Instructions to use hf-internal-testing/tiny-random-LevitForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LevitForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-LevitForImageClassification") 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-LevitForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-LevitForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b09f69de169bf80f239ddcb9556563d1e383f61d74340b705874537445242386
- Size of remote file:
- 28.1 MB
- SHA256:
- 8d9898f1cd239d975da171a97f7cb2ec41d7c2c067673b6b0c1370f84a929c77
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