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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a72aa74735d5c3c0be20283eb7a09018e5b3c9998b7941a8d1968a5c6333fe5c
- Size of remote file:
- 1.95 MB
- SHA256:
- 692b7effcb51788d22d2e33c5703487d183b2b0ffb900365784d538610cc44c9
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