Instructions to use microsoft/swinv2-tiny-patch4-window16-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swinv2-tiny-patch4-window16-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swinv2-tiny-patch4-window16-256") 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/swinv2-tiny-patch4-window16-256") model = AutoModelForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window16-256") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c6ca0e9affe6c3ed2c2d1ec7a51e97759d2e83f9f3012ba28bfe2862d746a2d
- Size of remote file:
- 113 MB
- SHA256:
- 6f38b148e61acaee12bd7e9592377177cc83c1eec18b1ec119aeaf0852a79843
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.