Instructions to use hf-internal-testing/tiny-random-PerceiverForImageClassificationConvProcessing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PerceiverForImageClassificationConvProcessing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/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-internal-testing/tiny-random-PerceiverForImageClassificationConvProcessing") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-PerceiverForImageClassificationConvProcessing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 86458239463f03593a1242d01f025d861bd94d3802bc136060e32a0b188fe3ae
- Size of remote file:
- 187 kB
- SHA256:
- 1c3991b737436acb61e04eaf31c966d077a1163c7ee7483e24ede508141414e9
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