Instructions to use hf-tiny-model-private/tiny-random-PerceiverForImageClassificationFourier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-PerceiverForImageClassificationFourier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/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-tiny-model-private/tiny-random-PerceiverForImageClassificationFourier") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-PerceiverForImageClassificationFourier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 33a1cad02c375dc0f2b630167cd0aa418fce2492600ef15fea21a45fd803a4cc
- Size of remote file:
- 122 kB
- SHA256:
- 772643b538e301f7a9287e499e1214a0920283e80d64202a48b7196b8162df9e
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