Instructions to use hf-tiny-model-private/tiny-random-EfficientFormerForImageClassificationWithTeacher 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-EfficientFormerForImageClassificationWithTeacher 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-EfficientFormerForImageClassificationWithTeacher") 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-EfficientFormerForImageClassificationWithTeacher", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15a69b03e1b582fb4b0570d117ce7f3431b153749b805175f8d78a93115d8080
- Size of remote file:
- 45.8 MB
- SHA256:
- 4345ac68cf8ede6f03a3f525893bc47272c580717648440262d89d4e68eb6a05
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