Auto-deploy 2026-05-16 21:23 UTC

README.md

@@ -18,35 +18,25 @@ metrics:
 5-class audio classifier for bee colony health monitoring.
 Trained on segmented hive recordings using MFCC-based feature extraction.
 
-> Last updated: 2026-05-04 07:31 UTC
+> Last updated: 2026-05-16 21:23 UTC
 
 ## Classes
 
 | Label | Description |
 |---|---|
-| `active_colony` | — |
-| `external_noise` | — |
-| `missing_queen` | — |
-| `pest_infestation` | — |
-| `pre_swarm` | — |
-| `queenbee_present` | — |
-| `swarming` | — |
+| (classes not found) | — |
 
 ## Model performance
 
 | File | Description | Accuracy | F1 (weighted) |
 |---|---|---|---|
-| `random_forest_model.pkl` | Random Forest | 0.9444 | 0.9444 |
-| `svm_rbf_model.pkl` | SVM (RBF) | 0.8889 | 0.8880 |
-| `xgboost_model.pkl` | XGBoost **best** | 0.9630 | 0.9630 |
-| `gradient_boosting_model.pkl` | Gradient Boosting | 0.8519 | 0.8514 |
 | `bee_cnn_classifier.h5` | CNN (Mel Spectrogram) | — | — |
 | `best_cnn.h5` | CNN checkpoint | — | — |
 
 `label_encoder.pkl` is required by all classical ML models.
 `cnn_label_encoder.pkl` is required by the CNN models.
 
-## Feature extraction (172 features per 5-second segment)
+## Feature extraction (0 features per 5-second segment)
 
 - 40 MFCCs × (mean + std) = 80
 - 40 delta-MFCCs × mean = 40
@@ -67,7 +57,7 @@ import joblib
 import librosa
 import numpy as np
 
-model = joblib.load("xgboost_model.pkl")
+model = joblib.load("random_forest_model.pkl")
 le    = joblib.load("label_encoder.pkl")
 
 def extract_features(y, sr, n_mfcc=40,

bee_cnn_classifier.h5

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best_cnn.h5

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cnn_label_encoder.pkl

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config.json

@@ -5,18 +5,10 @@
   "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",
+  "classes": [],
+  "num_classes": 0,
+  "n_features": 0,
+  "best_model": "random_forest_model.pkl",
   "uploaded_files": [
     "random_forest_model.pkl",
     "svm_rbf_model.pkl",
@@ -27,27 +19,6 @@
     "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
-    }
-  },
+  "classical_metrics": {},
   "cnn_metrics": {}
 }

gradient_boosting_model.pkl

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label_encoder.pkl

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random_forest_model.pkl

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svm_rbf_model.pkl

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xgboost_model.pkl

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