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")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ViTAMIn-O/PDLO_classifier") model = AutoModelForImageClassification.from_pretrained("ViTAMIn-O/PDLO_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download results_seed_46.json from ViTAMIn-O/PDLO_classifier: direct link, hf CLI and curl.
- Browser
- Download file 713 Bytes
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https://huggingface.co/ViTAMIn-O/PDLO_classifier/resolve/main/results_seed_46.json
- Command line
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hf download hf://ViTAMIn-O/PDLO_classifier/results_seed_46.json
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curl -L -o results_seed_46.json https://huggingface.co/ViTAMIn-O/PDLO_classifier/resolve/main/results_seed_46.json
713 Bytes
| {"Accuracy": 0.9625, "Balanced_Accuracy": 0.9625, "MCC": 0.9276125895432153, "Cohen_Kappa": 0.925, "Brier_Score": 0.03875666580073609, "Global_AUROC": 0.99375, "satisfactory_AUROC": 0.99375, "unsatisfactory_AUROC": 0.99375, "satisfactory_Precision": 0.9302325581395349, "satisfactory_Recall": 1.0, "satisfactory_Specificity": 0.925, "satisfactory_NPV": 1.0, "satisfactory_F1": 0.963855421686747, "unsatisfactory_Precision": 1.0, "unsatisfactory_Recall": 0.925, "unsatisfactory_Specificity": 1.0, "unsatisfactory_NPV": 0.9302325581395349, "unsatisfactory_F1": 0.961038961038961, "Macro_Precision": 0.9651162790697674, "Macro_Recall": 0.9625, "Macro_Specificity": 0.9625, "Macro_F1": 0.9624471913628541, "Seed": 46} |