Instructions to use hf-tiny-model-private/tiny-random-PerceiverForImageClassificationConvProcessing 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-PerceiverForImageClassificationConvProcessing 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-PerceiverForImageClassificationConvProcessing") 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-PerceiverForImageClassificationConvProcessing") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-PerceiverForImageClassificationConvProcessing") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6953cf05f41063f5d6e0588bcc7d15e60d2afb85ab28bf438b7153f2288e757c
- Size of remote file:
- 172 kB
- SHA256:
- c64d2337c0e0a7cba37b95991d9cc7324fe87af6e607add21537327f1b7f529f
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