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:
- d7741382fee0da45330ad9e196e705f368fb3b31254ccc5b2f15e9f7675532d3
- Size of remote file:
- 3.31 kB
- SHA256:
- 58624c8f6bc2840410d448aac9d83680f2fdbc64d77c1b84aee738bce301d5b2
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