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", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f07b2ab535f0c9846e9739490f8d872fbec8a0ad93882e90cce0507378fbf7c
- Size of remote file:
- 1.84 MB
- SHA256:
- 4c5c9074859b84f728088148f0f36bfef3cee148fb0d5839a3b01338babb08e6
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