Instructions to use hf-internal-testing/tiny-random-NatForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-NatForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/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-internal-testing/tiny-random-NatForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 64f7fba866ae1d1c43d773f853f457ecf9aa17215145d90e1b60f6f1e5148285
- Size of remote file:
- 342 kB
- SHA256:
- de8868fb1d7dbf91386e9df3c0d3daebf8b2ddb919963acffc30c2f02b90f2a8
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