Update README.md

README.md

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 ---
 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)