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:
- 58cfc5e2820b65fc7c528b3cbd69faf83b1dbc28c53c8a15de4a394302909973
- Size of remote file:
- 1.84 MB
- SHA256:
- 5dce176e7c3852c27d1007b357d4b83081dae5a24af682d55ad44bd2f341456c
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