Instructions to use glopez/cifar-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use glopez/cifar-10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="glopez/cifar-10") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("glopez/cifar-10") model = AutoModelForImageClassification.from_pretrained("glopez/cifar-10") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 214fadefd5af082bcd37e72a242ed284e673d0da722b6f0a2a9f9f8df5ca0f58
- Size of remote file:
- 3.38 kB
- SHA256:
- 85de8c2443edd63bab0d98c36637cbba93f17087b2ab26eec8dbc45cf21313eb
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