Instructions to use hf-internal-testing/tiny-random-EfficientFormerForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-EfficientFormerForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-EfficientFormerForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-EfficientFormerForImageClassification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f3ce14adfc5d97aa8d916f86ad2f79052b15958806eca8f943b4fb3119e4c5cc
- Size of remote file:
- 1.95 MB
- SHA256:
- 549dcdc71494e144048d3659514da843d7c2d19bc5de2e1877106f0ba21796f3
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