Instructions to use hf-internal-testing/tiny-random-CvtForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-CvtForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/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-internal-testing/tiny-random-CvtForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-CvtForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 31e8cccdfdc3f475271ed53e6e495fcee8a6a3dd035ecc2d7971fb07cc3fa688
- Size of remote file:
- 5.77 MB
- SHA256:
- c8366035a514f1615b97304e44f20c242ae1dd216e10666df6ce8df6b58672fc
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