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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80c2ac3cdaa71ff29f270e2a6b0d312265a567d4c5d9c5d2f62f0e4a27ca1c35
- Size of remote file:
- 1.48 MB
- SHA256:
- c20cd5c8157dba9477f9c43160d663582312c6370bc329dbcaef496978e3359c
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