Instructions to use Augusto777/swinv2-tiny-patch4-window8-256-ve-RXX40 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Augusto777/swinv2-tiny-patch4-window8-256-ve-RXX40 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Augusto777/swinv2-tiny-patch4-window8-256-ve-RXX40") 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("Augusto777/swinv2-tiny-patch4-window8-256-ve-RXX40") model = AutoModelForImageClassification.from_pretrained("Augusto777/swinv2-tiny-patch4-window8-256-ve-RXX40", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb0d2a14c20da9ab5fe4e64858c1014d9508b5b2ec63af9242341fec3e320160
- Size of remote file:
- 4.79 kB
- SHA256:
- b66ec0b73ca10028fdd6f4f0ebbaa77c03978c498f1e4f80bd3d4ea5c9eb2394
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