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:
- 0fcb4a239682360fbd29ceb6b609dac03b44826bf90b9a80d3db0debf3c309c9
- Size of remote file:
- 1.21 GB
- SHA256:
- 6c6eca927a142098f2087af163a91dbaf495ee2c2a84ca80b8627f7e44399c23
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