Instructions to use alfredcs/swin-cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alfredcs/swin-cifar10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="alfredcs/swin-cifar10") 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("alfredcs/swin-cifar10") model = AutoModelForImageClassification.from_pretrained("alfredcs/swin-cifar10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e5b1e1bbc5eb256e5312856a2b91a0abd8c9d812ccb2c24cc6cb70674fbc167c
- Size of remote file:
- 437 MB
- SHA256:
- 3bdbd5980d97da4b4fd7a6f3b2fef970936c2cbf22667ab117a7bb6a0fb1a87d
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