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:
- b5161317ef817559f821b7233ad2b3ec6e70eba16b692456fc8e73f6d7aff1ec
- Size of remote file:
- 1.84 MB
- SHA256:
- 2124748eb8901447d7737dc00b45ee69d3300db5dd20f786437b1a1c25253926
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