Instructions to use hf-internal-testing/tiny-random-SwinForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SwinForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-SwinForImageClassification") 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-SwinForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-SwinForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bfbdf388642b5ef549da8cccdf5a433c5b8c60686f4013624c0e6c733f14ed7b
- Size of remote file:
- 678 kB
- SHA256:
- 6c08242c8d446c997e0397b4774dfe1b6df06c371f55e58805de3837a9f2570b
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