Instructions to use pamixsun/swinv2_tiny_for_glaucoma_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pamixsun/swinv2_tiny_for_glaucoma_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pamixsun/swinv2_tiny_for_glaucoma_classification") 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("pamixsun/swinv2_tiny_for_glaucoma_classification") model = AutoModelForImageClassification.from_pretrained("pamixsun/swinv2_tiny_for_glaucoma_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc570871ab042fa1e5083521e8075f7c95e45466012a3ecb64756b12870a24a9
- Size of remote file:
- 110 MB
- SHA256:
- 95cfbbc74cee50346361f456d2a43293ac15c91d575e53999ba268659ab271ea
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