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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b47a9a556a04bba54ec1d345cc3cf6daae141b9c060e353132c27a125ac0530a
- Size of remote file:
- 262 kB
- SHA256:
- 215357c95fa850b8e78fcf2a5acda7037fc095a5cd778c55ae3688a147ce2589
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