Instructions to use DHEIVER/finetuned-BreastCancer-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DHEIVER/finetuned-BreastCancer-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DHEIVER/finetuned-BreastCancer-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("DHEIVER/finetuned-BreastCancer-Classification") model = AutoModelForImageClassification.from_pretrained("DHEIVER/finetuned-BreastCancer-Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97cc5b8e246386ebfa68065583d5220dc068f46e91a19a423672477673574315
- Size of remote file:
- 4.16 kB
- SHA256:
- ed7e174832b603466fc33f3238fc41805793c043e3e32bcc34e79490831c0ea3
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