Instructions to use RobertoSonic/swinv2-base-patch4-window8-256-dmae-humeda-DAV15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RobertoSonic/swinv2-base-patch4-window8-256-dmae-humeda-DAV15 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="RobertoSonic/swinv2-base-patch4-window8-256-dmae-humeda-DAV15") 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("RobertoSonic/swinv2-base-patch4-window8-256-dmae-humeda-DAV15") model = AutoModelForImageClassification.from_pretrained("RobertoSonic/swinv2-base-patch4-window8-256-dmae-humeda-DAV15") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59d232e905d47725c97946bfadcd0270d2a4ce9fcf13f2e9e5c08e1bbb109351
- Size of remote file:
- 5.43 kB
- SHA256:
- 37cd53091063501cb234d7d1d800ea7c0c6c643e4c62e49923c91da29a16079e
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