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:
- 2bb71edb0e6f4dcb6f7f4e31461d58d0e358b21bd14fc630e0ec21bb26187ba2
- Size of remote file:
- 1.84 MB
- SHA256:
- 1d94f147ef59d5afee516e46eaee1cc8f09fecb2a0c64e31c3e7cb35d7a1ce8c
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