Instructions to use hf-tiny-model-private/tiny-random-EfficientFormerForImageClassification 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-EfficientFormerForImageClassification 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-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-tiny-model-private/tiny-random-EfficientFormerForImageClassification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0824a50448c6144418cee66d375823f48c39e83cc4ebd32ca3fcc188e5e0120d
- Size of remote file:
- 45.8 MB
- SHA256:
- e215b768832e200c8613235448744c0f5ccf2de96aaeb3bac5554a5657431ea9
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