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:
- 541ad99f44580fa60f91fb41dc6a7da82d70b162b09b440a37eb9dc1fd6b9b9b
- Size of remote file:
- 28.3 MB
- SHA256:
- 266b5ec09e69d4b8bb5bbd5b2a6a13805be5441c8aec64ddf867c6bccb7d452a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.