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:
- 981405694220e3c53e605498aad2c7a10f9a7e86a616a29e81c2a98c2442e83d
- Size of remote file:
- 187 kB
- SHA256:
- 0a7165d239a064cb4ff72bdc4b220aefc4cd3588eb9d1a557ae8bfb439224763
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