Instructions to use hf-internal-testing/tiny-random-LevitForImageClassificationWithTeacher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LevitForImageClassificationWithTeacher with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-LevitForImageClassificationWithTeacher") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-LevitForImageClassificationWithTeacher") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-LevitForImageClassificationWithTeacher", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e79edeb771867bab670b37575281b6a11c6ed46e52c22dc0d2176cd16ddcbbf4
- Size of remote file:
- 28.1 MB
- SHA256:
- 1dca4c1fd6ff5aa3227e5b6d7c3fe86f4dfeeb7f9c0c05b896a8b757e02b86ea
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