Instructions to use hf-internal-testing/tiny-random-PerceiverForImageClassificationFourier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PerceiverForImageClassificationFourier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-PerceiverForImageClassificationFourier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoTokenizer, AutoModelForImageClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-PerceiverForImageClassificationFourier") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-PerceiverForImageClassificationFourier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 800d06d174d1198ee761f764283fa2ed8d52ead0cb52056341a87f5ef17a8a76
- Size of remote file:
- 136 kB
- SHA256:
- 3fb2d53c73da2bf44b7310a3a8d3e6bea61db1f8b509a8b1478b63d288b4d548
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