Instructions to use hf-tiny-model-private/tiny-random-EfficientNetForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-EfficientNetForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/tiny-random-EfficientNetForImageClassification") 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-tiny-model-private/tiny-random-EfficientNetForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-EfficientNetForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29d3d9fb943fec022be81bc99a7208d6b607d3dbdd77cc7a3fac693abc19ca27
- Size of remote file:
- 4.61 MB
- SHA256:
- edc54bbbd7520484bbb389c33ef095ddbd00d8e3fb7c98db74b4a3f217875512
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