Instructions to use hf-tiny-model-private/tiny-random-PerceiverForImageClassificationLearned 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-PerceiverForImageClassificationLearned 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-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-tiny-model-private/tiny-random-PerceiverForImageClassificationLearned") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-PerceiverForImageClassificationLearned") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6edd2e1d3443b19076ac52d2ecb86a08937b8da39bb3655c0c6409c9e0e2624
- Size of remote file:
- 1.48 MB
- SHA256:
- 3e7525c7851cea8c293e9f0308549a5d3a02aa527af26cf2a1746f8a69f44245
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