Auto-deploy 2026-05-04 07:31 UTC

README.md

@@ -18,7 +18,7 @@ metrics:
 5-class audio classifier for bee colony health monitoring.
 Trained on segmented hive recordings using MFCC-based feature extraction.
 
-> Last updated: 2026-04-29 10:19 UTC
+> Last updated: 2026-05-04 07:31 UTC
 
 ## Classes
 
@@ -27,6 +27,8 @@ Trained on segmented hive recordings using MFCC-based feature extraction.
 | `active_colony` | — |
 | `external_noise` | — |
 | `missing_queen` | — |
+| `pest_infestation` | — |
+| `pre_swarm` | — |
 | `queenbee_present` | — |
 | `swarming` | — |
 
@@ -34,17 +36,17 @@ Trained on segmented hive recordings using MFCC-based feature extraction.
 
 | File | Description | Accuracy | F1 (weighted) |
 |---|---|---|---|
-| `random_forest_model.pkl` | Random Forest | 0.9966 | 0.9966 |
-| `svm_rbf_model.pkl` | SVM (RBF) | 0.9950 | 0.9950 |
-| `xgboost_model.pkl` | XGBoost | 0.9977 | 0.9977 |
-| `gradient_boosting_model.pkl` | Gradient Boosting **best** | 0.9983 | 0.9983 |
+| `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 (171 features per 5-second segment)
+## Feature extraction (172 features per 5-second segment)
 
 - 40 MFCCs × (mean + std) = 80
 - 40 delta-MFCCs × mean = 40
@@ -65,7 +67,7 @@ import joblib
 import librosa
 import numpy as np
 
-model = joblib.load("gradient_boosting_model.pkl")
+model = joblib.load("xgboost_model.pkl")
 le    = joblib.load("label_encoder.pkl")
 
 def extract_features(y, sr, n_mfcc=40,

bee_cnn_classifier.h5

@@ -1,3 +1,3 @@
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+size 10338648

best_cnn.h5

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cnn_label_encoder.pkl

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config.json

@@ -9,12 +9,14 @@
     "active_colony",
     "external_noise",
     "missing_queen",
+    "pest_infestation",
+    "pre_swarm",
     "queenbee_present",
     "swarming"
   ],
-  "num_classes": 5,
-  "n_features": 171,
-  "best_model": "gradient_boosting_model.pkl",
+  "num_classes": 7,
+  "n_features": 172,
+  "best_model": "xgboost_model.pkl",
   "uploaded_files": [
     "random_forest_model.pkl",
     "svm_rbf_model.pkl",
@@ -27,24 +29,24 @@
   ],
   "classical_metrics": {
     "Random Forest": {
-      "accuracy": 0.9966,
-      "f1_weighted": 0.9966,
-      "f1_macro": 0.9957
+      "accuracy": 0.9444,
+      "f1_weighted": 0.9444,
+      "f1_macro": 0.9444
     },
     "XGBoost": {
-      "accuracy": 0.9977,
-      "f1_weighted": 0.9977,
-      "f1_macro": 0.9973
+      "accuracy": 0.963,
+      "f1_weighted": 0.963,
+      "f1_macro": 0.963
     },
     "SVM (RBF)": {
-      "accuracy": 0.995,
-      "f1_weighted": 0.995,
-      "f1_macro": 0.9938
+      "accuracy": 0.8889,
+      "f1_weighted": 0.888,
+      "f1_macro": 0.8883
     },
     "Gradient Boosting": {
-      "accuracy": 0.9983,
-      "f1_weighted": 0.9983,
-      "f1_macro": 0.9979
+      "accuracy": 0.8519,
+      "f1_weighted": 0.8514,
+      "f1_macro": 0.851
     }
   },
   "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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