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

# ATAS Model Weights

Three trained model files for the [ATAS (Aerial Threat Assessment System)](https://huggingface.co/spaces/Eakempreet/ATAS) 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](https://huggingface.co/spaces/Eakempreet/ATAS)