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:
- 02a117b7c53ac1e5207f1f53ccfb6153c6da3e19dfb07a02cf1fe0269a0d9c26
- Size of remote file:
- 122 kB
- SHA256:
- a281f24ec903fa189a108dbf893eaf560949aadf1c4032c7c39135fb3bee3b79
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