https://github.com/alihassanml/lums-competion-model

lums-competion-model
https://github.com/alihassanml/lums-competion-model

deep-learning machine-learning yolo

Last synced: 1 day ago
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lums-competion-model

• Host: GitHub
• URL: https://github.com/alihassanml/lums-competion-model
• Owner: alihassanml
• License: mit
• Created: 2024-12-08T17:12:39.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-12-22T13:50:49.000Z (10 months ago)
• Last Synced: 2025-09-01T12:04:54.526Z (about 2 months ago)
• Topics: deep-learning, machine-learning, yolo
• Language: Python
• Homepage:
• Size: 151 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
**Documentation for Magic Band Project**

---

**Project Overview**

The Magic Band project is an innovative AI-powered solution developed for the AI Nexus competition at HackXVI, PsiFi 2024. The model simulates a spellcasting scenario by recognizing specific hand movements and audio cues, offering a hands-on demonstration of the fusion of gesture recognition and audio processing in artificial intelligence.

---

**Key Features**

1. **Gesture Recognition:**

   - Tracks and identifies hand movements using advanced computer vision techniques.

   - Implements motion extraction and feature mapping for accurate gesture classification.

2. **Audio Cue Processing:**

   - Processes and interprets audio inputs corresponding to spoken commands.

   - Combines audio preprocessing techniques like MFCC and spectrogram analysis for robust speech recognition.

3. **Multi-Modal Integration:**

   - Seamlessly integrates gesture recognition and audio cues to create a cohesive spellcasting experience.

   - Uses multi-modal data augmentation to enhance training and testing processes.

---

**Technical Architecture**

1. **Model Structure:**

   - Core architecture built using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

   - Includes a custom feature fusion layer to combine gesture and audio inputs.

2. **Training Pipeline:**

   - Utilizes EfficientNet for hand movement detection and classification.

   - Employs pre-trained models and transfer learning for audio processing.

   - Implements loss functions optimized for multi-modal data, ensuring balanced learning.

3. **Optimization Techniques:**

   - Hyperparameter tuning using Bayesian Optimization.

   - Regularization techniques such as dropout and batch normalization to prevent overfitting.

   - Learning rate schedulers and checkpointing for efficient training.

---

**Dataset Preparation**

1. **Wave 1 Dataset:**

   - Contains basic examples of hand movements and corresponding audio cues.

   - Used as the foundation for initial model training.

2. **Wave 2 and Wave 3 Datasets:**

   - Introduce variations in gestures and audio inputs to increase model versatility.

   - Include diverse scenarios to mimic real-world conditions.

3. **Evaluation Dataset:**

   - Released on the final day for live testing and leaderboard tracking.

   - Designed to challenge model adaptability and robustness.

---

**Applications**

- **Interactive Gaming:** Provides an immersive experience for spellcasting games.

- **Assistive Technology:** Enhances accessibility by enabling gesture and voice-based control systems.

- **Education:** Demonstrates AI concepts in a practical, engaging way.

---

**Conclusion**

The Magic Band project exemplifies the power of artificial intelligence in creating engaging and innovative solutions. By combining gesture recognition and audio processing, it offers a glimpse into the future of multi-modal AI applications. This project showcases not only technical expertise but also creativity and problem-solving in a competitive environment.

**Download Links**

https://drive.google.com/drive/folders/12m5t94aTIZYNLO1qzbeNVljvcwUOLRq6?usp=sharing