Instructions to use TirathP/vit-base-patch16-224-finetuned-customData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TirathP/vit-base-patch16-224-finetuned-customData with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="TirathP/vit-base-patch16-224-finetuned-customData") 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("TirathP/vit-base-patch16-224-finetuned-customData") model = AutoModelForImageClassification.from_pretrained("TirathP/vit-base-patch16-224-finetuned-customData", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4326e8d60aa8235766f0f9ff84f5cca095cc50c38f1915831294fad0579d7d69
- Size of remote file:
- 343 MB
- SHA256:
- dac4f14e18378e32a24ab5ed6d669224d6395a08b46078745ecd2470a74927ea
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