Instructions to use hf-internal-testing/tiny-random-PerceiverForImageClassificationLearned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PerceiverForImageClassificationLearned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-PerceiverForImageClassificationLearned") 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-PerceiverForImageClassificationLearned") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-PerceiverForImageClassificationLearned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d81e1901ca7b4a6bec23a76feb2f189160bedbd59c4b95df1d4b049fce43acc
- Size of remote file:
- 1.6 MB
- SHA256:
- 48e87cebf9d0afdec4e1eb75e0409bbcbad821934c0eb5c2a27a48a95e4acdf8
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