Instructions to use frncscp/dinotron with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frncscp/dinotron with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="frncscp/dinotron") 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("frncscp/dinotron") model = AutoModelForImageClassification.from_pretrained("frncscp/dinotron", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f0e339ea714c549db005956d48efb922d2af4d6e87050a4af47076dc7274d10
- Size of remote file:
- 346 MB
- SHA256:
- 2374a0caa40a02f60bb643c5a200c8de765e7f5ede456702f38a2e5a275db1c2
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