Instructions to use microsoft/swin-base-patch4-window7-224-in22k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swin-base-patch4-window7-224-in22k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swin-base-patch4-window7-224-in22k") 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("microsoft/swin-base-patch4-window7-224-in22k") model = AutoModelForImageClassification.from_pretrained("microsoft/swin-base-patch4-window7-224-in22k") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 241840246a683ecfc4363939fdc614db875dcf6e416f292da002a97713745447
- Size of remote file:
- 437 MB
- SHA256:
- dfd8ca7de71e90b8ec52b2437a0915e1b41c8bcd64ad8e190b537a8923ab4bc7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.