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:
- f41cb4a9092179934172f1956da71d5ba0144b3dc1959deb6b2e6550604da93a
- Size of remote file:
- 4.54 MB
- SHA256:
- be5776e140d4ce8e2347733a0fd3f948cc1c121263e66af5dd8b38f9020ba2c2
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