Instructions to use hf-internal-testing/tiny-random-EfficientNetForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-EfficientNetForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/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-internal-testing/tiny-random-EfficientNetForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-EfficientNetForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d61f5471dca81cf1e75ef86341cb7b94ff4e4939b8d8c2e81b6d04507fb0eb22
- Size of remote file:
- 4.61 MB
- SHA256:
- 56150bb9bd3dce98ddd0773ed016478de3193e32b833ecb10bbacdd9b2ce8929
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