Instructions to use hf-tiny-model-private/tiny-random-CvtForImageClassification 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-CvtForImageClassification 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-CvtForImageClassification") 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-CvtForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-CvtForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e47a54a7321bbd7ea210c6612d93d5cd6cb32c9d6b64ed0c3454fca65b7ebec
- Size of remote file:
- 5.67 MB
- SHA256:
- 35997f7c148a7a1abe29ac5b1479cb2d310f8cafef02d8f4043a2493d43ca3f8
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