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:
- 30e7055ff30317c8aff19165fcf5a2a88caf5389a16dd53f509b4d38645ac20b
- Size of remote file:
- 1.5 MB
- SHA256:
- c053380fea7ceaa2326f27c88ca9729955507021c1e63fee1e09abaee5faacaf
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