Instructions to use hf-internal-testing/tiny-random-RegNetForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RegNetForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/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-internal-testing/tiny-random-RegNetForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-RegNetForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b9601f558451c2acf2d27f108e3b4ec2400dd0af1d0bc35fb53a9947b052cc8
- Size of remote file:
- 216 kB
- SHA256:
- 730e1731ece2ba404c8b5752b9ac742d6cc016bdf2169fedc0ca27aa3e2ee590
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