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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06435e1fe74d8506e3b2c4ce202dbe30b96a7e0e162c3698435a70933f81cc48
- Size of remote file:
- 172 kB
- SHA256:
- 94cee956f91ef9cf5988ee1f4ff98f078e3e0f100a814b05a614dd8467fd805d
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