Instructions to use hf-internal-testing/tiny-random-EfficientNetModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-EfficientNetModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-EfficientNetModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-EfficientNetModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-EfficientNetModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d91bfc5c1526c919f4af7ff0e2b6edcb66b32aef853d7116b10906499cf3c96
- Size of remote file:
- 4.51 MB
- SHA256:
- bd4374f2ce04b043bb6bcbf82e038654b620f26ebcc640ff41085fb6b4ebf0e9
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