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:
- 58bbd26648f2decce81fa45c6fdeb1765844529668e89bb643478d0df9c7ecd9
- Size of remote file:
- 348 MB
- SHA256:
- 44653b6297ec653f74cad4f7a4afb0c5c6415b9cec22847ca4969a1bb76f03fb
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