ATAS Model Weights
Three trained model files for the ATAS (Aerial Threat Assessment System) pipeline.
Models
1. Aircraft Classifier
- File:
aircraft_classifier/atas_final_fine_tuned_aircraft_classifier_model.keras - Architecture: EfficientNetV2-L + custom classification head
- Dataset: ~12k images, 101 aircraft classes
- Top-1 Accuracy: 78.08% | Top-5 Accuracy: 92.02%
2. ETA Regressor
- File:
eta/atas_final_eta_regressor_model.joblib - Architecture: XGBoost Regressor (Optuna-tuned, ~944 trials)
- Task: Predicts time-to-impact in seconds
- R²: 0.9939 | MAE: 0.4552s
3. Hit Classifier
- File:
hit/atas_final_hit_classifier_model.joblib - Architecture: XGBoost Classifier
- Task: Predicts missile hit probability after evasion
- Recall: 0.9966 | F1: 0.9968 | ROC-AUC: 0.9999
Usage
These models are used together in the ATAS pipeline. See the live demo: 👉 https://huggingface.co/spaces/Eakempreet/ATAS
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