best_models_summary.csv

dataset,metric,model_name,accuracy,precision,recall,f1_score,model_path,model_type
satellite,accuracy,EfficientNetB6 + Softmax ,0.95,0.96,0.94,0.95,efficientnetb6_satellite_best_model.h5,h5
satellite,precision,InceptionResNetv2 + Random Forest,0.8562,9.8393,0.7921,0.815,inceptionresnetv2_RandomForest_satellite.joblib,joblib
satellite,recall,InceptionResNetv2 + MLP,0.8944,0.7991,0.9831,0.8816,inceptionresnetv2_MLP_satellite_binary.keras,keras
satellite,f1_score,EfficientNetB6 + Softmax,0.95,0.96,0.94,0.95,efficientnetb6_satellite_best_model.h5,h5
uav,accuracy,ConvNextSmall + SVM,0.9893,0.9024,0.893,0.8813,convnextsmall_SVM_uav.joblib,joblib
uav,precision,ResNet101 + KNN,0.9827,0.9826,0.9827,0.9826,resnet101_KNN_uav.joblib,joblib
uav,recall,ResNet101 + KNN,0.9827,0.9826,0.9827,0.9826,resnet101_KNN_uav.joblib,joblib
uav,f1_score,ResNet101 + KNN,0.9827,0.9826,0.9827,0.9826,resnet101_KNN_uav.joblib,joblib