Instructions to use nenzilea/car-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nenzilea/car-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nenzilea/car-classification") 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("nenzilea/car-classification") model = AutoModelForImageClassification.from_pretrained("nenzilea/car-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 590888f9d5d6b4c09efe68a1339c7e32e02118015166c30b64a7ada91b5d0663
- Size of remote file:
- 5.27 kB
- SHA256:
- 667e91de5f9f0e7f040635a9365c3bd5b13998298c8ace8948fea3d9269304fd
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