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:
- 212a013875168af71abdef8638d1efdda0e302f65d135a4099829586e2e4598d
- Size of remote file:
- 5.26 MB
- SHA256:
- cb7b9b7f9b5ebee05c706474c9418859cc29828fab682d1b6850f73988f26eeb
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