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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe6167246c000b3d0a6397ae45ed8bf5760619bcdcd779488505010faa19baef
- Size of remote file:
- 5.16 MB
- SHA256:
- b3ec84fcb1cbb777cd75e0f3ffb002c0dd211beb8705beaddccd18789a5034dd
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