Instructions to use hf-internal-testing/tiny-random-EfficientFormerForImageClassificationWithTeacher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-EfficientFormerForImageClassificationWithTeacher with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/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-internal-testing/tiny-random-EfficientFormerForImageClassificationWithTeacher", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24142f1f733d1b0d2c7fab48162e292ad15d8f40668a3377746603fb580c64f7
- Size of remote file:
- 1.84 MB
- SHA256:
- 38ebdab7bd7cfdae29cfe8e5dedaf57051c0e83e1987e8ca63efda179cdf165c
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