Instructions to use hf-tiny-model-private/tiny-random-NatForImageClassification 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-NatForImageClassification 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-NatForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-NatForImageClassification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0627720ebe6f84e10edf7065ed1500c94b07f90c155cf276102bf3b5da9b0664
- Size of remote file:
- 342 kB
- SHA256:
- d5613e0f9f6c5cb94d42fa3d9b1dd2d06b4cbd362ab598d0edb20b5ae95a1947
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