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:
- 9c6c2821767d30e1ed860f5d13f673ff9be57d3a7d1b4eb08d8ede055b7d4840
- Size of remote file:
- 242 kB
- SHA256:
- 1252e2f8de6c54a76d9e6a6258d3c61b8f3bfcb394657cfdd655d3590c482c30
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