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:
- b812ff2d0f8336f94dd5fafac2604e57d8e25b3d5e110ef2c6a8a4a70345406f
- Size of remote file:
- 324 kB
- SHA256:
- e72057974ab4dcad7097681c2339d9985430294d8c9111ebb56eab046aa67635
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