Instructions to use DerrickLegacy256/bee-audio-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use DerrickLegacy256/bee-audio-classifier with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("DerrickLegacy256/bee-audio-classifier", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
| { | |
| "sample_rate": 22050, | |
| "segment_length_sec": 5.0, | |
| "n_mfcc": 40, | |
| "hop_length": 512, | |
| "n_fft": 2048, | |
| "model_type": "bee_audio_classifier", | |
| "classes": [ | |
| "active_colony", | |
| "external_noise", | |
| "missing_queen", | |
| "pest_infestation", | |
| "pre_swarm", | |
| "queenbee_present", | |
| "swarming" | |
| ], | |
| "num_classes": 7, | |
| "n_features": 172, | |
| "best_model": "xgboost_model.pkl", | |
| "uploaded_files": [ | |
| "random_forest_model.pkl", | |
| "svm_rbf_model.pkl", | |
| "xgboost_model.pkl", | |
| "gradient_boosting_model.pkl", | |
| "bee_cnn_classifier.h5", | |
| "best_cnn.h5", | |
| "label_encoder.pkl", | |
| "cnn_label_encoder.pkl" | |
| ], | |
| "classical_metrics": { | |
| "Random Forest": { | |
| "accuracy": 0.9444, | |
| "f1_weighted": 0.9444, | |
| "f1_macro": 0.9444 | |
| }, | |
| "XGBoost": { | |
| "accuracy": 0.963, | |
| "f1_weighted": 0.963, | |
| "f1_macro": 0.963 | |
| }, | |
| "SVM (RBF)": { | |
| "accuracy": 0.8889, | |
| "f1_weighted": 0.888, | |
| "f1_macro": 0.8883 | |
| }, | |
| "Gradient Boosting": { | |
| "accuracy": 0.8519, | |
| "f1_weighted": 0.8514, | |
| "f1_macro": 0.851 | |
| } | |
| }, | |
| "cnn_metrics": {} | |
| } |