feat: implement comprehensive land cover classification pipeline with model benchmarking and experiment logging

log_mobilenet.txt

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+🚀 BẮT ĐẦU HUẤN LUYỆN MÔ HÌNH MOBILENET-LRASPP (GPU & CACHE)
+Initializing FeatureExtractor (mode=extended)...
+📦 Đang load cache: training_data_507bd2ba4ec0d3fe107839cbf73a7a7d.joblib...
+✅ Loaded 632 samples từ cache!
+   ⚡ Đã bỏ qua download위성 data (tiết kiệm thời gian)
+[CACHE HIT] Using cached dataset with 632 samples
+Training MOBILENET-LRASPP model...
+Building MobileNetV3 + LR-ASPP model on cuda...
+[MOBILENET] Class distribution: [ 48  89   3  86  74  38 117  50]
+[MOBILENET] Class weights: [0.37418982 0.20181024 5.98703525 0.20885013 0.24271772 0.47266082
+ 0.15351377 0.35922223]
+Training MobileNetV3 + LR-ASPP model with PyTorch...
+MobileNet Epoch 5/25, Train Loss: 1.0614, Val Loss: 1.3584, Val Acc: 40.16%, LR: 0.000800
+[MOBILENET] Epoch 5/25 - Train Loss: 1.0614, Val Loss: 1.3584, Val Acc: 40.16%
+MobileNet Epoch 10/25, Train Loss: 0.9054, Val Loss: 0.8582, Val Acc: 59.06%, LR: 0.000800
+[MOBILENET] Epoch 10/25 - Train Loss: 0.9054, Val Loss: 0.8582, Val Acc: 59.06%
+MobileNet Epoch 15/25, Train Loss: 0.7728, Val Loss: 0.8231, Val Acc: 61.42%, LR: 0.000800
+[MOBILENET] Epoch 15/25 - Train Loss: 0.7728, Val Loss: 0.8231, Val Acc: 61.42%
+MobileNet Epoch 20/25, Train Loss: 0.7125, Val Loss: 0.8783, Val Acc: 59.84%, LR: 0.000400
+[MOBILENET] Epoch 20/25 - Train Loss: 0.7125, Val Loss: 0.8783, Val Acc: 59.84%
+[MOBILENET] Early stopping at epoch 24 (best val loss: 0.8029)
+MobileNet early stopped at epoch 24
+Evaluating model...
+Generating classification report...
+Saving model...
+[MODEL MANAGER] Saving model to: model_train/model_mobilenet-lraspp_auto.joblib
+[MODEL MANAGER] Saving metadata to: model_train/model_mobilenet-lraspp_auto_info.json
+[MODEL MANAGER] Model saved successfully!
+Training complete!
+✅ Hoàn thành! Accuracy: 0.5669