Instructions to use hf-tiny-model-private/tiny-random-RegNetForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-RegNetForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/tiny-random-RegNetForImageClassification") 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("hf-tiny-model-private/tiny-random-RegNetForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-RegNetForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a5e47a1c7cb2be237ce5786732073af8b144569c480b8a4c31c7d173cf6f6482
- Size of remote file:
- 242 kB
- SHA256:
- d5851d84a20366162f99d9c1489d14c2be79d07892d9c004cc58a48a3a9a486f
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