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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee9686f5e96252303a276a6e23121124ae8cfeeaf8de06eeda45313f0613b01c
- Size of remote file:
- 4.44 MB
- SHA256:
- af8e881d44a1fe379c8d7b0b99b3121cc88990af77777879f6df01612f2480c3
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