README.md

metadata

license: mit
tags:
  - computer-vision
  - classification
  - xgboost
  - tensorflow
  - military
  - pipeline

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