d38627b84f
- Size of remote file:
- 22.9 MB
- SHA256:
- a9146f3ee7cf6de399190132627e4aef1aeddd13692f5b333f24629dd36c37b0
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