Upload README.md with huggingface_hub

b1af411 verified 11 months ago

4.96 kB

language: en
license: mit
tags:
  - tensorflow
  - combat-detection
  - impact-classification
  - military-ai
  - sensor-fusion
datasets:
  - synthetic
metrics:
  - accuracy
model-index:
  - name: Guardian-Shield Combat Detector
    results:
      - task:
          type: impact-classification
        metrics:
          - type: accuracy
            value: 0.9546
            name: Test Accuracy

Guardian-Shield Combat Impact Detector

Model Description

AI-powered combat impact classification system achieving 95.46% accuracy on 6 impact classes:

💥 Blast
🔫 Gunshot
🎯 Artillery
🚗 Vehicle Crash
🤕 Fall
✅ Normal

Model Architecture

ResNet-inspired Deep Learning Model

Dilated convolutions (rates: 1, 2, 4)
Multi-head attention (16 heads)
Squeeze-and-Excitation blocks
Dual pooling (Average + Max)
Parameters: ~8.4M
Format: TensorFlow Lite (optimized for edge deployment)

Intended Use

Primary Use: Real-time combat impact detection on wearable military devices

Input: 13-channel sensor data (200 timesteps)

3-axis accelerometer
3-axis gyroscope
3-axis magnetometer
Heart rate, SpO2, breathing rate, temperature

Output:

Impact classification (6 classes)
Severity score (0-1)

Training Data

Size: 60,000 synthetic samples
Method: Physics-based signal modeling with physiological responses
Split: 70/15/15 (train/val/test)
Augmentation: Time shifting, scaling, noise injection

Performance Metrics

Metric	Value
Test Accuracy	95.46%
Validation Accuracy	~95.8%
Inference Time	<10ms (GPU T4)
Model Size	6.64 MB (TFLite)

Usage Example

import tensorflow as tf
import numpy as np

# Load model
interpreter = tf.lite.Interpreter('impact_classifier.tflite')
interpreter.allocate_tensors()

# Load normalization parameters
norm = np.load('norm.npz')
mean, std = norm['mean'], norm['std']

# Prepare sensor data (shape: [1, 200, 13])
sensor_data = your_sensor_reading  # Your 13-channel sensor data
normalized = (sensor_data - mean) / std

# Run inference
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

interpreter.set_tensor(input_details[0]['index'], normalized)
interpreter.invoke()

# Get predictions
impact_type = interpreter.get_tensor(output_details[0]['index'])
severity = interpreter.get_tensor(output_details[1]['index'])

# Decode results
classes = ['blast', 'gunshot', 'artillery', 'vehicle_crash', 'fall', 'normal']
predicted_class = classes[impact_type.argmax()]
severity_score = severity[0][0]

print(f"Impact: {predicted_class}")
print(f"Severity: {severity_score:.2f}")

Files in This Repository

impact_classifier.tflite - Optimized TensorFlow Lite model (6.64 MB)
norm.npz - Normalization parameters (mean and std)
README.md - This file

Deployment

Supported Platforms:

✅ Raspberry Pi (TensorFlow Lite)
✅ Android devices (TFLite Android SDK)
✅ Embedded systems (ARM Cortex-M7+)
✅ Edge TPU devices (Google Coral)

Limitations

⚠️ Important Considerations:

Trained on synthetic data only
Requires calibrated sensors (IMU + vitals)
Performance may vary with real-world sensor noise
Needs field validation with actual combat scenarios
Environmental factors (temperature, altitude) may affect accuracy

Training Details

Framework: TensorFlow 2.x
Hardware: Google Colab GPU (T4)
Training Time: ~2-3 hours
Optimizer: Adam (lr=0.001, clipnorm=1.0)
Loss: Categorical Crossentropy (label smoothing=0.1) + MSE
Callbacks: Early stopping, learning rate reduction, model checkpointing

Ethical Considerations

This model is designed for defensive military applications to:

Improve soldier safety and survivability
Enable faster medical response
Reduce combat casualties

Intended Users: Military medical personnel, combat medics, field commanders

Citation

@software{guardian_shield_2025,
  author = {Tadikamalla, Vaibhav},
  title = {Guardian-Shield Combat Impact Detector},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/tadikamallavaibhav/guardian-shield-combat-detector}
}

Contact

Author: Vaibhav Tadikamalla
Project: TIDS - Tactical Impact Detection System

For questions, issues, or collaboration opportunities, please open an issue on GitHub.

License

MIT License - See LICENSE file in GitHub repository

tadikamallavaibhav
/

GuardianShieldCombatDetector