Instructions to use MatanBT/swin-tiny-patch4-window7-224-cifar100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatanBT/swin-tiny-patch4-window7-224-cifar100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MatanBT/swin-tiny-patch4-window7-224-cifar100") 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("MatanBT/swin-tiny-patch4-window7-224-cifar100") model = AutoModelForImageClassification.from_pretrained("MatanBT/swin-tiny-patch4-window7-224-cifar100") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0346ee7187045ee745a3119bbe1a052562ccf727aaf7b2f59df21ffdea7d7e3c
- Size of remote file:
- 4.86 kB
- SHA256:
- c6479ac9ab2b29a1bb1a3dae6b2fd644f8fad87b65a000ac49c71c2a34351453
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