Instructions to use ViTAMIn-O/PDLO_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViTAMIn-O/PDLO_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ViTAMIn-O/PDLO_classifier") 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("ViTAMIn-O/PDLO_classifier") model = AutoModelForImageClassification.from_pretrained("ViTAMIn-O/PDLO_classifier") - Notebooks
- Google Colab
- Kaggle
| {"Accuracy": 0.925, "Balanced_Accuracy": 0.925, "MCC": 0.8510644963469901, "Cohen_Kappa": 0.85, "Brier_Score": 0.04958225747752335, "Global_AUROC": 0.988125, "satisfactory_AUROC": 0.988125, "unsatisfactory_AUROC": 0.988125, "satisfactory_Precision": 0.9047619047619048, "satisfactory_Recall": 0.95, "satisfactory_Specificity": 0.9, "satisfactory_NPV": 0.9473684210526315, "satisfactory_F1": 0.9268292682926829, "unsatisfactory_Precision": 0.9473684210526315, "unsatisfactory_Recall": 0.9, "unsatisfactory_Specificity": 0.95, "unsatisfactory_NPV": 0.9047619047619048, "unsatisfactory_F1": 0.9230769230769231, "Macro_Precision": 0.9260651629072681, "Macro_Recall": 0.925, "Macro_Specificity": 0.925, "Macro_F1": 0.924953095684803, "Seed": 42} |