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
| { | |
| "epoch": 15.0, | |
| "total_flos": 2.6189408941190627e+19, | |
| "train_loss": 0.0015452209050444202, | |
| "train_runtime": 893.2543, | |
| "train_samples_per_second": 106.868, | |
| "train_steps_per_second": 3.342 | |
| } |