Instructions to use HorcruxNo13/swin-tiny-patch4-window7-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HorcruxNo13/swin-tiny-patch4-window7-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="HorcruxNo13/swin-tiny-patch4-window7-224") 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("HorcruxNo13/swin-tiny-patch4-window7-224") model = AutoModelForImageClassification.from_pretrained("HorcruxNo13/swin-tiny-patch4-window7-224") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b522736756e7585f7fea6c4c5b30b5794d108b54751996b6da0a4154b6ee249
- Size of remote file:
- 110 MB
- SHA256:
- 82674c32aa3721464eb7fb9aa0d53a20834fe16e2e276661c5b6c65ceaf15dcc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.