Instructions to use hf-tiny-model-private/tiny-random-LevitForImageClassificationWithTeacher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-LevitForImageClassificationWithTeacher with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/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-tiny-model-private/tiny-random-LevitForImageClassificationWithTeacher") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-LevitForImageClassificationWithTeacher") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7873d2cefc12e7cf9f4718842916f3ff7ec9880aba11fb18f4137d74317e5023
- Size of remote file:
- 28.2 MB
- SHA256:
- 7e5aa6362a223c0b58df689a9268209288dccf28aef5380addc582ca6cad168e
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